From 2a34ec2d3a92b0dcdfbf09424ac55e1fcea61523 Mon Sep 17 00:00:00 2001 From: Mikyas Desta Date: Thu, 19 Nov 2020 11:41:11 -0800 Subject: [PATCH 01/10] hand pose --- tasks/hand_pose/dataloader.py | 81 ++++ tasks/hand_pose/hand_pose.json | 1 + tasks/hand_pose/live_hand_pose.ipynb | 222 +++++++++ tasks/hand_pose/live_hand_pose_SVM.ipynb | 408 +++++++++++++++++ .../live_hand_pose_cursor_control.ipynb | 429 ++++++++++++++++++ tasks/hand_pose/preprocessdata.py | 92 ++++ tasks/hand_pose/svmmodel.sav | Bin 0 -> 659217 bytes 7 files changed, 1233 insertions(+) create mode 100644 tasks/hand_pose/dataloader.py create mode 100644 tasks/hand_pose/hand_pose.json create mode 100644 tasks/hand_pose/live_hand_pose.ipynb create mode 100644 tasks/hand_pose/live_hand_pose_SVM.ipynb create mode 100644 tasks/hand_pose/live_hand_pose_cursor_control.ipynb create mode 100644 tasks/hand_pose/preprocessdata.py create mode 100644 tasks/hand_pose/svmmodel.sav diff --git a/tasks/hand_pose/dataloader.py b/tasks/hand_pose/dataloader.py new file mode 100644 index 0000000..5e17f5c --- /dev/null +++ b/tasks/hand_pose/dataloader.py @@ -0,0 +1,81 @@ +import os +import json +import cv2 +import numpy as np + +class dataloader: + + + def __init__(self, path, label_file, test_label): + self.train_path = path+"training/" + self.test_path = path+"testing/" + self.label_path = path+label_file + self.test_label_path = path+test_label + self.train_data = [] + self.train_images =[] + self.train_file_name = [] + self.test_data = [] + self.test_images = [] + self.test_file_name = [] + self.labels_train = [] + self.labels_test = [] + self.load_hand_dataset(self.train_path, self.test_path) + self._assert_exist(self.label_path) + self._assert_exist(self.test_label_path) + self.load_labels(self.label_path, self.test_label_path) + + + + def _assert_exist(self, label_path): + msg = 'File is not availble: %s' % label_path + assert os.path.exists(label_path), msg + def load_labels(self, label_path, test_label): + self._assert_exist(label_path) + self._assert_exist(test_label) + with open(label_path, 'r') as f: + label_data = json.load(f) + self.labels_train = label_data["labels"] + with open(test_label, 'r') as f: + test_label = json.load(f) + self.labels_test = test_label["labels"] + + #return labels_train, labels_test + def scaled_data(self, train_data, test_data): + raw_scaler = preprocessing.StandardScaler().fit(train_data) + scaled_train_data = raw_scaler.transform(train_data) + scaled_test_data = raw_scaler.transform(test_data) + return scaled_train_data, scaled_test_data, raw_scaler + def load_hand_dataset(self, train_path, test_path): + WIDTH = 256 + HEIGHT = 256 + for filename in sorted(os.listdir(train_path)): + self.train_file_name.append(filename) + image = cv2.imread(train_path+filename) + #image = image[:, ::-1, :] + #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + image = cv2.resize(image, (WIDTH, HEIGHT), interpolation = cv2.INTER_AREA) + self.train_data.append(np.reshape(np.array(image), 196608)) + self.train_images.append(image) + + for filename in sorted(os.listdir(test_path)): + self.test_file_name.append(filename) + image = cv2.imread(test_path+filename) + #image = image[:, ::-1, :] + #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + image = cv2.resize(image, (WIDTH, HEIGHT), interpolation = cv2.INTER_AREA) + self.test_images.append(image) + self.test_data.append(np.reshape(np.array(image), 196608)) + #return train_images, test_images, train_data, test_data + def smaller_dataset(self, dataset, no_samples_per_class, no_of_classes): + total_samples_per_class =100 + start = 0 + end = no_samples_per_class + new_dataset = [] + labels = [] + for i in range(no_of_classes): + new_data = dataset[start:end] + start = start+total_samples_per_class + end = start+no_samples_per_class + new_dataset.extend(new_data) + labels.extend([i+1]*no_samples_per_class) + return new_dataset, labels diff --git a/tasks/hand_pose/hand_pose.json b/tasks/hand_pose/hand_pose.json new file mode 100644 index 0000000..4df2740 --- /dev/null +++ b/tasks/hand_pose/hand_pose.json @@ -0,0 +1 @@ +{"supercategory": "hand", "id": 1, "name": "hand", "keypoints": ["palm","thumb_1", "thumb_2", "thumb_3", "thumb_4", "index_finger_1", "index_finger_2", "index_finger_3", "index_finger_4", "middle_finger_1", "middle_finger_2", "middle_finger_3", "middle_finger_4", "ring_finger_1", "ring_finger_2", "ring_finger_3", "ring_finger_4", "baby_finger_1", "baby_finger_2", "baby_finger_3", "baby_finger_4"], "skeleton": [[1, 5], [1, 9], [1, 13], [1, 17], [1, 21], [2, 3], [3, 4], [4, 5], [6, 7], [7, 8], [8, 9], [10, 11], [11, 12], [12, 13], [14, 15], [15, 16], [16, 17], [18, 19], [19, 20], [20, 21]]} diff --git a/tasks/hand_pose/live_hand_pose.ipynb b/tasks/hand_pose/live_hand_pose.ipynb new file mode 100644 index 0000000..e131561 --- /dev/null +++ b/tasks/hand_pose/live_hand_pose.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg \n", + "import trt_pose.coco\n", + "import math\n", + "import os\n", + "import numpy as np\n", + "import traitlets\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('hand_pose_john.json', 'r') as f:\n", + " hand_pose = json.load(f)\n", + "\n", + "topology = trt_pose.coco.coco_category_to_topology(hand_pose)\n", + "import trt_pose.models\n", + "\n", + "num_parts = len(hand_pose['keypoints'])\n", + "num_links = len(hand_pose['skeleton'])\n", + "\n", + "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", + "import torch\n", + "trt_weight = False \n", + "if trt_weight:\n", + " MODEL_WEIGHTS = 'epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + "\n", + "WIDTH = 256\n", + "HEIGHT = 256\n", + "\n", + "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", + "OPTIMIZED_MODEL = 'epoch_4150_trt.pth'\n", + "from torch2trt import TRTModule\n", + "\n", + "model_trt = TRTModule()\n", + "model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from trt_pose.draw_objects import DrawObjects\n", + "from trt_pose.parse_objects import ParseObjects\n", + "\n", + "parse_objects = ParseObjects(topology,cmap_threshold=0.15, link_threshold=0.15)\n", + "draw_objects = DrawObjects(topology)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import torchvision.transforms as transforms\n", + "import PIL.Image\n", + "\n", + "mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()\n", + "std = torch.Tensor([0.229, 0.224, 0.225]).cuda()\n", + "device = torch.device('cuda')\n", + "\n", + "def preprocess(image):\n", + " global device\n", + " device = torch.device('cuda')\n", + " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", + " image = PIL.Image.fromarray(image)\n", + " image = transforms.functional.to_tensor(image).to(device)\n", + " image.sub_(mean[:, None, None]).div_(std[:, None, None])\n", + " return image[None, ...]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's define a function that will preprocess the image, which is originally in BGR8 / HWC format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from jetcam.usb_camera import USBCamera\n", + "from jetcam.csi_camera import CSICamera\n", + "from jetcam.utils import bgr8_to_jpeg\n", + "\n", + "camera = USBCamera(width=WIDTH, height=HEIGHT, capture_fps=30, capture_device=1)\n", + "#camera = CSICamera(width=WIDTH, height=HEIGHT, capture_fps=30)\n", + "\n", + "camera.running = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets\n", + "from IPython.display import display\n", + "\n", + "\n", + "image_w = ipywidgets.Image(format='jpeg', width=256, height=256)\n", + "display(image_w)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def execute(change):\n", + " image = change['new']\n", + " data = preprocess(image)\n", + " cmap, paf = model_trt(data)\n", + " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", + " counts, objects, peaks = parse_objects(cmap, paf)#, cmap_threshold=0.15, link_threshold=0.15)\n", + " draw_objects(image, counts, objects, peaks) \n", + " image_w.value = bgr8_to_jpeg(image[:, ::-1, :])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "execute({'new': camera.value})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "camera.observe(execute, names='value')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#camera.unobserve_all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#camera.running = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tasks/hand_pose/live_hand_pose_SVM.ipynb b/tasks/hand_pose/live_hand_pose_SVM.ipynb new file mode 100644 index 0000000..df8bf8b --- /dev/null +++ b/tasks/hand_pose/live_hand_pose_SVM.ipynb @@ -0,0 +1,408 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-5ru0ruys because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + ] + } + ], + "source": [ + "import json\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg \n", + "import trt_pose.coco\n", + "import math\n", + "import os\n", + "import numpy as np\n", + "import traitlets\n", + "import pickle \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('hand_pose.json', 'r') as f:\n", + " hand_pose = json.load(f)\n", + "\n", + "topology = trt_pose.coco.coco_category_to_topology(hand_pose)\n", + "import trt_pose.models\n", + "\n", + "num_parts = len(hand_pose['keypoints'])\n", + "num_links = len(hand_pose['skeleton'])\n", + "\n", + "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", + "import torch\n", + "trt_weight_available = False \n", + "if trt_weight_available:\n", + " MODEL_WEIGHTS = 'epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + "\n", + "WIDTH = 256\n", + "HEIGHT = 256\n", + "\n", + "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", + "OPTIMIZED_MODEL = 'epoch_4150_trt.pth'\n", + "from torch2trt import TRTModule\n", + "\n", + "model_trt = TRTModule()\n", + "model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from trt_pose.draw_objects import DrawObjects\n", + "from trt_pose.parse_objects import ParseObjects\n", + "\n", + "parse_objects = ParseObjects(topology,cmap_threshold=0.15, link_threshold=0.15)\n", + "draw_objects = DrawObjects(topology)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import torchvision.transforms as transforms\n", + "import PIL.Image\n", + "\n", + "mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()\n", + "std = torch.Tensor([0.229, 0.224, 0.225]).cuda()\n", + "device = torch.device('cuda')\n", + "\n", + "def preprocess(image):\n", + " global device\n", + " device = torch.device('cuda')\n", + " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", + " image = PIL.Image.fromarray(image)\n", + " image = transforms.functional.to_tensor(image).to(device)\n", + " image.sub_(mean[:, None, None]).div_(std[:, None, None])\n", + " return image[None, ...]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.svm import SVC\n", + "clf = make_pipeline(StandardScaler(), SVC(gamma='auto', kernel='rbf'))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from preprocessdata import preprocessdata\n", + "preprocessdata = preprocessdata(topology, num_parts)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from dataloader import dataloader\n", + "path = \"/home/mikyas/mike_dataset/dh/\"\n", + "label_file = \"dh_training_label.json\"\n", + "test_label = \"dh_test_label.json\"\n", + "hand = dataloader(path, label_file, test_label)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def data_preprocess(images):\n", + " dist_bn_joints_all_data = []\n", + " for im in images:\n", + " im = im[:, ::-1, :]\n", + " data_im = preprocess(im)\n", + " cmap, paf = model_trt(data_im)\n", + " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", + " counts, objects, peaks = parse_objects(cmap, paf)\n", + " joints = preprocessdata.joints_inference(im, counts, objects, peaks)\n", + " dist_bn_joints = preprocessdata.find_distance(joints)\n", + " dist_bn_joints_all_data.append(dist_bn_joints)\n", + " return dist_bn_joints_all_data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def smaller_dataset(dataset, no_samples_per_class, no_of_classes):\n", + " total_samples_per_class =100\n", + " start = 0\n", + " end = no_samples_per_class\n", + " new_dataset = []\n", + " labels = []\n", + " for i in range(no_of_classes):\n", + " new_data = dataset[start:end]\n", + " start = start+total_samples_per_class\n", + " end = start+no_samples_per_class\n", + " new_dataset.extend(new_data)\n", + " labels.extend([i+1]*no_samples_per_class)\n", + " return new_dataset, labels" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "train_images, labels_train = hand.smaller_dataset(hand.train_images,90,6)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "joints_train = data_preprocess(train_images)\n", + "joints_test = data_preprocess(hand.test_images)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "svm_train = False\n", + "if svm_train:\n", + " clf, predicted = preprocessdata.trainsvm(clf, joints_train, joints_test, labels_train, hand.labels_test)\n", + " filename = 'svmmodel.sav'\n", + " pickle.dump(clf, open(filename, 'wb'))\n", + "else:\n", + " filename = 'svmmodel.sav'\n", + " clf = pickle.load(open(filename, 'rb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocessdata.svm_accuracy(clf.predict(joints_test), hand.labels_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 5])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.predict([joints_test[40],[0]*num_parts*num_parts])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from jetcam.usb_camera import USBCamera\n", + "from jetcam.csi_camera import CSICamera\n", + "from jetcam.utils import bgr8_to_jpeg\n", + "\n", + "camera = USBCamera(width=WIDTH, height=HEIGHT, capture_fps=30, capture_device=1)\n", + "#camera = CSICamera(width=WIDTH, height=HEIGHT, capture_fps=30)\n", + "\n", + "camera.running = True" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7b0071a1b69d4aae9cac315803ceea35", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Image(value=b'', format='jpeg', height='256', width='256')" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ipywidgets\n", + "from IPython.display import display\n", + "\n", + "\n", + "image_w = ipywidgets.Image(format='jpeg', width=256, height=256)\n", + "display(image_w)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def execute(change):\n", + " image = change['new']\n", + " data = preprocess(image)\n", + " cmap, paf = model_trt(data)\n", + " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", + " counts, objects, peaks = parse_objects(cmap, paf)#, cmap_threshold=0.15, link_threshold=0.15)\n", + " draw_objects(image, counts, objects, peaks)\n", + " joints = preprocessdata.joints_inference(image, counts, objects, peaks)\n", + " dist_bn_joints = preprocessdata.find_distance(joints)\n", + " gesture = clf.predict([dist_bn_joints,[0]*num_parts*num_parts])\n", + " gesture_joints = gesture[0]\n", + " preprocessdata.prev_queue.append(gesture_joints)\n", + " preprocessdata.prev_queue.pop(0)\n", + " preprocessdata.print_label(image, preprocessdata.prev_queue)\n", + " image_w.value = bgr8_to_jpeg(image)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "execute({'new': camera.value})" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "camera.observe(execute, names='value')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#camera.unobserve_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#camera.running = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tasks/hand_pose/live_hand_pose_cursor_control.ipynb b/tasks/hand_pose/live_hand_pose_cursor_control.ipynb new file mode 100644 index 0000000..62c50cd --- /dev/null +++ b/tasks/hand_pose/live_hand_pose_cursor_control.ipynb @@ -0,0 +1,429 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-25fusy21 because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + ] + } + ], + "source": [ + "import json\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg \n", + "import trt_pose.coco\n", + "import math\n", + "import os\n", + "import numpy as np\n", + "import traitlets\n", + "import pickle \n", + "import pyautogui\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('hand_pose.json', 'r') as f:\n", + " hand_pose = json.load(f)\n", + "\n", + "topology = trt_pose.coco.coco_category_to_topology(hand_pose)\n", + "import trt_pose.models\n", + "\n", + "num_parts = len(hand_pose['keypoints'])\n", + "num_links = len(hand_pose['skeleton'])\n", + "\n", + "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", + "import torch\n", + "trt_weight_available = False \n", + "if trt_weight_available:\n", + " MODEL_WEIGHTS = 'epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + "\n", + "WIDTH = 256\n", + "HEIGHT = 256\n", + "\n", + "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", + "OPTIMIZED_MODEL = 'epoch_4150_trt.pth'\n", + "from torch2trt import TRTModule\n", + "\n", + "model_trt = TRTModule()\n", + "model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from trt_pose.draw_objects import DrawObjects\n", + "from trt_pose.parse_objects import ParseObjects\n", + "\n", + "parse_objects = ParseObjects(topology,cmap_threshold=0.15, link_threshold=0.15)\n", + "draw_objects = DrawObjects(topology)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import torchvision.transforms as transforms\n", + "import PIL.Image\n", + "\n", + "mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()\n", + "std = torch.Tensor([0.229, 0.224, 0.225]).cuda()\n", + "device = torch.device('cuda')\n", + "\n", + "def preprocess(image):\n", + " global device\n", + " device = torch.device('cuda')\n", + " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", + " image = PIL.Image.fromarray(image)\n", + " image = transforms.functional.to_tensor(image).to(device)\n", + " image.sub_(mean[:, None, None]).div_(std[:, None, None])\n", + " return image[None, ...]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.svm import SVC\n", + "clf = make_pipeline(StandardScaler(), SVC(gamma='auto', kernel='rbf'))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from preprocessdata import preprocessdata\n", + "preprocessdata = preprocessdata(topology, num_parts)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from dataloader import dataloader\n", + "path = \"/home/mikyas/mike_dataset/dh/\"\n", + "label_file = \"dh_training_label.json\"\n", + "test_label = \"dh_test_label.json\"\n", + "hand = dataloader(path, label_file, test_label)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def data_preprocess(images):\n", + " dist_bn_joints_all_data = []\n", + " for im in images:\n", + " im = im[:, ::-1, :]\n", + " data_im = preprocess(im)\n", + " cmap, paf = model_trt(data_im)\n", + " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", + " counts, objects, peaks = parse_objects(cmap, paf)\n", + " joints = preprocessdata.joints_inference(im, counts, objects, peaks)\n", + " dist_bn_joints = preprocessdata.find_distance(joints)\n", + " dist_bn_joints_all_data.append(dist_bn_joints)\n", + " return dist_bn_joints_all_data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def smaller_dataset(dataset, no_samples_per_class, no_of_classes):\n", + " total_samples_per_class =100\n", + " start = 0\n", + " end = no_samples_per_class\n", + " new_dataset = []\n", + " labels = []\n", + " for i in range(no_of_classes):\n", + " new_data = dataset[start:end]\n", + " start = start+total_samples_per_class\n", + " end = start+no_samples_per_class\n", + " new_dataset.extend(new_data)\n", + " labels.extend([i+1]*no_samples_per_class)\n", + " return new_dataset, labels" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "train_images, labels_train = hand.smaller_dataset(hand.train_images,90,6)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "joints_train = data_preprocess(train_images)\n", + "joints_test = data_preprocess(hand.test_images)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "svm_train = False\n", + "if svm_train:\n", + " clf, predicted = preprocessdata.trainsvm(clf, joints_train, joints_test, labels_train, hand.labels_test)\n", + " filename = 'svmmodel.sav'\n", + " pickle.dump(clf, open(filename, 'wb'))\n", + "else:\n", + " filename = 'svmmodel.sav'\n", + " clf = pickle.load(open(filename, 'rb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocessdata.svm_accuracy(clf.predict(joints_test), hand.labels_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 5])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.predict([joints_test[40],[0]*num_parts*num_parts])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from jetcam.usb_camera import USBCamera\n", + "from jetcam.csi_camera import CSICamera\n", + "from jetcam.utils import bgr8_to_jpeg\n", + "\n", + "camera = USBCamera(width=WIDTH, height=HEIGHT, capture_fps=30, capture_device=1)\n", + "#camera = CSICamera(width=WIDTH, height=HEIGHT, capture_fps=30)\n", + "\n", + "camera.running = True" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f39babbc04b544ce94ee51d2722677d3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Image(value=b'', format='jpeg', height='256', width='256')" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ipywidgets\n", + "from IPython.display import display\n", + "\n", + "\n", + "image_w = ipywidgets.Image(format='jpeg', width=256, height=256)\n", + "display(image_w)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "screenWidth, screenHeight = pyautogui.size()\n", + "p_text = 'none'\n", + "pyautogui.FAILSAFE = False\n", + "def control_cursor(text, joints):\n", + " global p_text\n", + " if p_text!=\"stop\" and text==\"stop\":\n", + " pyautogui.click()\n", + " if text == \"pan one\":\n", + " pyautogui.moveTo(((joints[8][0])*1000)/256, ((joints[8][1])*700)/256)\n", + " #pyautogui.moveTo((joints[8][0]), (joints[8][1]))\n", + " p_text = text" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def execute(change):\n", + " image = change['new']\n", + " data = preprocess(image)\n", + " cmap, paf = model_trt(data)\n", + " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", + " counts, objects, peaks = parse_objects(cmap, paf)#, cmap_threshold=0.15, link_threshold=0.15)\n", + " draw_objects(image, counts, objects, peaks)\n", + " joints = preprocessdata.joints_inference(image, counts, objects, peaks)\n", + " dist_bn_joints = preprocessdata.find_distance(joints)\n", + " gesture = clf.predict([dist_bn_joints,[0]*num_parts*num_parts])\n", + " gesture_joints = gesture[0]\n", + " preprocessdata.prev_queue.append(gesture_joints)\n", + " preprocessdata.prev_queue.pop(0)\n", + " preprocessdata.print_label(image, preprocessdata.prev_queue)\n", + " control_cursor(preprocessdata.text, joints)\n", + " image_w.value = bgr8_to_jpeg(image)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "execute({'new': camera.value})" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "camera.observe(execute, names='value')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#camera.unobserve_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "#camera.running = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tasks/hand_pose/preprocessdata.py b/tasks/hand_pose/preprocessdata.py new file mode 100644 index 0000000..06972a2 --- /dev/null +++ b/tasks/hand_pose/preprocessdata.py @@ -0,0 +1,92 @@ +import math +import pickle +import cv2 +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler + +class preprocessdata: + + def __init__(self, topology, num_parts): + self.joints = [] + self.dist_bn_joints = [] + self.topology = topology + self.num_parts = num_parts + self.text = "no hand" + self.num_frames = 7 + self.prev_queue = [7]*self.num_frames + + def svm_accuracy(self, test_predicted, labels_test): + predicted = [] + for i in range(len(labels_test)): + if labels_test[i]==test_predicted[i]: + predicted.append(0) + else: + predicted.append(1) + accuracy = 1 - sum(predicted)/len(labels_test) + return accuracy + def trainsvm(self, clf, train_data, test_data, labels_train, labels_test): + clf.fit(train_data,labels_train) + predicted_test = clf.predict(test_data) + return clf, predicted_test + #def loadsvmweights(): + + def joints_inference(self, image, counts, objects, peaks): + joints_t = [] + height = image.shape[0] + width = image.shape[1] + K = self.topology.shape[0] + count = int(counts[0]) + for i in range(count): + obj = objects[0][i] + C = obj.shape[0] + for j in range(C): + k = int(obj[j]) + picked_peaks = peaks[0][j][k] + joints_t.append([round(float(picked_peaks[1]) * width), round(float(picked_peaks[0]) * height)]) + joints_pt = joints_t[:self.num_parts] + rest_of_joints_t = joints_t[self.num_parts:] + #when it does not predict a particular joint in the same association it will try to find it in a different association + for i in range(len(rest_of_joints_t)): + l = i%self.num_parts + if joints_pt[l] == [0,0]: + joints_pt[l] = rest_of_joints_t[i] + #if nothing is predicted + if count == 0: + joints_pt = [[0,0]]*self.num_parts + return joints_pt + def find_distance(self, joints): + joints_features = [] + for i in joints: + for j in joints: + dist_between_i_j = math.sqrt((i[0]-j[0])**2+(i[1]-j[1])**2) + joints_features.append(dist_between_i_j) + return joints_features + def print_label(self, image, gesture_joints): + font = cv2.FONT_HERSHEY_SIMPLEX + color = (255, 0, 0) + org = (50, 50) + thickness = 2 + fontScale = 0.5 + if self.prev_queue == [1]*7: + self.text = 'OK' + elif self.prev_queue == [2]*7: + self.text = 'fist' + elif self.prev_queue == [3]*7: + self.text = 'pan two' + elif self.prev_queue == [4]*7: + self.text = 'pan one' + elif self.prev_queue == [5]*7: + self.text = 'tumbs up' + elif self.prev_queue == [6]*7: + self.text = 'stop' + elif self.prev_queue == [7]*7: + self.text = 'no hand' + image = cv2.putText(image, self.text, org, font, + fontScale, color, thickness, cv2.LINE_AA) + return image + + + + + + \ No newline at end of file diff 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The dataset created can be used for pose classification. \n", + "In order to create a new dataset for gesture recoginition specify the following parameters \n", + "\n", + "**no_of_classes** - Number of classes to be created. i.e. For hand pose the number of hand gestures to be created.\n", + "\n", + "**path_dir** - Path to the directory to be created\n", + "\n", + "**dataset_name** - The name of the dataset to be created\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def create_directories_for_classes(no_of_classes, path_dir, dataset_name):\n", + " dir_ = os.path.join(path_dir, dataset_name)\n", + " for i in range(no_of_classes):\n", + " dir_to_create = os.path.join(dir_,\"%s\" % (i+1))\n", + " try:\n", + " os.makedirs(dir_to_create)\n", + " except FileExistsError:\n", + " print(os.path.join(\"The following directory was not created because it already exsists\", dir_ , ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "dir_datasets = '/home/mikyas/mike_dataset/bbbb/'\n", + "dataset_name = \"hand_dataset\"\n", + "no_of_classes = 3\n", + "create_directories_for_classes(no_of_classes, dir_datasets, dataset_name )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import ipywidgets.widgets as widgets\n", + "dir_ = os.path.join(dir_datasets, dataset_name)\n", + "curr_class_no = 1\n", + "button_layout = widgets.Layout(width='128px', height='32px')\n", + "curr_dir = os.path.join(dir_,'%s'%curr_class_no )\n", + "collecting_button = widgets.Button(description= 'Collect Class ' + str(curr_class_no), button_style='success', layout=button_layout)\n", + "prev_button = widgets.Button(description='Previous Class', button_style='primary', layout=button_layout)\n", + "nxt_button = widgets.Button(description='Next Class', button_style='info', layout=button_layout)\n", + "\n", + "dir_count = widgets.IntText(layout=button_layout, value=len(os.listdir(curr_dir)))\n", + "dir_count.continuous_update" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from uuid import uuid1\n", + "def save_snapshot(directory):\n", + " image_path = os.path.join(directory, str(uuid1()) + '.jpg')\n", + " with open(image_path, 'wb') as f:\n", + " f.write(image_w.value)\n", + "def save_dir():\n", + " global curr_dir, dir_count\n", + " save_snapshot(curr_dir)\n", + " dir_count.value = len(os.listdir(curr_dir))\n", + "def prev_dir():\n", + " global curr_class_no, curr_dir, no_of_classes\n", + " if curr_class_no>1:\n", + " curr_class_no-=1\n", + " curr_dir = os.path.join(dir_,'%s'%curr_class_no )\n", + " collecting_button.description = 'Collect Class ' + str(curr_class_no)\n", + " dir_count.value = len(os.listdir(curr_dir))\n", + " dir_count.continuous_update\n", + "def nxt_dir():\n", + " global curr_class_no, curr_dir, no_of_classes\n", + " if curr_class_no" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('hand_pose.json', 'r') as f:\n", + " hand_pose = json.load(f)\n", + "\n", + "topology = trt_pose.coco.coco_category_to_topology(hand_pose)\n", + "import trt_pose.models\n", + "\n", + "num_parts = len(hand_pose['keypoints'])\n", + "num_links = len(hand_pose['skeleton'])\n", + "\n", + "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", + "import torch\n", + "trt_weight_available = False \n", + "if trt_weight_available:\n", + " MODEL_WEIGHTS = 'epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + "\n", + "WIDTH = 256\n", + "HEIGHT = 256\n", + "\n", + "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", + "OPTIMIZED_MODEL = 'epoch_4150_trt.pth'\n", + "from torch2trt import TRTModule\n", + "\n", + "model_trt = TRTModule()\n", + "model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from trt_pose.draw_objects import DrawObjects\n", + "from trt_pose.parse_objects import ParseObjects\n", + "\n", + "parse_objects = ParseObjects(topology,cmap_threshold=0.35, link_threshold=0.15)\n", + "draw_objects = DrawObjects(topology)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "This notebook creates a dataset (images and labels as a json file). The dataset created can be used for pose classification. \n", + "In order to create a new dataset for gesture recoginition specify the following parameters \n", + "\n", + "**no_of_classes** - Number of classes to be created. i.e. For hand pose the number of hand gestures to be created.\n", + "\n", + "**path_dir** - Path to the directory to be created\n", + "\n", + "**dataset_name** - The name of the dataset to be created\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_directories_for_classes(no_of_classes, path_dir, dataset_name):\n", + " dir_ = os.path.join(path_dir, dataset_name)\n", + " for i in range(no_of_classes):\n", + " dir_to_create = os.path.join(dir_,\"%s\" % (i+1))\n", + " try:\n", + " os.makedirs(dir_to_create)\n", + " except FileExistsError:\n", + " print(os.path.join(\"The following directory was not created because it already exsists\", dir_ , ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dir_datasets = '/home/mikyas/mike_dataset/ccc/'\n", + "dataset_name = \"hand_dataset\"\n", + "no_of_classes = 3\n", + "create_directories_for_classes(no_of_classes, dir_datasets, dataset_name )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets.widgets as widgets\n", + "dir_ = os.path.join(dir_datasets, dataset_name)\n", + "curr_class_no = 1\n", + "button_layout = widgets.Layout(width='128px', height='32px')\n", + "curr_dir = os.path.join(dir_,'%s'%curr_class_no )\n", + "collecting_button = widgets.Button(description= 'Collect Class ' + str(curr_class_no), button_style='success', layout=button_layout)\n", + "prev_button = widgets.Button(description='Previous Class', button_style='primary', layout=button_layout)\n", + "nxt_button = widgets.Button(description='Next Class', button_style='info', layout=button_layout)\n", + "\n", + "dir_count = widgets.IntText(layout=button_layout, value=len(os.listdir(curr_dir)))\n", + "dir_count.continuous_update" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from uuid import uuid1\n", + "def save_snapshot(directory):\n", + " image_path = os.path.join(directory, str(uuid1()) + '.jpg')\n", + " with open(image_path, 'wb') as f:\n", + " f.write(image_w.value)\n", + "def save_dir():\n", + " global curr_dir, dir_count\n", + " save_snapshot(curr_dir)\n", + " dir_count.value = len(os.listdir(curr_dir))\n", + "def prev_dir():\n", + " global curr_class_no, curr_dir, no_of_classes\n", + " if curr_class_no>1:\n", + " curr_class_no-=1\n", + " curr_dir = os.path.join(dir_,'%s'%curr_class_no )\n", + " collecting_button.description = 'Collect Class ' + str(curr_class_no)\n", + " dir_count.value = len(os.listdir(curr_dir))\n", + " dir_count.continuous_update\n", + "def nxt_dir():\n", + " global curr_class_no, curr_dir, no_of_classes\n", + " if curr_class_no" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "with open('hand_pose_john.json', 'r') as f:\n", + "with open('hand_pose.json', 'r') as f:\n", " hand_pose = json.load(f)\n", "\n", "topology = trt_pose.coco.coco_category_to_topology(hand_pose)\n", @@ -52,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -112,9 +131,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b6a3287eaa4647798acc17a87cd942ed", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Image(value=b'', format='jpeg', height='256', width='256')" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import ipywidgets\n", "from IPython.display import display\n", @@ -126,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -142,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -151,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ diff --git a/tasks/pose_classifiaction_utils/gesture_data_collection.ipynb b/tasks/pose_classifiaction_utils/gesture_data_collection.ipynb new file mode 100644 index 0000000..06d09ed --- /dev/null +++ b/tasks/pose_classifiaction_utils/gesture_data_collection.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook creates a dataset (images and labels as a json file). The dataset created can be used for pose classification. \n", + "In order to create a new dataset for gesture recoginition specify the following parameters \n", + "\n", + "**no_of_classes** - Number of classes to be created. i.e. For hand pose the number of hand gestures to be created.\n", + "\n", + "**path_dir** - Path to the directory to be created\n", + "\n", + "**dataset_name** - The name of the dataset to be created\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_directories_for_classes(no_of_classes, path_dir, dataset_name):\n", + " dir_ = os.path.join(path_dir, dataset_name)\n", + " for i in range(no_of_classes):\n", + " dir_to_create = os.path.join(dir_,\"%s\" % (i+1))\n", + " try:\n", + " os.makedirs(dir_to_create)\n", + " except FileExistsError:\n", + " print(os.path.join(\"The following directory was not created because it already exsists\", dir_ , ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dir_datasets = '/home/mikyas/mike_dataset/bbbb/'\n", + "dataset_name = \"hand_dataset\"\n", + "no_of_classes = 3\n", + "create_directories_for_classes(no_of_classes, dir_datasets, dataset_name )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets.widgets as widgets\n", + "dir_ = os.path.join(dir_datasets, dataset_name)\n", + "curr_class_no = 1\n", + "button_layout = widgets.Layout(width='128px', height='32px')\n", + "curr_dir = os.path.join(dir_,'%s'%curr_class_no )\n", + "collecting_button = widgets.Button(description= 'Collect Class ' + str(curr_class_no), button_style='success', layout=button_layout)\n", + "prev_button = widgets.Button(description='Previous Class', button_style='primary', layout=button_layout)\n", + "nxt_button = widgets.Button(description='Next Class', button_style='info', layout=button_layout)\n", + "\n", + "dir_count = widgets.IntText(layout=button_layout, value=len(os.listdir(curr_dir)))\n", + "dir_count.continuous_update" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from uuid import uuid1\n", + "def save_snapshot(directory):\n", + " image_path = os.path.join(directory, str(uuid1()) + '.jpg')\n", + " with open(image_path, 'wb') as f:\n", + " f.write(image_w.value)\n", + "def save_dir():\n", + " global curr_dir, dir_count\n", + " save_snapshot(curr_dir)\n", + " dir_count.value = len(os.listdir(curr_dir))\n", + "def prev_dir():\n", + " global curr_class_no, curr_dir, no_of_classes\n", + " if curr_class_no>1:\n", + " curr_class_no-=1\n", + " curr_dir = os.path.join(dir_,'%s'%curr_class_no )\n", + " collecting_button.description = 'Collect Class ' + str(curr_class_no)\n", + " dir_count.value = len(os.listdir(curr_dir))\n", + " dir_count.continuous_update\n", + "def nxt_dir():\n", + " global curr_class_no, curr_dir, no_of_classes\n", + " if curr_class_no Date: Mon, 30 Nov 2020 01:28:51 -0800 Subject: [PATCH 03/10] Create README.md --- tasks/hand_pose/README.md | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 tasks/hand_pose/README.md diff --git a/tasks/hand_pose/README.md b/tasks/hand_pose/README.md new file mode 100644 index 0000000..8cf2892 --- /dev/null +++ b/tasks/hand_pose/README.md @@ -0,0 +1,38 @@ +# Hand Pose Detection And Classification + +This project is an extention of TRT Pose for Hand Pose Detection. The project includes + +- Pretrained models for hand pose estimation capable of running in real time on Jetson Xavier NX. + +- Scripts for applications of Hand Pose Estimation + + - Hand gesture recoginition (hand pose classification) + + - Cursor control + + - Mini-Paint type of application + +- Pretrained model for gesture recoginition + +## Getting Started + +### Step 1 - Install trt_pose and it's dependencies + +Make sure to follow all the instructions from trt_pose and install all it's depenedencies. +Follow the following instruction from https://github.com/NVIDIA-AI-IOT/trt_pose. + +### Step 2 - Install dependecies for hand pose + +### Step 3 - Run hand pose and it's applications + + A) Hand Pose demo + + B) Hand gesture recoginition (hand pose classification) + + C) Cursor control application + + D) Mini-Paint + +The model was trained using the training script in trt_pose and the hand pose data collected in Nvidia. + +Model details: resnet18 From bda642d6e81bdc9c38ce6486e06003f7e45abdcd Mon Sep 17 00:00:00 2001 From: MikyasDesta Date: Mon, 30 Nov 2020 01:36:13 -0800 Subject: [PATCH 04/10] Update README.md --- tasks/hand_pose/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tasks/hand_pose/README.md b/tasks/hand_pose/README.md index 8cf2892..e38ef1b 100644 --- a/tasks/hand_pose/README.md +++ b/tasks/hand_pose/README.md @@ -1,4 +1,4 @@ -# Hand Pose Detection And Classification +# Hand Pose Estimation And Classification This project is an extention of TRT Pose for Hand Pose Detection. The project includes From b04913f7b8f8af1e715493595346bcd0ea124d1d Mon Sep 17 00:00:00 2001 From: MikyasDesta Date: Wed, 2 Dec 2020 00:56:53 -0800 Subject: [PATCH 05/10] Update README.md --- tasks/hand_pose/README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/tasks/hand_pose/README.md b/tasks/hand_pose/README.md index e38ef1b..9dca0f0 100644 --- a/tasks/hand_pose/README.md +++ b/tasks/hand_pose/README.md @@ -28,6 +28,12 @@ Follow the following instruction from https://github.com/NVIDIA-AI-IOT/trt_pose. A) Hand Pose demo B) Hand gesture recoginition (hand pose classification) + + To make your own hand gesture classification from the hand pose estimation, follow the following steps + + - Create your own dataset using the gesture_data_collection.ipynb or gesture_data_collection_with_pose.ipynb. This will allow you to create the type of gestures you want to classify. (eg. tumbs up, fist,etc). This notebook will automatically create a dataset with images and labels that is ready to be trained for gesture classification. + - Train using the train_gesture_classification.ipynb notebook file. It uses an SVM from scikit-learn. you can experiment your other types of models to train. You can also infer in this training script. + C) Cursor control application From 5a28588fe67f60231dc05e628cd061238850a6cb Mon Sep 17 00:00:00 2001 From: MikyasDesta Date: Wed, 2 Dec 2020 02:19:47 -0800 Subject: [PATCH 06/10] Update README.md --- tasks/hand_pose/README.md | 29 ++++++++++++++++++++++++++--- 1 file changed, 26 insertions(+), 3 deletions(-) diff --git a/tasks/hand_pose/README.md b/tasks/hand_pose/README.md index 9dca0f0..313ae21 100644 --- a/tasks/hand_pose/README.md +++ b/tasks/hand_pose/README.md @@ -22,20 +22,43 @@ Make sure to follow all the instructions from trt_pose and install all it's depe Follow the following instruction from https://github.com/NVIDIA-AI-IOT/trt_pose. ### Step 2 - Install dependecies for hand pose + + pip install traitlets + ### Step 3 - Run hand pose and it's applications A) Hand Pose demo + + - Open and follow live_hand_pose.ipynb notebook. B) Hand gesture recoginition (hand pose classification) - + - Install dependecies + - scikit-learn + - pip install -U scikit-learn + - or install it from the source + The current gesture classification model supports six classes (fist, pan, stop, fine, peace, no hand). + More gestures can be added by a simple process of creating your own dataset and training it on an svm model. + An SVM model weight is provided for inference. + To make your own hand gesture classification from the hand pose estimation, follow the following steps - - Create your own dataset using the gesture_data_collection.ipynb or gesture_data_collection_with_pose.ipynb. This will allow you to create the type of gestures you want to classify. (eg. tumbs up, fist,etc). This notebook will automatically create a dataset with images and labels that is ready to be trained for gesture classification. - - Train using the train_gesture_classification.ipynb notebook file. It uses an SVM from scikit-learn. you can experiment your other types of models to train. You can also infer in this training script. + - Create your own dataset using the gesture_data_collection.ipynb or gesture_data_collection_with_pose.ipynb. + This will allow you to create the type of gestures you want to classify. (eg. tumbs up, fist,etc). + This notebook will automatically create a dataset with images and labels that is ready to be trained for gesture classification. + + - Train using the train_gesture_classification.ipynb notebook file. It uses an SVM from scikit-learn. + Other types of models can also be experimented. C) Cursor control application + - Install dependecies + - pyautogui + - python3 -m pip install pyautogui + - On jetson install it from the source + - Open and follow the cursor_control_live_demo.ipynb notebook. + - This will allow you to control your mouse cursor on your desktop. It uses the hand gesture classification. + When your hand geture is pan, you can control the cursor. when it is stop, it's left click. 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z_58swKMiBSh?Z9H{6T4)r)~(rfB0>i%abD$*JrcLJ0LOq9=K!}qwJni20}dYwME6^ z!P{l2U&SUR5OFjQiddfi2^|NrZksRziL4cMgdeK()C!Sx#yhQUQnqR3p!Y7umgv#4A z>FB=GzvYvD%nR1ci(_{nP0X#0EDen;tZB?k=nf=n^bX`@U8ck69Y~QY%)jvakK50N z?B8=f=5__lYH|LZsoeq73x%O7{w$6ddkI^$paW_>-`9Webv0155H|7`Xn{k*^|M^t zYT%3@Jy<%s4L%iHbCbTc9Tdb$HaJeM1yj3C!bW0aVf2PuoaXmEp!}oHg`qq3P;~b; z^DFILK=(a;hYP#n8=+)6eujw+cj~0sGhjkt;6l1zr zGjIHZpuu!S1h-MbO7kANeZSX=VPIiI z8?`X+=qwV{c`o3@^!(eK_K&{vLK(bAGx+E-eR+N7_y5~>{?^QZv9{6vaj!X$VaHg{ eP`05vF@uc$?pZSQv4su-?j<> From 931ab90d3e89d513687afb543c4508a76cce9778 Mon Sep 17 00:00:00 2001 From: Mikyas Desta Date: Wed, 2 Dec 2020 02:33:14 -0800 Subject: [PATCH 08/10] new structure --- ...l.ipynb => cursor_control_live_demo.ipynb} | 155 ++------- tasks/hand_pose/draw_hand_pose.ipynb | 278 ++++++++++++++++ .../gesture_classification_live_demo.ipynb | 301 ++++++++++++++++++ tasks/hand_pose/gesture_data_collection.ipynb | 85 +++-- .../gesture_data_collection_with_pose.ipynb | 89 +++--- tasks/hand_pose/hand_pose_m.json | 1 + tasks/hand_pose/live_hand_pose.ipynb | 18 +- tasks/hand_pose/preprocessdata.py | 14 +- ...ynb => train_gesture_classification.ipynb} | 105 +++--- trt_pose/draw_objects.py | 2 +- 10 files changed, 781 insertions(+), 267 deletions(-) rename tasks/hand_pose/{live_hand_pose_cursor_control.ipynb => cursor_control_live_demo.ipynb} (70%) create mode 100644 tasks/hand_pose/draw_hand_pose.ipynb create mode 100644 tasks/hand_pose/gesture_classification_live_demo.ipynb create mode 100644 tasks/hand_pose/hand_pose_m.json rename tasks/hand_pose/{live_hand_pose_SVM.ipynb => train_gesture_classification.ipynb} (86%) diff --git a/tasks/hand_pose/live_hand_pose_cursor_control.ipynb b/tasks/hand_pose/cursor_control_live_demo.ipynb similarity index 70% rename from tasks/hand_pose/live_hand_pose_cursor_control.ipynb rename to tasks/hand_pose/cursor_control_live_demo.ipynb index 62c50cd..bcd3d41 100644 --- a/tasks/hand_pose/live_hand_pose_cursor_control.ipynb +++ b/tasks/hand_pose/cursor_control_live_demo.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-25fusy21 because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-kjp96j9b because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" ] } ], @@ -55,16 +55,22 @@ "\n", "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", "import torch\n", - "trt_weight_available = False \n", - "if trt_weight_available:\n", - " MODEL_WEIGHTS = 'epoch_4150.pth'\n", - " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + "\n", "\n", "WIDTH = 256\n", "HEIGHT = 256\n", - "\n", "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", - "OPTIMIZED_MODEL = 'epoch_4150_trt.pth'\n", + "\n", + "if not os.path.exists('resnet18_244x224_epoch_4150_trt.pth'):\n", + " MODEL_WEIGHTS = 'resnet18_244x224_epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + " import torch2trt\n", + " model_trt = torch2trt.torch2trt(model, [data], fp16_mode=True, max_workspace_size=1<<25)\n", + " OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + " torch.save(model_trt.state_dict(), OPTIMIZED_MODEL)\n", + "\n", + "\n", + "OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", "from torch2trt import TRTModule\n", "\n", "model_trt = TRTModule()\n", @@ -130,84 +136,11 @@ "preprocessdata = preprocessdata(topology, num_parts)" ] }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from dataloader import dataloader\n", - "path = \"/home/mikyas/mike_dataset/dh/\"\n", - "label_file = \"dh_training_label.json\"\n", - "test_label = \"dh_test_label.json\"\n", - "hand = dataloader(path, label_file, test_label)" - ] - }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], - "source": [ - "def data_preprocess(images):\n", - " dist_bn_joints_all_data = []\n", - " for im in images:\n", - " im = im[:, ::-1, :]\n", - " data_im = preprocess(im)\n", - " cmap, paf = model_trt(data_im)\n", - " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", - " counts, objects, peaks = parse_objects(cmap, paf)\n", - " joints = preprocessdata.joints_inference(im, counts, objects, peaks)\n", - " dist_bn_joints = preprocessdata.find_distance(joints)\n", - " dist_bn_joints_all_data.append(dist_bn_joints)\n", - " return dist_bn_joints_all_data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def smaller_dataset(dataset, no_samples_per_class, no_of_classes):\n", - " total_samples_per_class =100\n", - " start = 0\n", - " end = no_samples_per_class\n", - " new_dataset = []\n", - " labels = []\n", - " for i in range(no_of_classes):\n", - " new_data = dataset[start:end]\n", - " start = start+total_samples_per_class\n", - " end = start+no_samples_per_class\n", - " new_dataset.extend(new_data)\n", - " labels.extend([i+1]*no_samples_per_class)\n", - " return new_dataset, labels" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "train_images, labels_train = hand.smaller_dataset(hand.train_images,90,6)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "joints_train = data_preprocess(train_images)\n", - "joints_test = data_preprocess(hand.test_images)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], "source": [ "svm_train = False\n", "if svm_train:\n", @@ -215,53 +148,13 @@ " filename = 'svmmodel.sav'\n", " pickle.dump(clf, open(filename, 'wb'))\n", "else:\n", - " filename = 'svmmodel.sav'\n", + " filename = 'svmmodel_new.sav'\n", " clf = pickle.load(open(filename, 'rb'))" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessdata.svm_accuracy(clf.predict(joints_test), hand.labels_test)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3, 5])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf.predict([joints_test[40],[0]*num_parts*num_parts])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -277,13 +170,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f39babbc04b544ce94ee51d2722677d3", + "model_id": "f735c3a4f55842d3bde40004c969478b", "version_major": 2, "version_minor": 0 }, @@ -306,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -317,7 +210,7 @@ " global p_text\n", " if p_text!=\"stop\" and text==\"stop\":\n", " pyautogui.click()\n", - " if text == \"pan one\":\n", + " if text == \"pan\":\n", " pyautogui.moveTo(((joints[8][0])*1000)/256, ((joints[8][1])*700)/256)\n", " #pyautogui.moveTo((joints[8][0]), (joints[8][1]))\n", " p_text = text" @@ -325,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -349,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -358,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -376,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/tasks/hand_pose/draw_hand_pose.ipynb b/tasks/hand_pose/draw_hand_pose.ipynb new file mode 100644 index 0000000..03b0d35 --- /dev/null +++ b/tasks/hand_pose/draw_hand_pose.ipynb @@ -0,0 +1,278 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg \n", + "import trt_pose.coco\n", + "import math\n", + "import os\n", + "import numpy as np\n", + "import traitlets\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('hand_pose.json', 'r') as f:\n", + " hand_pose = json.load(f)\n", + "\n", + "topology = trt_pose.coco.coco_category_to_topology(hand_pose)\n", + "import trt_pose.models\n", + "\n", + "num_parts = len(hand_pose['keypoints'])\n", + "num_links = len(hand_pose['skeleton'])\n", + "\n", + "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", + "import torch\n", + "\n", + "\n", + "WIDTH = 256\n", + "HEIGHT = 256\n", + "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", + "\n", + "if not os.path.exists('resnet18_244x224_epoch_4150_trt.pth'):\n", + " MODEL_WEIGHTS = 'resnet18_244x224_epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + " import torch2trt\n", + " model_trt = torch2trt.torch2trt(model, [data], fp16_mode=True, max_workspace_size=1<<25)\n", + " OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + " torch.save(model_trt.state_dict(), OPTIMIZED_MODEL)\n", + "\n", + "\n", + "OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + "from torch2trt import TRTModule\n", + "\n", + "model_trt = TRTModule()\n", + "model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from trt_pose.draw_objects import DrawObjects\n", + "from trt_pose.parse_objects import ParseObjects\n", + "\n", + "parse_objects = ParseObjects(topology,cmap_threshold=0.15, link_threshold=0.15)\n", + "draw_objects = DrawObjects(topology)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import torchvision.transforms as transforms\n", + "import PIL.Image\n", + "\n", + "mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()\n", + "std = torch.Tensor([0.229, 0.224, 0.225]).cuda()\n", + "device = torch.device('cuda')\n", + "\n", + "def preprocess(image):\n", + " global device\n", + " device = torch.device('cuda')\n", + " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", + " image = PIL.Image.fromarray(image)\n", + " image = transforms.functional.to_tensor(image).to(device)\n", + " image.sub_(mean[:, None, None]).div_(std[:, None, None])\n", + " return image[None, ...]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's define a function that will preprocess the image, which is originally in BGR8 / HWC format." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from jetcam.usb_camera import USBCamera\n", + "from jetcam.csi_camera import CSICamera\n", + "from jetcam.utils import bgr8_to_jpeg\n", + "\n", + "camera = USBCamera(width=WIDTH, height=HEIGHT, capture_fps=30, capture_device=1)\n", + "#camera = CSICamera(width=WIDTH, height=HEIGHT, capture_fps=30)\n", + "\n", + "camera.running = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from preprocessdata import preprocessdata\n", + "preprocessdata = preprocessdata(topology, num_parts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import ipywidgets\n", + "from IPython.display import display\n", + "\n", + "\n", + "image_w = ipywidgets.Image(format='jpeg', width=256, height=256)\n", + "display(image_w)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pen = []\n", + "def draw(image, joints):\n", + " cv2.circle(image, (joints[17][0], joints[17][1]), 1,(255,0,255), 2)\n", + " cv2.circle(image, (joints[9][0], joints[9][1]), 1,(0,255,0), 2)\n", + " cv2.circle(image, (joints[5][0], joints[5][1]), 1,(255,255,255), 2)\n", + " cv2.circle(image, (joints[1][0], joints[1][1]), 1,(0,0,0), 2)\n", + " dist_between_j17_j1 = math.sqrt((joints[17][0]-joints[1][0])**2+(joints[17][1]-joints[1][1])**2)\n", + " dist_between_j9_j1 = math.sqrt((joints[9][0]-joints[1][0])**2+(joints[9][1]-joints[1][1])**2)\n", + " global pen\n", + " if dist_between_j9_j1<30:\n", + " pen.append((joints[5][0], joints[5][1]))\n", + " for i in range(len(pen)):\n", + " if i > 0:\n", + " cv2.line(image,pen[i-1], pen[i], (0,0,0), 2)\n", + " #cv2.circle(image, pen[i], 1,(0,0,0), 2)\n", + " if dist_between_j17_j1<5:\n", + " pen.clear()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def draw_pose(image, joints):\n", + " for i in range (len(joints)):\n", + " cv2.circle(image, (joints[i][0], joints[i][1]), 1,(0,0,255), 2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def execute(change):\n", + " image = change['new']\n", + " data = preprocess(image)\n", + " cmap, paf = model_trt(data)\n", + " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", + " counts, objects, peaks = parse_objects(cmap, paf)#, cmap_threshold=0.15, link_threshold=0.15)\n", + " draw_objects(image, counts, objects, peaks) \n", + " joints = preprocessdata.joints_inference(image, counts, objects, peaks)\n", + " dist_bn_joints = preprocessdata.find_distance(joints)\n", + " #draw(image, joints)\n", + " #draw_pose(image, joints)\n", + " image_w.value = bgr8_to_jpeg(image[:, ::-1, :])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "execute({'new': camera.value})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "camera.observe(execute, names='value')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "camera.unobserve_all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#camera.running = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tasks/hand_pose/gesture_classification_live_demo.ipynb b/tasks/hand_pose/gesture_classification_live_demo.ipynb new file mode 100644 index 0000000..678adc3 --- /dev/null +++ b/tasks/hand_pose/gesture_classification_live_demo.ipynb @@ -0,0 +1,301 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-ef8bt1at because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + ] + } + ], + "source": [ + "import json\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg \n", + "import trt_pose.coco\n", + "import math\n", + "import os\n", + "import numpy as np\n", + "import traitlets\n", + "import pickle \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('hand_pose.json', 'r') as f:\n", + " hand_pose = json.load(f)\n", + "\n", + "topology = trt_pose.coco.coco_category_to_topology(hand_pose)\n", + "import trt_pose.models\n", + "\n", + "num_parts = len(hand_pose['keypoints'])\n", + "num_links = len(hand_pose['skeleton'])\n", + "\n", + "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", + "import torch\n", + "\n", + "\n", + "WIDTH = 256\n", + "HEIGHT = 256\n", + "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", + "\n", + "if not os.path.exists('resnet18_244x224_epoch_4150_trt.pth'):\n", + " MODEL_WEIGHTS = 'resnet18_244x224_epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + " import torch2trt\n", + " model_trt = torch2trt.torch2trt(model, [data], fp16_mode=True, max_workspace_size=1<<25)\n", + " OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + " torch.save(model_trt.state_dict(), OPTIMIZED_MODEL)\n", + "\n", + "\n", + "OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + "from torch2trt import TRTModule\n", + "\n", + "model_trt = TRTModule()\n", + "model_trt.load_state_dict(torch.load(OPTIMIZED_MODEL))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from trt_pose.draw_objects import DrawObjects\n", + "from trt_pose.parse_objects import ParseObjects\n", + "\n", + "parse_objects = ParseObjects(topology,cmap_threshold=0.12, link_threshold=0.15)\n", + "draw_objects = DrawObjects(topology)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import torchvision.transforms as transforms\n", + "import PIL.Image\n", + "\n", + "mean = torch.Tensor([0.485, 0.456, 0.406]).cuda()\n", + "std = torch.Tensor([0.229, 0.224, 0.225]).cuda()\n", + "device = torch.device('cuda')\n", + "\n", + "def preprocess(image):\n", + " global device\n", + " device = torch.device('cuda')\n", + " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", + " image = PIL.Image.fromarray(image)\n", + " image = transforms.functional.to_tensor(image).to(device)\n", + " image.sub_(mean[:, None, None]).div_(std[:, None, None])\n", + " return image[None, ...]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.svm import SVC\n", + "clf = make_pipeline(StandardScaler(), SVC(gamma='auto', kernel='rbf'))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from preprocessdata import preprocessdata\n", + "preprocessdata = preprocessdata(topology, num_parts)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "svm_train = False\n", + "if svm_train:\n", + " clf, predicted = preprocessdata.trainsvm(clf, joints_train, joints_test, hand.labels_train, hand.labels_test)\n", + " filename = 'svmmodel_new.sav'\n", + " pickle.dump(clf, open(filename, 'wb'))\n", + "else:\n", + " filename = 'svmmodel_new.sav'\n", + " clf = pickle.load(open(filename, 'rb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from jetcam.usb_camera import USBCamera\n", + "from jetcam.csi_camera import CSICamera\n", + "from jetcam.utils import bgr8_to_jpeg\n", + "\n", + "camera = USBCamera(width=WIDTH, height=HEIGHT, capture_fps=30, capture_device=1)\n", + "#camera = CSICamera(width=WIDTH, height=HEIGHT, capture_fps=30)\n", + "\n", + "camera.running = True" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "66e0849369fd4c479432cfc61a0b2a2e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Image(value=b'', format='jpeg', height='256', width='256')" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ipywidgets\n", + "from IPython.display import display\n", + "\n", + "\n", + "image_w = ipywidgets.Image(format='jpeg', width=256, height=256)\n", + "display(image_w)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def execute(change):\n", + " image = change['new']\n", + " data = preprocess(image)\n", + " cmap, paf = model_trt(data)\n", + " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", + " counts, objects, peaks = parse_objects(cmap, paf)#, cmap_threshold=0.15, link_threshold=0.15)\n", + " draw_objects(image, counts, objects, peaks)\n", + " joints = preprocessdata.joints_inference(image, counts, objects, peaks)\n", + " dist_bn_joints = preprocessdata.find_distance(joints)\n", + " gesture = clf.predict([dist_bn_joints,[0]*num_parts*num_parts])\n", + " gesture_joints = gesture[0]\n", + " preprocessdata.prev_queue.append(gesture_joints)\n", + " preprocessdata.prev_queue.pop(0)\n", + " preprocessdata.print_label(image, preprocessdata.prev_queue)\n", + " image_w.value = bgr8_to_jpeg(image)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "execute({'new': camera.value})" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "camera.observe(execute, names='value')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "camera.unobserve_all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#camera.running = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tasks/hand_pose/gesture_data_collection.ipynb b/tasks/hand_pose/gesture_data_collection.ipynb index 79b283b..0658057 100644 --- a/tasks/hand_pose/gesture_data_collection.ipynb +++ b/tasks/hand_pose/gesture_data_collection.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -42,19 +42,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ - "dir_datasets = '/home/mikyas/mike_dataset/bbbb/'\n", + "dir_datasets = '/home/mikyas/mike_dataset/jj/'\n", "dataset_name = \"hand_dataset\"\n", - "no_of_classes = 3\n", + "no_of_classes = 5\n", "create_directories_for_classes(no_of_classes, dir_datasets, dataset_name )" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -63,7 +63,7 @@ "False" ] }, - "execution_count": 6, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -151,18 +151,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "367e2321d6904e06b8c5124b1f130d95", + "model_id": "fe359c9b5fe04069acc2f1d3549d5415", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Image(value=b'', format='jpeg', height='256', width='256')" + "Image(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xff\\xdb\\x00C\\x00\\x02\\x01\\x0…" ] }, "metadata": {}, @@ -171,7 +171,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dce7390567824591bc21e4de5e0acbae", + "model_id": "025ec73f99464909b3ba3aca3fa1799c", "version_major": 2, "version_minor": 0 }, @@ -185,7 +185,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f899e6f4630c489aa6e1fbe385e44bb4", + "model_id": "71a2b3ee3ecc4ed9b66a5f9a3ee6fe02", "version_major": 2, "version_minor": 0 }, @@ -199,7 +199,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "caec93af4f014f9c942c7f1a9b90671c", + "model_id": "7c858c4fc67d4a51a0143bd55bbe5068", "version_major": 2, "version_minor": 0 }, @@ -220,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -299,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -309,16 +309,29 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 36, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "/home/mikyas/mike_dataset/jj/hand_dataset\n", + "0\n", + "1\n", + "2\n", + "3\n", + "4\n" + ] + }, { "data": { "text/plain": [ - "'/home/mikyas/mike_dataset/bbbb/hand_dataset/hand_dataset.json'" + "'/home/mikyas/mike_dataset/jj/hand_dataset/hand_dataset.json'" ] }, - "execution_count": 18, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -330,7 +343,7 @@ " os.makedirs(dir_training)\n", "except FileExistsError:\n", " print(os.path.join(\"The following directory was not created because it already exsists\", dir_ , ))\n", - "for i in range(len(os.listdir(dir_datasets))):\n", + "for i in range(len(os.listdir(dir_))):\n", " dir_to_check = os.path.join(dir_,\"%s\" % (i+1))+'/'\n", " for count, filename in enumerate(os.listdir(dir_to_check)):\n", " src = dir_to_check+filename\n", @@ -342,10 +355,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "camera.running = True" + ] }, { "cell_type": "code", @@ -367,6 +382,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" } }, "nbformat": 4, diff --git a/tasks/hand_pose/gesture_data_collection_with_pose.ipynb b/tasks/hand_pose/gesture_data_collection_with_pose.ipynb index 41edc72..a685d2e 100644 --- a/tasks/hand_pose/gesture_data_collection_with_pose.ipynb +++ b/tasks/hand_pose/gesture_data_collection_with_pose.ipynb @@ -2,17 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-_q7c2z7b because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import json\n", @@ -27,20 +19,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "with open('hand_pose.json', 'r') as f:\n", " hand_pose = json.load(f)\n", @@ -53,16 +34,22 @@ "\n", "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", "import torch\n", - "trt_weight_available = False \n", - "if trt_weight_available:\n", - " MODEL_WEIGHTS = 'epoch_4150.pth'\n", - " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + "\n", "\n", "WIDTH = 256\n", "HEIGHT = 256\n", - "\n", "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", - "OPTIMIZED_MODEL = 'epoch_4150_trt.pth'\n", + "\n", + "if not os.path.exists('resnet18_244x224_epoch_4150_trt.pth'):\n", + " MODEL_WEIGHTS = 'resnet18_244x224_epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + " import torch2trt\n", + " model_trt = torch2trt.torch2trt(model, [data], fp16_mode=True, max_workspace_size=1<<25)\n", + " OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + " torch.save(model_trt.state_dict(), OPTIMIZED_MODEL)\n", + "\n", + "\n", + "OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", "from torch2trt import TRTModule\n", "\n", "model_trt = TRTModule()\n", @@ -83,10 +70,8 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "This notebook creates a dataset (images and labels as a json file). The dataset created can be used for pose classification. \n", "In order to create a new dataset for gesture recoginition specify the following parameters \n", @@ -120,9 +105,9 @@ "metadata": {}, "outputs": [], "source": [ - "dir_datasets = '/home/mikyas/mike_dataset/ccc/'\n", - "dataset_name = \"hand_dataset\"\n", - "no_of_classes = 3\n", + "dir_datasets = '/home/mikyas/mike_dataset/gesture/'\n", + "dataset_name = \"hand_dataset_test\"\n", + "no_of_classes = 6\n", "create_directories_for_classes(no_of_classes, dir_datasets, dataset_name )" ] }, @@ -155,7 +140,7 @@ "def save_snapshot(directory):\n", " image_path = os.path.join(directory, str(uuid1()) + '.jpg')\n", " with open(image_path, 'wb') as f:\n", - " f.write(image_w.value)\n", + " f.write(image_s.value)\n", "def save_dir():\n", " global curr_dir, dir_count\n", " save_snapshot(curr_dir)\n", @@ -361,12 +346,12 @@ "outputs": [], "source": [ "import shutil\n", - "dir_training = dir_datasets +'/trainig/'\n", + "dir_training = dir_datasets +'/testing/'\n", "try:\n", " os.makedirs(dir_training)\n", "except FileExistsError:\n", " print(os.path.join(\"The following directory was not created because it already exsists\", dir_ , ))\n", - "for i in range(len(os.listdir(dir_datasets))):\n", + "for i in range(len(os.listdir(dir_))):\n", " dir_to_check = os.path.join(dir_,\"%s\" % (i+1))+'/'\n", " for count, filename in enumerate(os.listdir(dir_to_check)):\n", " src = dir_to_check+filename\n", @@ -375,6 +360,34 @@ "shutil.move(dir_training,dir_)\n", "shutil.move(dir_+'.json',dir_)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/tasks/hand_pose/hand_pose_m.json b/tasks/hand_pose/hand_pose_m.json new file mode 100644 index 0000000..e54ca48 --- /dev/null +++ b/tasks/hand_pose/hand_pose_m.json @@ -0,0 +1 @@ +{"supercategory": "hand", "id": 1, "name": "hand", "keypoints": ["palm","thumb_1", "thumb_2", "thumb_3", "thumb_4", "index_finger_1", "index_finger_2", "index_finger_3", "index_finger_4", "middle_finger_1", "middle_finger_2", "middle_finger_3", "middle_finger_4", "ring_finger_1", "ring_finger_2", "ring_finger_3", "ring_finger_4", "baby_finger_1", "baby_finger_2", "baby_finger_3", "baby_finger_4"], "skeleton": [[1, 2], [2, 3], [3, 4], [4, 5], [1, 6], [6, 7], [7, 8], [8, 9], [1, 10], [10, 11], [11, 12], [12, 13], [1, 14], [14, 15], [15, 16], [16, 17], [1, 18], [18, 19], [19, 20], [20, 21]]} diff --git a/tasks/hand_pose/live_hand_pose.ipynb b/tasks/hand_pose/live_hand_pose.ipynb index cbc45fb..58469ca 100644 --- a/tasks/hand_pose/live_hand_pose.ipynb +++ b/tasks/hand_pose/live_hand_pose.ipynb @@ -53,16 +53,22 @@ "\n", "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", "import torch\n", - "trt_weight = False \n", - "if trt_weight:\n", - " MODEL_WEIGHTS = 'epoch_4150.pth'\n", - " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + "\n", "\n", "WIDTH = 256\n", "HEIGHT = 256\n", - "\n", "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", - "OPTIMIZED_MODEL = 'epoch_4150_trt.pth'\n", + "\n", + "if not os.path.exists('resnet18_244x224_epoch_4150_trt.pth'):\n", + " MODEL_WEIGHTS = 'resnet18_244x224_epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + " import torch2trt\n", + " model_trt = torch2trt.torch2trt(model, [data], fp16_mode=True, max_workspace_size=1<<25)\n", + " OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + " torch.save(model_trt.state_dict(), OPTIMIZED_MODEL)\n", + "\n", + "\n", + "OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", "from torch2trt import TRTModule\n", "\n", "model_trt = TRTModule()\n", diff --git a/tasks/hand_pose/preprocessdata.py b/tasks/hand_pose/preprocessdata.py index 06972a2..bbc3c9d 100644 --- a/tasks/hand_pose/preprocessdata.py +++ b/tasks/hand_pose/preprocessdata.py @@ -45,12 +45,14 @@ def joints_inference(self, image, counts, objects, peaks): joints_t.append([round(float(picked_peaks[1]) * width), round(float(picked_peaks[0]) * height)]) joints_pt = joints_t[:self.num_parts] rest_of_joints_t = joints_t[self.num_parts:] + """ #when it does not predict a particular joint in the same association it will try to find it in a different association for i in range(len(rest_of_joints_t)): l = i%self.num_parts if joints_pt[l] == [0,0]: joints_pt[l] = rest_of_joints_t[i] #if nothing is predicted + """ if count == 0: joints_pt = [[0,0]]*self.num_parts return joints_pt @@ -68,17 +70,17 @@ def print_label(self, image, gesture_joints): thickness = 2 fontScale = 0.5 if self.prev_queue == [1]*7: - self.text = 'OK' - elif self.prev_queue == [2]*7: self.text = 'fist' + elif self.prev_queue == [2]*7: + self.text = 'pan' elif self.prev_queue == [3]*7: - self.text = 'pan two' + self.text = 'stop' elif self.prev_queue == [4]*7: - self.text = 'pan one' + self.text = 'peace' elif self.prev_queue == [5]*7: - self.text = 'tumbs up' + self.text = 'fine' elif self.prev_queue == [6]*7: - self.text = 'stop' + self.text = 'no hand' elif self.prev_queue == [7]*7: self.text = 'no hand' image = cv2.putText(image, self.text, org, font, diff --git a/tasks/hand_pose/live_hand_pose_SVM.ipynb b/tasks/hand_pose/train_gesture_classification.ipynb similarity index 86% rename from tasks/hand_pose/live_hand_pose_SVM.ipynb rename to tasks/hand_pose/train_gesture_classification.ipynb index df8bf8b..e91ac79 100644 --- a/tasks/hand_pose/live_hand_pose_SVM.ipynb +++ b/tasks/hand_pose/train_gesture_classification.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-5ru0ruys because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-mn8hww0h because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" ] } ], @@ -54,16 +54,22 @@ "\n", "model = trt_pose.models.resnet18_baseline_att(num_parts, 2 * num_links).cuda().eval()\n", "import torch\n", - "trt_weight_available = False \n", - "if trt_weight_available:\n", - " MODEL_WEIGHTS = 'epoch_4150.pth'\n", - " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + "\n", "\n", "WIDTH = 256\n", "HEIGHT = 256\n", - "\n", "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", - "OPTIMIZED_MODEL = 'epoch_4150_trt.pth'\n", + "\n", + "if not os.path.exists('resnet18_244x224_epoch_4150_trt.pth'):\n", + " MODEL_WEIGHTS = 'resnet18_244x224_epoch_4150.pth'\n", + " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", + " import torch2trt\n", + " model_trt = torch2trt.torch2trt(model, [data], fp16_mode=True, max_workspace_size=1<<25)\n", + " OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + " torch.save(model_trt.state_dict(), OPTIMIZED_MODEL)\n", + "\n", + "\n", + "OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", "from torch2trt import TRTModule\n", "\n", "model_trt = TRTModule()\n", @@ -79,7 +85,7 @@ "from trt_pose.draw_objects import DrawObjects\n", "from trt_pose.parse_objects import ParseObjects\n", "\n", - "parse_objects = ParseObjects(topology,cmap_threshold=0.15, link_threshold=0.15)\n", + "parse_objects = ParseObjects(topology,cmap_threshold=0.12, link_threshold=0.15)\n", "draw_objects = DrawObjects(topology)" ] }, @@ -131,20 +137,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from dataloader import dataloader\n", - "path = \"/home/mikyas/mike_dataset/dh/\"\n", - "label_file = \"dh_training_label.json\"\n", - "test_label = \"dh_test_label.json\"\n", + "path = \"/home/mikyas/mike_dataset/gestures/hand_dataset/\"\n", + "label_file = \"hand_dataset.json\"\n", + "test_label = \"hand_dataset_test.json\"\n", "hand = dataloader(path, label_file, test_label)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -185,33 +191,33 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "train_images, labels_train = hand.smaller_dataset(hand.train_images,90,6)" + "train_images, labels_train = hand.smaller_dataset(hand.train_images,100,6)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "joints_train = data_preprocess(train_images)\n", + "joints_train = data_preprocess(hand.train_images)\n", "joints_test = data_preprocess(hand.test_images)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "svm_train = False\n", "if svm_train:\n", - " clf, predicted = preprocessdata.trainsvm(clf, joints_train, joints_test, labels_train, hand.labels_test)\n", - " filename = 'svmmodel.sav'\n", + " clf, predicted = preprocessdata.trainsvm(clf, joints_train, joints_test, hand.labels_train, hand.labels_test)\n", + " filename = 'svmmodel_new.sav'\n", " pickle.dump(clf, open(filename, 'wb'))\n", "else:\n", " filename = 'svmmodel.sav'\n", @@ -220,47 +226,34 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "preprocessdata.svm_accuracy(clf.predict(joints_test), hand.labels_test)\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3, 5])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "clf.predict([joints_test[40],[0]*num_parts*num_parts])" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clf.predict(joints_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -276,13 +269,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7b0071a1b69d4aae9cac315803ceea35", + "model_id": "0eb636d637824f2596b9f26ee5c970c1", "version_major": 2, "version_minor": 0 }, @@ -305,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -328,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -337,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -346,16 +339,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#camera.unobserve_all()" + "camera.unobserve_all()" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/trt_pose/draw_objects.py b/trt_pose/draw_objects.py index 4e09989..d04570a 100644 --- a/trt_pose/draw_objects.py +++ b/trt_pose/draw_objects.py @@ -24,7 +24,7 @@ def __call__(self, image, object_counts, objects, normalized_peaks): peak = normalized_peaks[0][j][k] x = round(float(peak[1]) * width) y = round(float(peak[0]) * height) - cv2.circle(image, (x, y), 3, color, 2) + #cv2.circle(image, (x, y), 3, color, 2) for k in range(K): c_a = topology[k][2] From e98933e71fbae3b2b259ead9d53b7fd54ccda4da Mon Sep 17 00:00:00 2001 From: MikyasDesta Date: Wed, 2 Dec 2020 02:42:33 -0800 Subject: [PATCH 09/10] Update README.md --- tasks/hand_pose/README.md | 53 ++++++++++++++++++++------------------- 1 file changed, 27 insertions(+), 26 deletions(-) diff --git a/tasks/hand_pose/README.md b/tasks/hand_pose/README.md index 313ae21..7f76c9c 100644 --- a/tasks/hand_pose/README.md +++ b/tasks/hand_pose/README.md @@ -28,39 +28,40 @@ Follow the following instruction from https://github.com/NVIDIA-AI-IOT/trt_pose. ### Step 3 - Run hand pose and it's applications - A) Hand Pose demo +A) Hand Pose demo - - Open and follow live_hand_pose.ipynb notebook. + - Open and follow live_hand_pose.ipynb notebook. - B) Hand gesture recoginition (hand pose classification) - - Install dependecies - - scikit-learn - - pip install -U scikit-learn - - or install it from the source - The current gesture classification model supports six classes (fist, pan, stop, fine, peace, no hand). - More gestures can be added by a simple process of creating your own dataset and training it on an svm model. - An SVM model weight is provided for inference. +B) Hand gesture recoginition (hand pose classification) + - Install dependecies + - scikit-learn + - pip install -U scikit-learn + - or install it from the source + The current gesture classification model supports six classes (fist, pan, stop, fine, peace, no hand). + More gestures can be added by a simple process of creating your own dataset and training it on an svm model. + An SVM model weight is provided for inference. - To make your own hand gesture classification from the hand pose estimation, follow the following steps + To make your own hand gesture classification from the hand pose estimation, follow the following steps - - Create your own dataset using the gesture_data_collection.ipynb or gesture_data_collection_with_pose.ipynb. - This will allow you to create the type of gestures you want to classify. (eg. tumbs up, fist,etc). - This notebook will automatically create a dataset with images and labels that is ready to be trained for gesture classification. + - Create your own dataset using the gesture_data_collection.ipynb or gesture_data_collection_with_pose.ipynb. + This will allow you to create the type of gestures you want to classify. (eg. tumbs up, fist,etc). + This notebook will automatically create a dataset with images and labels that is ready to be trained for gesture classification. - - Train using the train_gesture_classification.ipynb notebook file. It uses an SVM from scikit-learn. - Other types of models can also be experimented. + - Train using the train_gesture_classification.ipynb notebook file. It uses an SVM from scikit-learn. + Other types of models can also be experimented. + C) Cursor control application + + - Install dependecies + - pyautogui + - python3 -m pip install pyautogui + - On jetson install it from the source + + - Open and follow the cursor_control_live_demo.ipynb notebook. + - This will allow you to control your mouse cursor on your desktop. It uses the hand gesture classification. + When your hand geture is pan, you can control the cursor. when it is stop, it's left click. - C) Cursor control application - - Install dependecies - - pyautogui - - python3 -m pip install pyautogui - - On jetson install it from the source - - Open and follow the cursor_control_live_demo.ipynb notebook. - - This will allow you to control your mouse cursor on your desktop. It uses the hand gesture classification. - When your hand geture is pan, you can control the cursor. when it is stop, it's left click. - - D) Mini-Paint +D) Mini-Paint The model was trained using the training script in trt_pose and the hand pose data collected in Nvidia. From 513d99c15ce62457a16b45ffb6cf6ff2e4b3f9a4 Mon Sep 17 00:00:00 2001 From: Mikyas Desta Date: Mon, 7 Dec 2020 15:51:11 -0800 Subject: [PATCH 10/10] new drawing --- .../gesture_classification_live_demo.ipynb | 52 +++++++--- tasks/hand_pose/gesture_classifier.py | 21 ++++ tasks/hand_pose/live_hand_pose.ipynb | 97 ++++++++++--------- tasks/hand_pose/preprocessdata.py | 3 +- 4 files changed, 108 insertions(+), 65 deletions(-) create mode 100644 tasks/hand_pose/gesture_classifier.py diff --git a/tasks/hand_pose/gesture_classification_live_demo.ipynb b/tasks/hand_pose/gesture_classification_live_demo.ipynb index 678adc3..7c9d158 100644 --- a/tasks/hand_pose/gesture_classification_live_demo.ipynb +++ b/tasks/hand_pose/gesture_classification_live_demo.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-ef8bt1at because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" + "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-ik_m4vpa because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" ] } ], @@ -60,16 +60,16 @@ "HEIGHT = 256\n", "data = torch.zeros((1, 3, HEIGHT, WIDTH)).cuda()\n", "\n", - "if not os.path.exists('resnet18_244x224_epoch_4150_trt.pth'):\n", - " MODEL_WEIGHTS = 'resnet18_244x224_epoch_4150.pth'\n", + "if not os.path.exists('26k_face_epoch_3500_trt.pth'):\n", + " MODEL_WEIGHTS = '26k_face_epoch_3500.pth'\n", " model.load_state_dict(torch.load(MODEL_WEIGHTS))\n", " import torch2trt\n", " model_trt = torch2trt.torch2trt(model, [data], fp16_mode=True, max_workspace_size=1<<25)\n", - " OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + " OPTIMIZED_MODEL = '26k_face_epoch_3500_trt.pth'\n", " torch.save(model_trt.state_dict(), OPTIMIZED_MODEL)\n", "\n", "\n", - "OPTIMIZED_MODEL = 'resnet18_244x224_epoch_4150_trt.pth'\n", + "OPTIMIZED_MODEL = '26k_face_epoch_3500_trt.pth'\n", "from torch2trt import TRTModule\n", "\n", "model_trt = TRTModule()\n", @@ -137,17 +137,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "svm_train = False\n", "if svm_train:\n", " clf, predicted = preprocessdata.trainsvm(clf, joints_train, joints_test, hand.labels_train, hand.labels_test)\n", - " filename = 'svmmodel_new.sav'\n", + " filename = 'svmmodel.sav'\n", " pickle.dump(clf, open(filename, 'wb'))\n", "else:\n", - " filename = 'svmmodel_new.sav'\n", + " filename = 'svmmodel.sav'\n", " clf = pickle.load(open(filename, 'rb'))" ] }, @@ -171,11 +171,33 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [], + "source": [ + "def draw_joints(image, joints):\n", + " count = 0\n", + " for i in joints:\n", + " if i==[0,0]:\n", + " count+=1\n", + " if count>= 3:\n", + " return \n", + " for i in joints:\n", + " cv2.circle(image, (i[0],i[1]), 2, (0,0,255), 1)\n", + " cv2.circle(image, (joints[0][0],joints[0][1]), 2, (255,0,255), 1)\n", + " for i in hand_pose['skeleton']:\n", + " if joints[i[0]-1][0]==0 or joints[i[1]-1][0] == 0:\n", + " break\n", + " cv2.line(image, (joints[i[0]-1][0],joints[i[0]-1][1]), (joints[i[1]-1][0],joints[i[1]-1][1]), (0,255,0), 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "66e0849369fd4c479432cfc61a0b2a2e", + "model_id": "c09b79578b5249a48aa40018667da6b6", "version_major": 2, "version_minor": 0 }, @@ -198,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -207,9 +229,9 @@ " data = preprocess(image)\n", " cmap, paf = model_trt(data)\n", " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", - " counts, objects, peaks = parse_objects(cmap, paf)#, cmap_threshold=0.15, link_threshold=0.15)\n", - " draw_objects(image, counts, objects, peaks)\n", + " counts, objects, peaks = parse_objects(cmap, paf)\n", " joints = preprocessdata.joints_inference(image, counts, objects, peaks)\n", + " draw_joints(image, joints)\n", " dist_bn_joints = preprocessdata.find_distance(joints)\n", " gesture = clf.predict([dist_bn_joints,[0]*num_parts*num_parts])\n", " gesture_joints = gesture[0]\n", @@ -221,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -239,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/tasks/hand_pose/gesture_classifier.py b/tasks/hand_pose/gesture_classifier.py new file mode 100644 index 0000000..8f2ad48 --- /dev/null +++ b/tasks/hand_pose/gesture_classifier.py @@ -0,0 +1,21 @@ +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler + +class gesture_classifier: + + def __init__(self): + pass + + def svm_accuracy(self, test_predicted, labels_test): + predicted = [] + for i in range(len(labels_test)): + if labels_test[i]==test_predicted[i]: + predicted.append(0) + else: + predicted.append(1) + accuracy = 1 - sum(predicted)/len(labels_test) + return accuracy + def trainsvm(self, clf, train_data, test_data, labels_train, labels_test): + clf.fit(train_data,labels_train) + predicted_test = clf.predict(test_data) + return clf, predicted_test \ No newline at end of file diff --git a/tasks/hand_pose/live_hand_pose.ipynb b/tasks/hand_pose/live_hand_pose.ipynb index 58469ca..2d60a88 100644 --- a/tasks/hand_pose/live_hand_pose.ipynb +++ b/tasks/hand_pose/live_hand_pose.ipynb @@ -2,17 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-i_5u1bwm because the default path (/home/mikyas/.cache/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", "import cv2\n", @@ -27,20 +19,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "with open('hand_pose.json', 'r') as f:\n", " hand_pose = json.load(f)\n", @@ -77,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -112,6 +93,18 @@ " return image[None, ...]" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from preprocessdata import preprocessdata\n", + "preprocessdata = preprocessdata(topology, num_parts)\n", + "from gesture_classifier import gesture_classifier\n", + "gesture_classifier = gesture_classifier()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -121,7 +114,29 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def draw_joints(image, joints):\n", + " count = 0\n", + " for i in joints:\n", + " if i==[0,0]:\n", + " count+=1\n", + " if count>= 3:\n", + " return \n", + " for i in joints:\n", + " cv2.circle(image, (i[0],i[1]), 2, (0,0,255), 1)\n", + " cv2.circle(image, (joints[0][0],joints[0][1]), 2, (255,0,255), 1)\n", + " for i in hand_pose['skeleton']:\n", + " if joints[i[0]-1][0]==0 or joints[i[1]-1][0] == 0:\n", + " break\n", + " cv2.line(image, (joints[i[0]-1][0],joints[i[0]-1][1]), (joints[i[1]-1][0],joints[i[1]-1][1]), (0,255,0), 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -137,24 +152,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b6a3287eaa4647798acc17a87cd942ed", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Image(value=b'', format='jpeg', height='256', width='256')" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import ipywidgets\n", "from IPython.display import display\n", @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -175,14 +175,15 @@ " data = preprocess(image)\n", " cmap, paf = model_trt(data)\n", " cmap, paf = cmap.detach().cpu(), paf.detach().cpu()\n", - " counts, objects, peaks = parse_objects(cmap, paf)#, cmap_threshold=0.15, link_threshold=0.15)\n", - " draw_objects(image, counts, objects, peaks) \n", + " counts, objects, peaks = parse_objects(cmap, paf)\n", + " joints = preprocessdata.joints_inference(image, counts, objects, peaks)\n", + " draw_joints(image, joints)\n", " image_w.value = bgr8_to_jpeg(image[:, ::-1, :])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -191,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/tasks/hand_pose/preprocessdata.py b/tasks/hand_pose/preprocessdata.py index bbc3c9d..ead0ae1 100644 --- a/tasks/hand_pose/preprocessdata.py +++ b/tasks/hand_pose/preprocessdata.py @@ -1,8 +1,7 @@ import math import pickle import cv2 -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import StandardScaler + class preprocessdata: