Js docker (#897)

* fixed docker bug minimized the size for docker by removing cache and optimizing which torch is installed

* fixed introspection error now docker should be good to go

* removed webots as an external module and replaced with a script from bit bots to install. this will make the code a lot more modular and decrease size. Made scripts to install and get the env uptodate so that new members dont have to struggle on installitaion

* Removed .idea files

* fixed src problem

* fixed src problem

.gitignore

@@ -8,6 +8,7 @@
 __pycache__/
 *.pyc
 .idea
+!.idea/soccerbot.iml
 # webots cache files
 soccer_webots/protos/*.cache
 soccer_webots/protos/Soccerbot_meshes/*.cache

pyproject.toml

@@ -11,7 +11,7 @@ authors = [
 ]
 description = "Main software for bez the humanoid soccer player"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = "=3.8"
 license = {text = "BSD-3-Clause"}
 dynamic = ["version", "dependencies", "optional-dependencies"]
 

soccer_perception/soccer_object_detection/src/soccer_object_detection/sam_poc.py

@@ -0,0 +1,89 @@
+import os
+import sys
+from os.path import expanduser
+
+import cv2
+import matplotlib
+import numpy as np
+
+matplotlib.use("agg")
+import matplotlib.pyplot as plt
+import torch
+from segment_anything import SamPredictor, sam_model_registry
+
+
+def show_mask(mask, ax, random_color=False):
+    if random_color:
+        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
+    else:
+        color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
+    h, w = mask.shape[-2:]
+    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+    ax.imshow(mask_image)
+
+
+def show_points(coords, labels, ax, marker_size=375):
+    pos_points = coords[labels == 1]
+    neg_points = coords[labels == 0]
+    ax.scatter(pos_points[:, 0], pos_points[:, 1], color="green", marker="*", s=marker_size, edgecolor="white", linewidth=1.25)
+    ax.scatter(neg_points[:, 0], neg_points[:, 1], color="red", marker="*", s=marker_size, edgecolor="white", linewidth=1.25)
+
+
+def show_box(box, ax):
+    x0, y0 = box[0], box[1]
+    w, h = box[2] - box[0], box[3] - box[1]
+    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor="green", facecolor=(0, 0, 0, 0), lw=2))
+
+
+src_path = expanduser("~") + "/catkin_ws/src/soccerbot/soccer_perception/"
+test_path = src_path + "data/images/simulation"
+model_path = src_path + "soccer_object_detection/models/half_5.pt"
+
+model2 = torch.hub.load("ultralytics/yolov5", "custom", path=model_path)
+matplotlib.use("TkAgg")  # Change backend after loading model
+
+# set model params
+# initialize the model
+sam_checkpoint = "sam_vit_b_01ec64.pth"
+model_type = "vit_b"
+sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
+predictor = SamPredictor(sam)
+
+for file_name in os.listdir(f"{test_path}/images"):
+    img = cv2.imread(os.path.join(f"{test_path}/images", file_name))
+    img = cv2.resize(img, dsize=(640, 480))
+    results2 = model2(img)
+    l = []
+    for prediction in results2.xyxy[0]:
+        x1, y1, x2, y2, confidence, img_class = prediction.cpu().numpy()
+        l.append([round(x1), round(y1), round(x2), round(y2)])
+    print(l)
+    if len(l) == 0:
+        continue
+    input_boxes = torch.tensor(l, device=predictor.device)
+
+    predictor.set_image(img)
+    # input_box = np.array(l[0])
+    # input_box = np.array([125, 100, 300, 375])
+    #
+    # masks, _, _ = predictor.predict(box=input_box[None, :])
+    # plt.figure(figsize=(10, 10))
+    # plt.imshow(img)
+    # show_mask(masks[0], plt.gca())
+    # show_box(input_box, plt.gca())
+    # plt.axis('off')
+    # plt.show()
+    transformed_boxes = predictor.transform.apply_boxes_torch(input_boxes, img.shape[:2])
+    masks, _, _ = predictor.predict_torch(point_coords=None, point_labels=None, boxes=transformed_boxes, multimask_output=False)
+
+    plt.figure(figsize=(10, 10))
+    plt.imshow(img)
+    for mask in masks:
+        show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
+    for box in input_boxes:
+        show_box(box.cpu().numpy(), plt.gca())
+    plt.axis("off")
+    plt.show()
+    cv2.waitKey()
+    # cv2.destroyAllWindows()
+    # break