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utils.py
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utils.py
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import numpy as np
from math import ceil
import cv2
from kivy.logger import Logger
from kivy.graphics.texture import Texture
image_mean = np.array([127, 127, 127])
image_std = 128.0
iou_threshold = 0.3
center_variance = 0.1
size_variance = 0.2
min_boxes = [[10.0, 16.0, 24.0], [32.0, 48.0], [64.0, 96.0], [128.0, 192.0, 256.0]]
strides = [8.0, 16.0, 32.0, 64.0]
def define_img_size(image_size):
shrinkage_list = []
feature_map_w_h_list = []
for size in image_size:
feature_map = [int(ceil(size / stride)) for stride in strides]
feature_map_w_h_list.append(feature_map)
for i in range(0, len(image_size)):
shrinkage_list.append(strides)
priors = generate_priors(feature_map_w_h_list, shrinkage_list, image_size, min_boxes)
return priors
def generate_priors(feature_map_list, shrinkage_list, image_size, min_boxes):
priors = []
for index in range(0, len(feature_map_list[0])):
scale_w = image_size[0] / shrinkage_list[0][index]
scale_h = image_size[1] / shrinkage_list[1][index]
for j in range(0, feature_map_list[1][index]):
for i in range(0, feature_map_list[0][index]):
x_center = (i + 0.5) / scale_w
y_center = (j + 0.5) / scale_h
for min_box in min_boxes[index]:
w = min_box / image_size[0]
h = min_box / image_size[1]
priors.append([
x_center,
y_center,
w,
h
])
print("priors nums:{}".format(len(priors)))
return np.clip(priors, 0.0, 1.0)
def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
scores = box_scores[:, -1]
boxes = box_scores[:, :-1]
picked = []
indexes = np.argsort(scores)
indexes = indexes[-candidate_size:]
while len(indexes) > 0:
current = indexes[-1]
picked.append(current)
if 0 < top_k == len(picked) or len(indexes) == 1:
break
current_box = boxes[current, :]
indexes = indexes[:-1]
rest_boxes = boxes[indexes, :]
iou = iou_of(
rest_boxes,
np.expand_dims(current_box, axis=0),
)
indexes = indexes[iou <= iou_threshold]
return box_scores[picked, :]
def area_of(left_top, right_bottom):
hw = np.clip(right_bottom - left_top, 0.0, None)
return hw[..., 0] * hw[..., 1]
def iou_of(boxes0, boxes1, eps=1e-5):
overlap_left_top = np.maximum(boxes0[..., :2], boxes1[..., :2])
overlap_right_bottom = np.minimum(boxes0[..., 2:], boxes1[..., 2:])
overlap_area = area_of(overlap_left_top, overlap_right_bottom)
area0 = area_of(boxes0[..., :2], boxes0[..., 2:])
area1 = area_of(boxes1[..., :2], boxes1[..., 2:])
return overlap_area / (area0 + area1 - overlap_area + eps)
def predict(width, height, confidences, boxes, prob_threshold, iou_threshold=0.3, top_k=-1):
boxes = boxes[0]
confidences = confidences[0]
picked_box_probs = []
picked_labels = []
for class_index in range(1, confidences.shape[1]):
probs = confidences[:, class_index]
mask = probs > prob_threshold
probs = probs[mask]
if probs.shape[0] == 0:
continue
subset_boxes = boxes[mask, :]
box_probs = np.concatenate([subset_boxes, probs.reshape(-1, 1)], axis=1)
box_probs = hard_nms(box_probs,
iou_threshold=iou_threshold,
top_k=top_k,
)
picked_box_probs.append(box_probs)
picked_labels.extend([class_index] * box_probs.shape[0])
if not picked_box_probs:
return np.array([]), np.array([]), np.array([])
picked_box_probs = np.concatenate(picked_box_probs)
picked_box_probs[:, 0] *= width
picked_box_probs[:, 1] *= height
picked_box_probs[:, 2] *= width
picked_box_probs[:, 3] *= height
return picked_box_probs[:, :4].astype(np.int32), np.array(picked_labels), picked_box_probs[:, 4]
def convert_locations_to_boxes(locations, priors, center_variance,
size_variance):
if len(priors.shape) + 1 == len(locations.shape):
priors = np.expand_dims(priors, 0)
return np.concatenate([
locations[..., :2] * center_variance * priors[..., 2:] + priors[..., :2],
np.exp(locations[..., 2:] * size_variance) * priors[..., 2:]
], axis=len(locations.shape) - 1)
def center_form_to_corner_form(locations):
return np.concatenate([locations[..., :2] - locations[..., 2:] / 2,
locations[..., :2] + locations[..., 2:] / 2], len(locations.shape) - 1)
def overlap_images(background, overlap):
alpha = overlap[:, :, 3] / 255.0
background[:, :, 0] = (1. - alpha) * background[:, :, 0] + alpha * overlap[:, :, 0]
background[:, :, 1] = (1. - alpha) * background[:, :, 1] + alpha * overlap[:, :, 1]
background[:, :, 2] = (1. - alpha) * background[:, :, 2] + alpha * overlap[:, :, 2]
return background
def find_faces(self, platform):
if (platform == 'android'):
Logger.info(f"Camera: Rotating frame")
self.frame = np.rot90(self.frame)
Logger.info(f"Camera: frame size {self.frame.shape}")
rect = cv2.resize(self.frame, (self.width, self.height))
if (platform == 'android'):
rect = cv2.flip(rect, 0)
rect = cv2.cvtColor(rect, cv2.COLOR_BGR2RGB)
self.model.setInput(cv2.dnn.blobFromImage(rect, 1 / image_std, (self.width, self.height), 127))
boxes, scores = self.model.forward(["boxes", "scores"])
boxes = np.expand_dims(np.reshape(boxes, (-1, 4)), axis=0)
scores = np.expand_dims(np.reshape(scores, (-1, 2)), axis=0)
boxes = convert_locations_to_boxes(boxes, self.priors, center_variance, size_variance)
boxes = center_form_to_corner_form(boxes)
boxes, labels, probs = predict(self.frame.shape[1], self.frame.shape[0], scores, boxes, self.threshold)
Logger.info(f"Model: boxes detected {boxes}")
return boxes
def draw_on_faces(self, boxes, platform):
for i in range(boxes.shape[0]):
box = boxes[i, :]
if any([b < 0 for b in box]):
break
if self.thug_life:
face = self.frame[box[1]:box[3], box[0]:box[2]]
result = self.frame.copy()
if (platform == 'android'):
result = cv2.flip(result, 0)
try:
overlap_image = cv2.resize(self.overlap_image, dsize=(face.shape[1], face.shape[0]))
transparent_background = np.zeros(self.frame.shape, dtype="uint8")
transparent_background[box[1]:box[3], box[0]:box[2]] = overlap_image
overlap_image = transparent_background
result = overlap_images(result, overlap_image)
if (platform == 'android'):
result = cv2.flip(result, 0)
self.frame = result
except Exception as e:
Logger.info(f"Camera: Failed to resize overlap image {e}")
else:
cv2.rectangle(self.frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 4)
return self.frame
def make_new_texture_frame(self):
Logger.info(f"Camera: Displaying frame")
if self.display_speed == 0:
self.frame = cv2.resize(self.frame, (int(self.window_height * self.size_ratio), self.window_height))
elif self.display_speed == 1:
self.frame = cv2.resize(self.frame, (int(800 * self.size_ratio), 800))
else:
self.frame = cv2.resize(self.frame, (int(600 * self.size_ratio), 600))
self.frame = self.frame.reshape((self.frame.shape[1], self.frame.shape[0], 4))
buf = self.frame.tostring()
Logger.info(f"Camera: converted to bytes {len(buf)}")
texture1 = Texture.create(size=(self.frame.shape[0], self.frame.shape[1]), colorfmt='rgba')
texture1.blit_buffer(buf, colorfmt='rgba', bufferfmt='ubyte')
return texture1