def detect_objects(image_np, sess, detection_graph):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
rect_points, class_names, class_colors = draw_boxes_and_labels(
boxes=np.squeeze(boxes),
classes=np.squeeze(classes).astype(np.int32),
scores=np.squeeze(scores),
category_index=category_index,
min_score_thresh=.5
)
return dict(rect_points=rect_points, class_names=class_names, class_colors=class_colors)
def detect_objects(image_np, sess, detection_graph):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes,
num_detections) = sess.run([boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
rect_points, class_names, class_colors = draw_boxes_and_labels(
boxes=np.squeeze(boxes),
classes=np.squeeze(classes).astype(np.int32),
scores=np.squeeze(scores),
category_index=category_index,
min_score_thresh=.5)
return dict(rect_points=rect_points,
class_names=class_names,
class_colors=class_colors)
def detect_objects(image_np, sess, detection_graph, category_index):
# 扩展图像的尺寸, 因为模型要求图像具有形状: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# 检测结果，每个框表示检测到特定对象的图像的一部分
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# scores表示识别出的物体的置信度 是一个概率值，他最后会与label一起显示在图像上
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# 运行会话开始检测并返回结果，feed为传入的图像张量
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# 检测结果的可视化，也就是将返回的框框画在图像上
# 返回框坐标、类名和颜色
rect_points, class_names, class_colors = draw_boxes_and_labels(
boxes=np.squeeze(boxes),
classes=np.squeeze(classes).astype(np.int32),
scores=np.squeeze(scores),
category_index=category_index,
min_score_thresh=.3 # 可视化的最低分数阈值
)
return dict(rect_points=rect_points, class_names=class_names, class_colors=class_colors)
def detect_objects(image_np, sess, detection_graph, width, height, one_width,
one_height, index, rect_points, class_names, class_colors):
#print('1'+str(rect_points))
if index % 3 == 0:
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
rect_points, class_names, class_colors = draw_boxes_and_labels(
boxes=np.squeeze(boxes),
classes=np.squeeze(classes).astype(np.int32),
scores=np.squeeze(scores),
category_index=category_index,
min_score_thresh=.5)
#print('2'+str(rect_points))
font = cv2.FONT_HERSHEY_SIMPLEX
for point, name, color in zip(rect_points, class_names, class_colors):
if point['xmax'] - point['xmin'] < 0.4:
cv2.rectangle(
image_np,
(int(point['xmin'] * width), int(point['ymin'] * height)),
(int(point['xmax'] * width), int(point['ymax'] * height)),
color, 3)
cv2.rectangle(
image_np,
(int(point['xmin'] * width), int(point['ymin'] * height)),
(int(point['xmin'] * width) + len(name[0]) * 6,
int(point['ymin'] * height) - 10), color, -1, cv2.LINE_AA)
cv2.putText(
image_np, name[0],
(int(point['xmin'] * width), int(point['ymin'] * height)),
font, 0.3, (0, 0, 0), 1)
return cv2.resize(
image_np,
(one_width, one_height)), rect_points, class_names, class_colors
def get_CNN_output(self, image_np):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
masks = self.detection_graph.get_tensor_by_name('detection_masks:0')
# Actual detection.
(boxes, scores, classes, num_detections, masks) = \
self.sess.run([boxes, scores, classes, num_detections, masks], feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
rect_points, class_scores, class_colors = draw_boxes_and_labels(
boxes=np.squeeze(boxes),
classes=np.squeeze(classes).astype(np.int32),
scores=np.squeeze(scores),
min_score_thresh=.3)
ma_alpha = 0.1
for rp, cs, ma in zip(rect_points, class_scores, masks[0]):
abs_xmin = int(rp['xmin'] * self.slice_size[1] + self.offset[1])
abs_ymin = int(rp['ymin'] * self.slice_size[0] + self.offset[0])
abs_xmax = np.min(
(int(rp['xmax'] * self.slice_size[1] + self.offset[1]),
self.frame_shape[1]))
abs_ymax = np.min(
(int(rp['ymax'] * self.slice_size[0] + self.offset[0]),
self.frame_shape[0]))
if abs_xmax <= abs_xmin or abs_ymax <= abs_ymin:
continue
ma_overlay = np.zeros(self.frame_shape[:2], dtype=np.uint8)
ma_overlay[abs_ymin:abs_ymax, abs_xmin:abs_xmax] = cs[0]
self.moving_avg_image = cv2.addWeighted(ma_overlay, ma_alpha,
self.moving_avg_image,
1 - ma_alpha, 0)
self.detections.append(
dict(rel_rect=rp,
score=cs,
abs_rect=(abs_xmin, abs_ymin, abs_xmax, abs_ymax),
mask=ma))
def detect_objects(image_np, sess, detection_graph):
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
(boxes, scores, classes,
num_detections) = sess.run([boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
rect_points, class_names, class_colors = draw_boxes_and_labels(
boxes=np.squeeze(boxes),
classes=np.squeeze(classes).astype(np.int32),
scores=np.squeeze(scores),
category_index=category_index,
min_score_thresh=.5)
return dict(rect_points=rect_points,
class_names=class_names,
class_colors=class_colors)
def detect_objects(image_np, sess, detection_graph):
global detectioncounter
global emptyframecounter
global pause
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes,
num_detections) = sess.run([boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
#comine results form cv to be processed later
combined = list(
map(lambda x, y, z: [float(x), float(y),
z.tolist()], scores[0], classes[0], boxes[0]))
bigger_than_threshold = list(
filter(lambda x: x[0] >= MIN_THRESHOLD, combined))
#when objects are recognized, it is counted which objects are there.
# also detectioncounter is counted up. If this 10 was overruled, we got the object counter.
#If no object is selected and it expires
#the objectCounter and detectioncounter are set to 0.
if pause > 0: #pause detection
pause -= 1
else:
if len(bigger_than_threshold) != 0:
countObjects(bigger_than_threshold)
detectioncounter += 1
emptyframecounter = 0
if detectioncounter > TRIGGER_TIME:
evaluateObjectcounter()
detectioncounter = 0
else:
emptyframecounter += 1
if emptyframecounter > RESET_TIME:
for i in range(0, 11):
objectCounter[i][0] = 0
detectioncounter = 0
emptyframecounter = 0
# Visualization of the results of a detection.
rect_points, class_names, class_colors = draw_boxes_and_labels(
boxes=np.squeeze(boxes),
classes=np.squeeze(classes).astype(np.int32),
scores=np.squeeze(scores),
category_index=category_index,
min_score_thresh=MIN_THRESHOLD)
return dict(rect_points=rect_points,
class_names=class_names,
class_colors=class_colors)
