AttributeError: 'NoneType' object has no attribute 'shape'

前提・実現したいこと

以下を使って、物体間の距離が一定以下になった回数の検出を試みています。
https://github.com/basileroth75/covid-social-distancing-detection

プログラミングは完全な初心者ですが、仕事の関係で手探りで行っているので、
文章などおかしいかもしれませんがご容赦ください。

発生している問題・エラーメッセージ

サンプルではうまくいくのですが、自分で学習させたモデル？を使った場合エラーが出てしまいます。

Traceback (most recent call last):
File "social_distanciation_video_detection.py", line 193, in <module>
transformed_downoids = compute_point_perspective_transformation(matrix,array_groundpoints)
File "C:\covid-social-distancing-detection\src\bird_view_transfo_functions.py", line 31, in compute_point_perspective_transformation
for i in range(0,transformed_points.shape[0]):
AttributeError: 'NoneType' object has no attribute 'shape'

該当のソースコード

social_distanciation_video_detection.py

python
1
2######################################### 
3#		     Select the model 			#
4#########################################
5model_names_list = [name for name in os.listdir("../models/.") if name.find(".") == -1]
6for index,model_name in enumerate(model_names_list):
7    print(" - {} [{}]".format(model_name,index))
8model_num = input(" Please select the number related to the model that you want : ")
9if model_num == "":
10	model_path="../models/faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb" 
11else :
12	model_path = "../models/"+model_names_list[int(model_num)]+"/frozen_inference_graph.pb"
13print(bcolors.WARNING + " [ Loading the TENSORFLOW MODEL ... ]"+bcolors.ENDC)
14model = Model(model_path)
15print(bcolors.OKGREEN +"Done : [ Model loaded and initialized ] ..."+bcolors.ENDC)
16
17
18######################################### 
19#		     Select the video 			#
20#########################################
21video_names_list = [name for name in os.listdir("../video/") if name.endswith(".mp4") or name.endswith(".avi")]
22for index,video_name in enumerate(video_names_list):
23    print(" - {} [{}]".format(video_name,index))
24video_num = input("Enter the exact name of the video (including .mp4 or else) : ")
25if video_num == "":
26	video_path="../video/PETS2009.avi"  
27else :
28	video_path = "../video/"+video_names_list[int(video_num)]
29
30
31######################################### 
32#		    Minimal distance			#
33#########################################
34distance_minimum = input("Prompt the size of the minimal distance between 2 pedestrians : ")
35if distance_minimum == "":
36	distance_minimum = "110"
37
38
39######################################### 
40#     Compute transformation matrix		#
41#########################################
42# Compute  transformation matrix from the original frame
43matrix,imgOutput = compute_perspective_transform(corner_points,width_og,height_og,cv2.imread(img_path))
44height,width,_ = imgOutput.shape
45blank_image = np.zeros((height,width,3), np.uint8)
46height = blank_image.shape[0]
47width = blank_image.shape[1] 
48dim = (width, height)
49
50
51
52
53######################################################
54#########									 #########
55# 				START THE VIDEO STREAM               #
56#########									 #########
57######################################################
58vs = cv2.VideoCapture(video_path)
59output_video_1,output_video_2 = None,None
60# Loop until the end of the video stream
61while True:	
62	# Load the image of the ground and resize it to the correct size
63	img = cv2.imread("../img/chemin_1.png")
64	bird_view_img = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
65	
66	# Load the frame
67	(frame_exists, frame) = vs.read()
68	# Test if it has reached the end of the video
69	if not frame_exists:
70		break
71	else:
72		# Resize the image to the correct size
73		frame = imutils.resize(frame, width=int(size_frame))
74		
75		# Make the predictions for this frame
76		(boxes, scores, classes) =  model.predict(frame)
77
78		# Get the human detected in the frame and return the 2 points to build the bounding box  
79		array_boxes_detected = get_human_box_detection(boxes,scores[0].tolist(),classes[0].tolist(),frame.shape[0],frame.shape[1])
80		
81		# Both of our lists that will contain the centroﾃｯds coordonates and the ground points
82		array_centroids,array_groundpoints = get_centroids_and_groundpoints(array_boxes_detected)
83
84		# Use the transform matrix to get the transformed coordonates
85		transformed_downoids = compute_point_perspective_transformation(matrix,array_groundpoints)
86		
87		# Show every point on the top view image 
88		for point in transformed_downoids:
89			x,y = point
90			cv2.circle(bird_view_img, (int(x),int(y)), BIG_CIRCLE, COLOR_GREEN, 2)
91			cv2.circle(bird_view_img, (int(x),int(y)), SMALL_CIRCLE, COLOR_GREEN, -1)
92
93		# Check if 2 or more people have been detected (otherwise no need to detect)
94		if len(transformed_downoids) >= 2:
95			for index,downoid in enumerate(transformed_downoids):
96				if not (downoid[0] > width or downoid[0] < 0 or downoid[1] > height+200 or downoid[1] < 0 ):
97					cv2.rectangle(frame,(array_boxes_detected[index][1],array_boxes_detected[index][0]),(array_boxes_detected[index][3],array_boxes_detected[index][2]),COLOR_GREEN,2)
98
99			# Iterate over every possible 2 by 2 between the points combinations 
100			list_indexes = list(itertools.combinations(range(len(transformed_downoids)), 2))
101			for i,pair in enumerate(itertools.combinations(transformed_downoids, r=2)):
102				# Check if the distance between each combination of points is less than the minimum distance chosen
103				if math.sqrt( (pair[0][0] - pair[1][0])**2 + (pair[0][1] - pair[1][1])**2 ) < int(distance_minimum):
104					# Change the colors of the points that are too close from each other to red
105					if not (pair[0][0] > width or pair[0][0] < 0 or pair[0][1] > height+200  or pair[0][1] < 0 or pair[1][0] > width or pair[1][0] < 0 or pair[1][1] > height+200  or pair[1][1] < 0):
106						change_color_on_topview(pair)
107						# Get the equivalent indexes of these points in the original frame and change the color to red
108						index_pt1 = list_indexes[i][0]
109						index_pt2 = list_indexes[i][1]
110						cv2.rectangle(frame,(array_boxes_detected[index_pt1][1],array_boxes_detected[index_pt1][0]),(array_boxes_detected[index_pt1][3],array_boxes_detected[index_pt1][2]),COLOR_RED,2)
111						cv2.rectangle(frame,(array_boxes_detected[index_pt2][1],array_boxes_detected[index_pt2][0]),(array_boxes_detected[index_pt2][3],array_boxes_detected[index_pt2][2]),COLOR_RED,2)
112
113
114	# Draw the green rectangle to delimitate the detection zone
115	draw_rectangle(corner_points)
116	# Show both images	
117	cv2.imshow("Bird view", bird_view_img)
118	cv2.imshow("Original picture", frame)
119
120
121	key = cv2.waitKey(1) & 0xFF
122
123	# Write the both outputs video to a local folders
124	if output_video_1 is None and output_video_2 is None:
125		fourcc1 = cv2.VideoWriter_fourcc(*"MJPG")
126		output_video_1 = cv2.VideoWriter("../output/video.avi", fourcc1, 25,(frame.shape[1], frame.shape[0]), True)
127		fourcc2 = cv2.VideoWriter_fourcc(*"MJPG")
128		output_video_2 = cv2.VideoWriter("../output/bird_view.avi", fourcc2, 25,(bird_view_img.shape[1], bird_view_img.shape[0]), True)
129	elif output_video_1 is not None and output_video_2 is not None:
130		output_video_1.write(frame)
131		output_video_2.write(bird_view_img)
132
133	# Break the loop
134	if key == ord("q"):
135		break
136
137

bird_view_transfo_functions.py

python
1import numpy as np
2import cv2
3
4
5def compute_perspective_transform(corner_points,width,height,image):
6	""" Compute the transformation matrix
7    @ corner_points : 4 corner points selected from the image
8    @ height, width : size of the image
9    """
10	# Create an array out of the 4 corner points
11	corner_points_array = np.float32(corner_points)
12	# Create an array with the parameters (the dimensions) required to build the matrix
13	img_params = np.float32([[0,0],[width,0],[0,height],[width,height]])
14	# Compute and return the transformation matrix
15	matrix = cv2.getPerspectiveTransform(corner_points_array,img_params) 
16	img_transformed = cv2.warpPerspective(image,matrix,(width,height))
17	return matrix,img_transformed
18
19
20def compute_point_perspective_transformation(matrix,list_downoids):
21	""" Apply the perspective transformation to every ground point which have been detected on the main frame.
22    @ matrix : the 3x3 matrix 
23    @ list_downoids : list that contains the points to transform
24    return : list containing all the new points
25    """
26	# Compute the new coordinates of our points
27	list_points_to_detect = np.float32(list_downoids).reshape(-1, 1, 2)
28	transformed_points = cv2.perspectiveTransform(list_points_to_detect, matrix)
29	# Loop over the points and add them to the list that will be returned
30	transformed_points_list = list()
31	for i in range(0,transformed_points.shape[0]):
32		transformed_points_list.append([transformed_points[i][0][0],transformed_points[i][0][1]])
33	return transformed_points_list
34