Python人脸识别报错

yayc_zcyd

这个人脸识别在20行会报错

1. net = cv2.dnn.readNet(prototxtPath, weightsPath)

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就是这个↑
报错信息：

1. cv2.error: OpenCV(4.6.0) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\caffe\caffe_io.cpp:1126: error: (-2:Unspecified error) FAILED: fs.is_open(). Can't open "D:\opencv-face-blurring\face_detector\deploy.prototxt" in function 'cv::dnn::ReadProtoFromTextFile'
   

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源代码：

1. from zy.face_blurring import anonymize_face_pixelate
   
2. from zy.face_blurring import anonymize_face_simple
   
3. import numpy as np
   
4. import argparse
   
5. import cv2
   
6. import os
   
7. ap = argparse.ArgumentParser()
   
8. ap.add_argument("-m", "--method", type=str, default="simple",
   
9.         choices=["simple", "pixelated"],
   
10.         help="face blurring/anonymizing method")
    
11. ap.add_argument("-b", "--blocks", type=int, default=20,
    
12.         help="# of blocks for the pixelated blurring method")
    
13. ap.add_argument("-c", "--confidence", type=float, default=0.5,
    
14.         help="minimum probability to filter weak detections")
    
15. args = vars(ap.parse_args())
    
16. print("[INFO] loading face detector model...")
    
17. prototxtPath = os.path.sep.join([r'D:\opencv-face-blurring\face_detector', "deploy.prototxt"])
    
18. weightsPath = os.path.sep.join([r'D:\opencv-face-blurring\face_detector',
    
19.         "res10_300x300_ssd_iter_140000.caffemodel"])
    
20. net = cv2.dnn.readNet(prototxtPath, weightsPath)
    
21. image = cv2.imread(r'D:\opencv-face-blurring\examples\zy.png')
    
22. orig = image.copy()
    
23. (h, w) = image.shape[:2]
    
24. blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
    
25.         (104.0, 177.0, 123.0))
    
26. print("[INFO] computing face detections...")
    
27. net.setInput(blob)
    
28. detections = net.forward()
    
29. for i in range(0, detections.shape[2]):
    
30.         confidence = detections[0, 0, i, 2]
    
31.         if confidence > args["confidence"]:
    
32.                 box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
    
33.                 (startX, startY, endX, endY) = box.astype("int")
    
34.                 face = image[startY:endY, startX:endX]
    
35.                 if args["method"] == "pixelated":
    
36.                         face = anonymize_face_simple(face, factor=3.0)
    
37.                 else:
    
38.                         face = anonymize_face_pixelate(face,
    
39.                                 blocks=args["blocks"])
    
40.                 image[startY:endY, startX:endX] = face
    
41. output = np.hstack([orig, image])
    
42. cv2.imshow("Output", output)
    
43. cv2.waitKey(0)

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