Slow frame about age-gender-estimation HOT 6 CLOSED

Line #      Hits         Time  Per Hit   % Time  Line Contents
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    16                                           @profile
    17                                           def main():
    18                                               # for face detection
    19         1       296641 296641.0      1.5      detector = dlib.get_frontal_face_detector()
    20                                           
    21                                               # load model and weights
    22         1            5      5.0      0.0      img_size = 64
    23         1      3599541 3599541.0     18.7      model = WideResNet(img_size, depth=16, k=8)()
    24         1      2809484 2809484.0     14.6      model.load_weights(os.path.join("pretrained_models", "weights.18-4.06.hdf5"))
    25                                           
    26                                               # capture video
    27         1       754699 754699.0      3.9      cap = cv2.VideoCapture(0)
    28         1          322    322.0      0.0      cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
    29         1        35069  35069.0      0.2      cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
    30                                           
    31         1            4      4.0      0.0      while True:
    32                                                   # get video frame
    33        21       370552  17645.3      1.9          ret, img = cap.read()
    34                                           
    35        21          112      5.3      0.0          if not ret:
    36                                                       print("error: failed to capture image")
    37                                                       return -1
    38                                           
    39        21         4649    221.4      0.0          input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    40        21          294     14.0      0.0          img_h, img_w, _ = np.shape(input_img)
    41                                           
    42                                                   # detect faces using dlib detector
    43        21      4994374 237827.3     26.0          detected = detector(input_img, 1)
    44        21          447     21.3      0.0          faces = np.empty((len(detected), img_size, img_size, 3))
    45                                           
    46        42         1482     35.3      0.0          for i, d in enumerate(detected):
    47        21          138      6.6      0.0              x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
    48        21          143      6.8      0.0              xw1 = max(int(x1 - 0.4 * w), 0)
    49        21           44      2.1      0.0              yw1 = max(int(y1 - 0.4 * h), 0)
    50        21           59      2.8      0.0              xw2 = min(int(x2 + 0.4 * w), img_w - 1)
    51        21           49      2.3      0.0              yw2 = min(int(y2 + 0.4 * h), img_h - 1)
    52        21         3669    174.7      0.0              cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
    53                                                       # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)
    54        21         3102    147.7      0.0              faces[i,:,:,:] = cv2.resize(img[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))
    55                                           
    56        21           64      3.0      0.0          if len(detected) > 0:
    57                                                       # predict ages and genders of the detected faces
    58        21      5350109 254767.1     27.9              results = model.predict(faces)
    59        21           75      3.6      0.0              predicted_genders = results[0]
    60        21          263     12.5      0.0              ages = np.arange(0, 101).reshape(101, 1)
    61        21          715     34.0      0.0              predicted_ages = results[1].dot(ages).flatten()
    62                                           
    63                                                   # draw results
    64        42          940     22.4      0.0          for i, d in enumerate(detected):
    65        21          132      6.3      0.0              label = "{}, {}".format(int(predicted_ages[i]),
    66        21          438     20.9      0.0                                      "F" if predicted_genders[i][0] > 0.5 else "M")
    67        21         1897     90.3      0.0              draw_label(img, (d.left(), d.top()), label)
    68                                           
    69        21       138714   6605.4      0.7          cv2.imshow("result", img)
    70        21       837285  39870.7      4.4          key = cv2.waitKey(30)
    71                                           
    72        21          191      9.1      0.0          if key == 27:
    73         1            2      2.0      0.0              break