38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import numpy as np
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import cv2
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def detect_faces(frame, model, detection_threshold ):
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image = resizeImage(frame)
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outputs, maxpool = model.faceDetection.run([], {'input': image})
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outputs = outputs[0]
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maxpool = maxpool[0]
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mask = outputs[0] == maxpool[0]
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mask2 = outputs[1] > 0.2
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mask = mask.astype(int) * mask2.astype(int)
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outputs[0] = outputs[0] * mask.astype(int)
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faceLocation = np.argmax(outputs[0].flatten())
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x = faceLocation % 56
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y = faceLocation // 56
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if outputs[0, y, x] < detection_threshold:
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return None
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r = outputs[1, y, x] * 112.
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results= (((x * 4) - r, (y * 4) - r, r*2,r*2))
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results = np.array(results).astype(np.float32)
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results[[0,2]] *= frame.shape[1] / 224.
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results[[1,3]] *= frame.shape[0] / 224.
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return results
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def resizeImage(frame):
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mean = np.float32(np.array([-2.1179, -2.0357, -1.8044]))
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std = np.float32(np.array([0.0171, 0.0175, 0.0174]))
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targetimageSize = [224, 224]
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image = cv2.resize(frame, targetimageSize, interpolation=cv2.INTER_LINEAR) * std + mean
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image = np.expand_dims(image, 0)
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image = np.transpose(image, (0,3,1,2))
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return image
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