45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
import os
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import numpy as np
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import cv2
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import onnxruntime
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import time
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class FaceDetector():
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def __init__(self, detection_threshold=0.6):
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self.mean = np.float32(np.array([-2.1179, -2.0357, -1.8044]))
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self.std = np.float32(np.array([0.0171, 0.0175, 0.0174]))
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self.detection_threshold = detection_threshold
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self.targetimageSize = [224, 224]
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self.image = None
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def detect_faces(self, frame, model):
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self.resizeImage(frame)
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outputs, _ = model.faceDetection.run([], {'input': self.image})
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outputs = outputs[0]
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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] < self.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(self, frame):
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image = cv2.resize(frame, self.targetimageSize, interpolation=cv2.INTER_LINEAR) * self.std + self.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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self.image = image
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