38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
import numpy as np
|
|
import cv2
|
|
cv2.setNumThreads(6)
|
|
|
|
def detect_faces(frame, model, detection_threshold ):
|
|
|
|
image = resizeImage(frame)
|
|
|
|
outputs, maxpool = model.faceDetection.run([], {'input': image})
|
|
outputs = outputs[0]
|
|
maxpool = maxpool[0]
|
|
mask = outputs[0] == maxpool[0]
|
|
mask2 = outputs[1] > 0.15
|
|
outputs[0] = outputs[0] * mask.astype(int) * mask2.astype(int)
|
|
faceLocation = np.argmax(outputs[0].flatten())
|
|
x = faceLocation % 56
|
|
y = faceLocation // 56
|
|
if outputs[0, y, x] < detection_threshold:
|
|
return None
|
|
r = outputs[1, y, x] * 112.
|
|
results= (((x * 4) - r, (y * 4) - r, r*2,r*2))
|
|
results = np.array(results).astype(np.float32)
|
|
results[[0,2]] *= frame.shape[1] / 224.
|
|
results[[1,3]] *= frame.shape[0] / 224.
|
|
|
|
return results
|
|
|
|
def resizeImage(frame):
|
|
mean = np.float32(np.array([-2.1179, -2.0357, -1.8044]))
|
|
std = np.float32(np.array([0.0171, 0.0175, 0.0174]))
|
|
targetimageSize = [224, 224]
|
|
image = cv2.resize(frame, targetimageSize, interpolation=cv2.INTER_CUBIC) * std + mean
|
|
image = np.expand_dims(image, 0)
|
|
image = np.transpose(image, (0,3,1,2))
|
|
return image
|
|
|
|
|
|
|