def detect_rnet(im, dets, thresh):
"""通过rent选择box
参数：
im：输入图像
dets:pnet选择的box，是相对原图的绝对坐标
返回值：
box绝对坐标
"""
h, w, c = im.shape
# 将pnet的box变成包含它的正方形，可以避免信息损失
dets = convert_to_square(dets)
dets[:, 0:4] = np.round(dets[:, 0:4])
# 调整超出图像的box
[dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = pad(dets, w, h)
delete_size = np.ones_like(tmpw) * 20
ones = np.ones_like(tmpw)
zeros = np.zeros_like(tmpw)
num_boxes = np.sum(
np.where((np.minimum(tmpw, tmph) >= delete_size), ones, zeros))
cropped_ims = np.zeros((num_boxes, 3, 24, 24), dtype=np.float32)
if int(num_boxes) == 0:
print('P模型检测结果为空！')
return None, None
for i in range(int(num_boxes)):
# 将pnet生成的box相对与原图进行裁剪，超出部分用0补
if tmph[i] < 20 or tmpw[i] < 20:
continue
tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8)
try:
tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = im[y[i]:ey[i] + 1,
x[i]:ex[i] + 1, :]
img = cv2.resize(tmp, (24, 24))
img = img.transpose((2, 0, 1))
img = (img - 127.5) / 128
cropped_ims[i, :, :, :] = img
except:
continue
cls_scores, reg = predict_rnet(cropped_ims)
cls_scores = cls_scores[:, 1]
keep_inds = np.where(cls_scores > thresh)[0]
if len(keep_inds) > 0:
boxes = dets[keep_inds]
boxes[:, 4] = cls_scores[keep_inds]
reg = reg[keep_inds]
else:
return None, None
keep = py_nms(boxes, 0.6, mode='Union')
boxes = boxes[keep]
# 对pnet截取的图像的坐标进行校准，生成rnet的人脸框对于原图的绝对坐标
boxes_c = calibrate_box(boxes, reg[keep])
return boxes, boxes_c
def detect_onet(im, dets, thresh):
"""将onet的选框继续筛选基本和rnet差不多但多返回了landmark"""
h, w, c = im.shape
dets = convert_to_square(dets)
dets[:, 0:4] = np.round(dets[:, 0:4])
[dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = pad(dets, w, h)
num_boxes = dets.shape[0]
cropped_ims = np.zeros((num_boxes, 3, 48, 48), dtype=np.float32)
for i in range(num_boxes):
tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8)
tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = im[y[i]:ey[i] + 1,
x[i]:ex[i] + 1, :]
img = cv2.resize(tmp, (48, 48), interpolation=cv2.INTER_LINEAR)
img = img.transpose((2, 0, 1))
img = (img - 127.5) / 128
cropped_ims[i, :, :, :] = img
cls_scores, reg, landmark = predict_onet(cropped_ims)
cls_scores = cls_scores[:, 1]
keep_inds = np.where(cls_scores > thresh)[0]
if len(keep_inds) > 0:
boxes = dets[keep_inds]
boxes[:, 4] = cls_scores[keep_inds]
reg = reg[keep_inds]
landmark = landmark[keep_inds]
else:
return None, None
w = boxes[:, 2] - boxes[:, 0] + 1
h = boxes[:, 3] - boxes[:, 1] + 1
landmark[:, 0::2] = (np.tile(w, (5, 1)) * landmark[:, 0::2].T +
np.tile(boxes[:, 0], (5, 1)) - 1).T
landmark[:, 1::2] = (np.tile(h, (5, 1)) * landmark[:, 1::2].T +
np.tile(boxes[:, 1], (5, 1)) - 1).T
boxes_c = calibrate_box(boxes, reg)
keep = py_nms(boxes_c, 0.6, mode='Minimum')
boxes_c = boxes_c[keep]
landmark = landmark[keep]
return boxes_c, landmark
