def detect_pnet(self, im):
"""Get face candidates through pnet
Parameters:
----------
im: numpy array
input image array
Returns:
-------
boxes: numpy array
detected boxes before calibration
boxes_align: numpy array
boxes after calibration
"""
# im = self.unique_image_format(im)
h, w, c = im.shape
net_size = 12
current_scale = float(
net_size) / self.min_face_size # find initial scale
im_resized = self.resize_image(im, current_scale)
current_height, current_width, _ = im_resized.shape
# fcn
all_boxes = list()
while min(current_height, current_width) > net_size:
feed_imgs = []
image_tensor = convert_image_to_tensor(im_resized)
feed_imgs.append(image_tensor)
feed_imgs = torch.stack(feed_imgs)
feed_imgs = feed_imgs.to(device='cuda: 1', dtype=torch.float32)
cls_map, reg = self.pnet_detector(feed_imgs)
cls_map_np = convert_chwtensor_to_hwcnumpy(cls_map.cpu())
reg_np = convert_chwtensor_to_hwcnumpy(reg.cpu())
# landmark_np = image_tools.convert_chwTensor_to_hwcNumpy(landmark.cpu())
boxes = self.generate_bounding_box(cls_map_np[0, :, :], reg_np,
current_scale, self.thresh[0])
current_scale *= self.scale_factor
im_resized = self.resize_image(im, current_scale)
current_height, current_width, _ = im_resized.shape
if boxes.size == 0:
continue
keep = utils.nms(boxes[:, :5], 0.5, 'Union')
boxes = boxes[keep]
all_boxes.append(boxes)
if len(all_boxes) == 0:
return None, None
all_boxes = np.vstack(all_boxes)
# merge the detection from first stage
keep = utils.nms(all_boxes[:, 0:5], 0.7, 'Union')
all_boxes = all_boxes[keep]
# boxes = all_boxes[:, :5]
bw = all_boxes[:, 2] - all_boxes[:, 0] + 1
bh = all_boxes[:, 3] - all_boxes[:, 1] + 1
# landmark_keep = all_boxes[:, 9:].reshape((5,2))
boxes = np.vstack([
all_boxes[:, 0],
all_boxes[:, 1],
all_boxes[:, 2],
all_boxes[:, 3],
all_boxes[:, 4],
])
boxes = boxes.T
align_topx = all_boxes[:, 0] + all_boxes[:, 5] * bw
align_topy = all_boxes[:, 1] + all_boxes[:, 6] * bh
align_bottomx = all_boxes[:, 2] + all_boxes[:, 7] * bw
align_bottomy = all_boxes[:, 3] + all_boxes[:, 8] * bh
# refine the boxes
boxes_align = np.vstack([
align_topx,
align_topy,
align_bottomx,
align_bottomy,
all_boxes[:, 4],
])
boxes_align = boxes_align.T
return boxes_align
def detect_rnet(self, im, dets): ''' get face candidates using rnet :param im: input image array :param dets: detection results of pnet :return: boxes_align ''' h, w, c = im.shape if dets is None: return None dets = self.square_bbox(dets) dets[:, 0:4] = np.round(dets[:, 0:4]) [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self.pad(dets, w, h) num_boxes = dets.shape[0] cropped_ims_tensors = [] for i in range(num_boxes): #if dx[i] <= 0 or dy[i] <= 0 or edx[i] <= 0 or edy[i] <= 0: #continue '''if (ex[i] < x[i] or ey[i] < y[i] or edx[i] < dx[i] or edy[i] < dy[i] or x[i] > tmpw[i] or y[i] > tmph[i] or ex[i] < 0 or ey[i] < 0): continue''' #list = [x[i], y[i], ex[i], ey[i], dx[i], dy[i], edx[i], edy[i], tmpw[i], tmph[i]] #print(list) try: tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8) if edy[i] != ey[i]-y[i] or edx[i] != ex[i] - x[i]: continue tmp[dy[i]:edy[i] + dy[i] + 1, dx[i]:edx[i] + dx[i] + 1, :] = im[y[i]:ey[i] + 1, x[i]:ex[i] + 1, :] crop_im = cv2.resize(tmp, (24, 24)) crop_im_tensor = convert_image_to_tensor(crop_im) # cropped_ims_tensors[i, :, :, :] = crop_im_tensor cropped_ims_tensors.append(crop_im_tensor) except: continue if len(cropped_ims_tensors) == 0: return None, None cropped_ims_tensors = torch.stack(cropped_ims_tensors) feed_imgs = cropped_ims_tensors.to(device='cuda: 1', dtype=torch.float32) cls_map, reg = self.rnet_detector(feed_imgs) cls_map = cls_map.cpu().data.numpy() reg = reg.cpu().data.numpy() keep_inds = np.where(cls_map > self.thresh[1])[0] if len(keep_inds) > 0: boxes = dets[keep_inds] cls = cls_map[keep_inds] reg = reg[keep_inds] # landmark = landmark[keep_inds] else: return None, None keep = utils.nms(boxes, 0.7) if len(keep) == 0: return None, None keep_cls = cls[keep] keep_boxes = boxes[keep] keep_reg = reg[keep] # keep_landmark = landmark[keep] bw = keep_boxes[:, 2] - keep_boxes[:, 0] + 1 bh = keep_boxes[:, 3] - keep_boxes[:, 1] + 1 boxes = np.vstack([keep_boxes[:, 0], keep_boxes[:, 1], keep_boxes[:, 2], keep_boxes[:, 3], keep_cls[:, 0], # keep_boxes[:,0] + keep_landmark[:, 0] * bw, # keep_boxes[:,1] + keep_landmark[:, 1] * bh, # keep_boxes[:,0] + keep_landmark[:, 2] * bw, # keep_boxes[:,1] + keep_landmark[:, 3] * bh, # keep_boxes[:,0] + keep_landmark[:, 4] * bw, # keep_boxes[:,1] + keep_landmark[:, 5] * bh, # keep_boxes[:,0] + keep_landmark[:, 6] * bw, # keep_boxes[:,1] + keep_landmark[:, 7] * bh, # keep_boxes[:,0] + keep_landmark[:, 8] * bw, # keep_boxes[:,1] + keep_landmark[:, 9] * bh, ]) align_topx = keep_boxes[:, 0] + keep_reg[:, 0] * bw align_topy = keep_boxes[:, 1] + keep_reg[:, 1] * bh align_bottomx = keep_boxes[:, 2] + keep_reg[:, 2] * bw align_bottomy = keep_boxes[:, 3] + keep_reg[:, 3] * bh boxes_align = np.vstack([align_topx, align_topy, align_bottomx, align_bottomy, keep_cls[:, 0], # align_topx + keep_landmark[:, 0] * bw, # align_topy + keep_landmark[:, 1] * bh, # align_topx + keep_landmark[:, 2] * bw, # align_topy + keep_landmark[:, 3] * bh, # align_topx + keep_landmark[:, 4] * bw, # align_topy + keep_landmark[:, 5] * bh, # align_topx + keep_landmark[:, 6] * bw, # align_topy + keep_landmark[:, 7] * bh, # align_topx + keep_landmark[:, 8] * bw, # align_topy + keep_landmark[:, 9] * bh, ]) boxes = boxes.T boxes_align = boxes_align.T return boxes, boxes_align