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import os |
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import cv2 |
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import time |
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import numpy as np |
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import numexpr as ne |
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from multiprocessing.dummy import Process, Queue |
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from options.hifi_test_options import HifiTestOptions |
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from HifiFaceAPI_parallel_base import Consumer0Base, Consumer2Base, Consumer1BaseONNX |
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def np_norm(x): |
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return (x - np.average(x)) / np.std(x) |
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def reverse2wholeimage_hifi_trt_roi(swaped_img, mat_rev, img_mask, frame, roi_img, roi_box): |
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target_image = cv2.warpAffine(swaped_img, mat_rev, roi_img.shape[:2][::-1], borderMode=cv2.BORDER_REPLICATE)[ |
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..., |
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::-1] |
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local_dict = { |
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'img_mask': img_mask, |
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'target_image': target_image, |
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'roi_img': roi_img, |
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} |
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img = ne.evaluate('img_mask * (target_image * 255)+(1 - img_mask) * roi_img', local_dict=local_dict, |
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global_dict=None) |
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img = img.astype(np.uint8) |
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frame[roi_box[1]:roi_box[3], roi_box[0]:roi_box[2]] = img |
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return frame |
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def get_max_face(np_rois): |
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roi_areas = [] |
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for index in range(np_rois.shape[0]): |
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roi_areas.append((np_rois[index, 2] - np_rois[index, 0]) * (np_rois[index, 3] - np_rois[index, 1])) |
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return np.argmax(np.array(roi_areas)) |
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class Consumer0(Consumer0Base): |
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def __init__(self, opt, frame_queue_in, queue_list: list, block=True, fps_counter=False): |
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super().__init__(opt, frame_queue_in, None, queue_list, block, fps_counter) |
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def run(self): |
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counter = 0 |
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start_time = time.time() |
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kpss_old = None |
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rois_old = faces_old = Ms_old = masks_old = None |
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while True: |
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frame = self.frame_queue_in.get() |
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if frame is None: |
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break |
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try: |
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_, bboxes, kpss = self.scrfd_detector.get_bboxes(frame, max_num=0) |
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rois, faces, Ms, masks = self.face_alignment.forward( |
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frame, bboxes, kpss, limit=5, min_face_size=30, |
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crop_size=(self.crop_size, self.crop_size), apply_roi=True |
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) |
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except (TypeError, IndexError, ValueError) as e: |
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self.queue_list[0].put([None, frame]) |
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continue |
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if len(faces)==0: |
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self.queue_list[0].put([None, frame]) |
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continue |
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elif len(faces)==1: |
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face = np.array(faces[0]) |
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mat = Ms[0] |
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roi_box = rois[0] |
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else: |
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max_index = get_max_face(np.array(rois)) |
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face = np.array(faces[max_index]) |
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mat = Ms[max_index] |
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roi_box = rois[max_index] |
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roi_img = frame[roi_box[1]:roi_box[3], roi_box[0]:roi_box[2]] |
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face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB) |
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self.queue_list[0].put([face, mat, [], frame, roi_img, roi_box]) |
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if self.fps_counter: |
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counter += 1 |
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if (time.time() - start_time) > 10: |
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print("Consumer0 FPS: {}".format(counter / (time.time() - start_time))) |
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counter = 0 |
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start_time = time.time() |
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self.queue_list[0].put(None) |
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print('co stop') |
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class Consumer1(Consumer1BaseONNX): |
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def __init__(self, opt, feature_list, queue_list: list, block=True, fps_counter=False): |
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super().__init__(opt, feature_list, queue_list, block, fps_counter) |
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def run(self): |
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counter = 0 |
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start_time = time.time() |
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while True: |
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something_in = self.queue_list[0].get() |
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if something_in is None: |
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break |
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elif len(something_in) == 2: |
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self.queue_list[1].put([None, something_in[1]]) |
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continue |
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if len(self.feature_list) > 1: |
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self.feature_list.pop(0) |
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image_latent = self.feature_list[0][0] |
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mask_out, swap_face_out = self.predict(something_in[0], image_latent[0].reshape(1, -1)) |
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mask = cv2.warpAffine(mask_out[0][0].astype(np.float32), something_in[1], |
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something_in[4].shape[:2][::-1]) |
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mask[mask > 0.2] = 1 |
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mask = mask[:, :, np.newaxis].astype(np.uint8) |
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swap_face = swap_face_out[0].transpose((1, 2, 0)).astype(np.float32) |
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self.queue_list[1].put( |
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[swap_face, something_in[1], mask, something_in[3], something_in[4], something_in[5]]) |
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if self.fps_counter: |
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counter += 1 |
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if (time.time() - start_time) > 10: |
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print("Consumer1 FPS: {}".format(counter / (time.time() - start_time))) |
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counter = 0 |
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start_time = time.time() |
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self.queue_list[1].put(None) |
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print('c1 stop') |
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class Consumer2(Consumer2Base): |
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def __init__(self, queue_list: list, frame_queue_out, block=True, fps_counter=False): |
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super().__init__(queue_list, frame_queue_out, block, fps_counter) |
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self.face_detect_flag = True |
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def forward_func(self, something_in): |
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if len(something_in) == 2: |
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self.face_detect_flag = False |
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frame = something_in[1] |
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frame_out = frame.astype(np.uint8) |
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else: |
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self.face_detect_flag = True |
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swap_face = ((something_in[0] + 1) / 2) |
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frame_out = reverse2wholeimage_hifi_trt_roi( |
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swap_face, something_in[1], something_in[2], |
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something_in[3], something_in[4], something_in[5] |
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) |
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self.frame_queue_out.put([frame_out, self.face_detect_flag]) |
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class HifiFaceRealTime: |
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def __init__(self, feature_dict_list_, frame_queue_in, frame_queue_out, gpu=True, model_name=''): |
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self.opt = HifiTestOptions().parse() |
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if model_name != '': |
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self.opt.model_name = model_name |
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self.opt.input_size = 256 |
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self.feature_dict_list = feature_dict_list_ |
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self.frame_queue_in = frame_queue_in |
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self.frame_queue_out = frame_queue_out |
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self.gpu = gpu |
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def forward(self): |
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self.q0 = Queue(2) |
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self.q1 = Queue(2) |
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self.c0 = Consumer0(self.opt, self.frame_queue_in, [self.q0], fps_counter=False) |
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self.c1 = Consumer1(self.opt, self.feature_dict_list, [self.q0, self.q1], fps_counter=False) |
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self.c2 = Consumer2([self.q1], self.frame_queue_out, fps_counter=False) |
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self.c0.start() |
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self.c1.start() |
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self.c2.start() |
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self.c0.join() |
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self.c1.join() |
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self.c2.join() |
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return |
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