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face_mosaic.py
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import os
import os.path as osp
import time
from typing import List
import cv2
from munch import Munch
import numpy as np
from tqdm import tqdm
from yaml import safe_load
from inferencer import Yolov5OnnxDetectorWithLandmark, MobileFacenetOnnxRecognizer
from tracker.byte_tracker import BYTETracker
from utils import copy_audio, draw_mosaic
class TrackInfo:
def __init__(self, track_id):
self.track_id: int = track_id
self.bboxes: List[np.ndarray] = []
self.det_scores: List[float] = []
self.keypoints: List[np.ndarray] = []
self.features: List[np.ndarray] = []
self.rec_names: List[str] = []
self.rec_confidences: List[float] = []
self.current_index: int = 0 # 当前 track_id 第几次出现
self.best_name: str = None
self.best_conf: float = 0.0
class Results:
def __init__(self, frame_interval):
self.data = {}
self.faces = []
self.frame_interval = frame_interval
def update_recognition_information(self, track_results, src, recognizer):
result = []
for track_obj in track_results:
track_id = track_obj.track_id
score = track_obj.score.item()
if track_id in self.data:
track_info = self.data[track_id]
else:
track_info = TrackInfo(track_id=track_id)
self.data[track_id] = track_info
# print(track_obj.tlbr.astype(np.int64).tolist())
track_info.bboxes.append(track_obj.tlbr.astype(np.int64).tolist().copy())
track_info.det_scores.append(track_obj.score.item())
track_info.keypoints.append(track_obj.pt5.astype(np.int64).tolist().copy())
if track_info.current_index % self.frame_interval == 0:
pred_names, rec_confs, rec_flags = \
recognizer.predict(src, [track_obj.tlbr], [track_obj.pt5])
pred_name = str(pred_names[0]) if rec_flags[0] else 'Unknown'
track_info.rec_names.append(pred_name)
track_info.rec_confidences.append(rec_confs[0].item())
if track_info.best_conf < score * rec_confs[0].item():
track_info.best_name = pred_name
track_info.best_conf = score * rec_confs[0].item()
else:
track_info.rec_names.append(None)
track_info.rec_confidences.append(0.0)
result.append((track_id, track_info.current_index))
track_info.current_index += 1
self.faces.append(result)
def load_config_and_models(config_path):
with open(config_path, 'r') as f:
cfg = Munch.fromDict(safe_load(f))
detector = Yolov5OnnxDetectorWithLandmark(cfg.detector)
recognizer = MobileFacenetOnnxRecognizer(cfg.recognizer)
tracker = BYTETracker(cfg.tracker)
recognizer.set_db(detector)
return cfg, detector, tracker, recognizer
def save_results(video_path, results, save_path, preview=False):
video_capture=cv2.VideoCapture(video_path)
total = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
src_fps = video_capture.get(cv2.CAP_PROP_FPS)
width = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
radius = max(3, int(max(width, height) / 1000))
wait_time = 1 / src_fps
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
if osp.isfile(save_path):
os.remove(save_path)
out = cv2.VideoWriter(save_path, fourcc, src_fps, (width, height))
for i in tqdm(range(total), desc="Display results"):
if i >= len(results.faces):
break
ret, img = video_capture.read()
result = results.faces[i]
for (track_id, index) in result:
det_box = results.data[track_id].bboxes[index]
# draw boundding box
# cv2.rectangle(img, det_box[0:2], det_box[2:4], (0, 255, 0))
# pt5 = results.data[track_id].keypoints[index]
# draw five points
# for point in pt5:
# cv2.circle(img, point, radius, (0, 0, 255), -1)
pred_name = results.data[track_id].best_name
# draw name of recognition
# if pred_name != "Unknown":
# cv2.putText(img, pred_name, det_box[:2], None, 1, (254, 241, 2), 2)
if pred_name is None or pred_name == "Unknown":
draw_mosaic(img, det_box)
out.write(img)
if preview:
cv2.namedWindow("capture", 0)
cv2.imshow("capture", img)
time.sleep(wait_time)
key = cv2.waitKey(1)
if key == ord('q'):
exit()
video_capture.release()
out.release()
cv2.destroyAllWindows()
# write audio
copy_audio(video_path, save_path)
def predict_video(video_path, cfg, detector, tracker, recognizer):
video_capture=cv2.VideoCapture(video_path)
total = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
src_fps = video_capture.get(cv2.CAP_PROP_FPS)
frame_interval = round(src_fps * cfg.recognizer.recognize.time_interval)
results = Results(frame_interval)
# face and landmark detection + face tracking + face recognition
start = time.time()
cnt = 0
for i in tqdm(range(total), desc="Predicting"):
ret, src = video_capture.read()
dst = src.copy()
cnt += 1
if not ret:
break
det_boxes, det_scores, keypoints, flags = detector.predict([src])[0]
track_results = tracker.update(det_boxes, det_scores, keypoints)
results.update_recognition_information(track_results, src, recognizer)
duration = time.time() - start
process_fps = round(cnt / duration)
print("predict fps:", process_fps)
return results
def main():
config_path = 'configs/end2end_config.yml'
video_path = 'videos/Trump3.mp4'
save_path = 'videos/Trump3_mosaic.mp4'
cfg, detector, tracker, recognizer = load_config_and_models(config_path)
results = predict_video(video_path, cfg, detector, tracker, recognizer)
save_results(video_path, results, save_path, preview=False)
if __name__ == "__main__":
main()