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Added new inference code for webcam
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inference.py
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inference.py
@ -1,33 +1,182 @@
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# coding: utf-8
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import tyro
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from src.config.argument_config import ArgumentConfig
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from src.config.inference_config import InferenceConfig
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from src.config.crop_config import CropConfig
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from src.live_portrait_pipeline import LivePortraitPipeline
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import cv2
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import time
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import numpy as np
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def partial_fields(target_class, kwargs):
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return target_class(**{k: v for k, v in kwargs.items() if hasattr(target_class, k)})
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def main():
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# set tyro theme
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tyro.extras.set_accent_color("bright_cyan")
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args = tyro.cli(ArgumentConfig)
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# specify configs for inference
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inference_cfg = partial_fields(InferenceConfig, args.__dict__) # use attribute of args to initial InferenceConfig
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crop_cfg = partial_fields(CropConfig, args.__dict__) # use attribute of args to initial CropConfig
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inference_cfg = partial_fields(InferenceConfig, args.__dict__)
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crop_cfg = partial_fields(CropConfig, args.__dict__)
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live_portrait_pipeline = LivePortraitPipeline(
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inference_cfg=inference_cfg,
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crop_cfg=crop_cfg
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)
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# run
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live_portrait_pipeline.execute(args)
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# Initialize webcam 'assets/examples/driving/d6.mp4'
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cap = cv2.VideoCapture(0)
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# Process the first frame to initialize
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ret, frame = cap.read()
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if not ret:
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print("Failed to capture image")
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return
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source_image_path = args.source_image # Set the source image path here
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x_s, f_s, R_s, x_s_info, lip_delta_before_animation, crop_info, img_rgb = live_portrait_pipeline.execute_frame(frame, source_image_path)
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while True:
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# Capture frame-by-frame
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ret, frame = cap.read()
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if not ret:
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break
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# Process the frame
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result = live_portrait_pipeline.generate_frame(x_s, f_s, R_s, x_s_info, lip_delta_before_animation, crop_info, img_rgb, frame)
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cv2.imshow('img_rgb Image', img_rgb)
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cv2.imshow('Source Frame', frame)
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# [Key Change] Convert the result from RGB to BGR before displaying
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result_bgr = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
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# Display the resulting frame
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cv2.imshow('Live Portrait', result_bgr)
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# Press 'q' to exit the loop
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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# When everything is done, release the capture
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cap.release()
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cv2.destroyAllWindows()
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# live_portrait_pipeline.execute_frame(result_bgr)
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if __name__ == '__main__':
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st = time.time()
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main()
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print("Generation time:", (time.time() - st) * 1000)
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# 3. Reduced webcam latency 350 to 160
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# import cv2
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# import time
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# import threading
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# import numpy as np
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# import tyro
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# from src.config.argument_config import ArgumentConfig
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# from src.config.inference_config import InferenceConfig
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# from src.config.crop_config import CropConfig
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# from src.live_portrait_pipeline import LivePortraitPipeline
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# def partial_fields(target_class, kwargs):
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# return target_class(**{k: v for k, v in kwargs.items() if hasattr(target_class, k)})
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# class VideoCaptureThread:
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# def __init__(self, src=0):
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# self.cap = cv2.VideoCapture(src)
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# self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 480)
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# self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
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# self.cap.set(cv2.CAP_PROP_FPS, 60)
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# if not self.cap.isOpened():
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# print("Failed to open camera")
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# self.running = False
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# else:
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# self.ret = False
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# self.frame = None
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# self.running = True
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# self.thread = threading.Thread(target=self.update, args=())
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# self.thread.start()
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# def update(self):
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# while self.running:
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# self.ret, self.frame = self.cap.read()
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# if not self.ret:
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# print("Failed to read frame")
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# break
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# def read(self):
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# return self.ret, self.frame
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# def release(self):
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# self.running = False
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# self.thread.join()
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# self.cap.release()
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# def main():
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# # Set tyro theme
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# tyro.extras.set_accent_color("bright_cyan")
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# args = tyro.cli(ArgumentConfig)
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# # Specify configs for inference
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# inference_cfg = partial_fields(InferenceConfig, args.__dict__)
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# crop_cfg = partial_fields(CropConfig, args.__dict__)
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# live_portrait_pipeline = LivePortraitPipeline(
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# inference_cfg=inference_cfg,
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# crop_cfg=crop_cfg
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# )
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# # Initialize webcam 'assets/examples/driving/d6.mp4'
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# cap_thread = VideoCaptureThread(0)
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# # Wait for the first frame to be captured
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# while not cap_thread.ret and cap_thread.running:
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# time.sleep(0.1)
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# if not cap_thread.ret:
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# print("Failed to capture image")
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# cap_thread.release()
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# return
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# source_image_path = args.source_image # Set the source image path here
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# ret, frame = cap_thread.read()
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# x_s, f_s, R_s, x_s_info, lip_delta_before_animation, crop_info, img_rgb = live_portrait_pipeline.execute_frame(frame, source_image_path)
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# while cap_thread.running:
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# # Capture frame-by-frame
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# ret, frame = cap_thread.read()
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# if not ret:
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# break
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# # Process the frame
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# result = live_portrait_pipeline.generate_frame(x_s, f_s, R_s, x_s_info, lip_delta_before_animation, crop_info, img_rgb, frame)
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# # cv2.imshow('img_rgb Image', img_rgb)
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# cv2.imshow('Webcam Frame', frame)
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# # Convert the result from RGB to BGR before displaying
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# result_bgr = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
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# # Display the resulting frame
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# cv2.imshow('Webcam Live Portrait', result_bgr)
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# # Press 'q' to exit the loop
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# if cv2.waitKey(1) & 0xFF == ord('q'):
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# break
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# # When everything is done, release the capture
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# cap_thread.release()
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# cv2.destroyAllWindows()
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# if __name__ == '__main__':
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# st = time.time()
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# main()
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# print("Generation time:", (time.time() - st) * 1000)
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@ -1,13 +1,7 @@
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# coding: utf-8
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"""
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Pipeline of LivePortrait
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"""
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# TODO:
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# 1. 当前假定所有的模板都是已经裁好的，需要修改下
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# 2. pick样例图 source + driving
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import cv2
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import numpy as np
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import pickle
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@ -38,153 +32,67 @@ class LivePortraitPipeline(object):
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self.live_portrait_wrapper: LivePortraitWrapper = LivePortraitWrapper(cfg=inference_cfg)
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self.cropper = Cropper(crop_cfg=crop_cfg)
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def execute(self, args: ArgumentConfig):
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inference_cfg = self.live_portrait_wrapper.cfg # for convenience
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######## process source portrait ########
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img_rgb = load_image_rgb(args.source_image)
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def execute_frame(self, frame, source_image_path):
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inference_cfg = self.live_portrait_wrapper.cfg # for convenience
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# Load and preprocess source image
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img_rgb = load_image_rgb(source_image_path)
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img_rgb = resize_to_limit(img_rgb, inference_cfg.ref_max_shape, inference_cfg.ref_shape_n)
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log(f"Load source image from {args.source_image}")
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log(f"Load source image from {source_image_path}")
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crop_info = self.cropper.crop_single_image(img_rgb)
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source_lmk = crop_info['lmk_crop']
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img_crop, img_crop_256x256 = crop_info['img_crop'], crop_info['img_crop_256x256']
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if inference_cfg.flag_do_crop:
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I_s = self.live_portrait_wrapper.prepare_source(img_crop_256x256)
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else:
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I_s = self.live_portrait_wrapper.prepare_source(img_rgb)
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x_s_info = self.live_portrait_wrapper.get_kp_info(I_s)
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x_c_s = x_s_info['kp']
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R_s = get_rotation_matrix(x_s_info['pitch'], x_s_info['yaw'], x_s_info['roll'])
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f_s = self.live_portrait_wrapper.extract_feature_3d(I_s)
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x_s = self.live_portrait_wrapper.transform_keypoint(x_s_info)
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lip_delta_before_animation = None
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if inference_cfg.flag_lip_zero:
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# let lip-open scalar to be 0 at first
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c_d_lip_before_animation = [0.]
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combined_lip_ratio_tensor_before_animation = self.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_before_animation, source_lmk)
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combined_lip_ratio_tensor_before_animation = self.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_before_animation, crop_info['lmk_crop'])
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if combined_lip_ratio_tensor_before_animation[0][0] < inference_cfg.lip_zero_threshold:
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inference_cfg.flag_lip_zero = False
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else:
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lip_delta_before_animation = self.live_portrait_wrapper.retarget_lip(x_s, combined_lip_ratio_tensor_before_animation)
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############################################
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######## process driving info ########
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if is_video(args.driving_info):
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log(f"Load from video file (mp4 mov avi etc...): {args.driving_info}")
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# TODO: 这里track一下驱动视频 -> 构建模板
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driving_rgb_lst = load_driving_info(args.driving_info)
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driving_rgb_lst_256 = [cv2.resize(_, (256, 256)) for _ in driving_rgb_lst]
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I_d_lst = self.live_portrait_wrapper.prepare_driving_videos(driving_rgb_lst_256)
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n_frames = I_d_lst.shape[0]
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if inference_cfg.flag_eye_retargeting or inference_cfg.flag_lip_retargeting:
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driving_lmk_lst = self.cropper.get_retargeting_lmk_info(driving_rgb_lst)
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input_eye_ratio_lst, input_lip_ratio_lst = self.live_portrait_wrapper.calc_retargeting_ratio(source_lmk, driving_lmk_lst)
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elif is_template(args.driving_info):
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log(f"Load from video templates {args.driving_info}")
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with open(args.driving_info, 'rb') as f:
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template_lst, driving_lmk_lst = pickle.load(f)
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n_frames = template_lst[0]['n_frames']
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input_eye_ratio_lst, input_lip_ratio_lst = self.live_portrait_wrapper.calc_retargeting_ratio(source_lmk, driving_lmk_lst)
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else:
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raise Exception("Unsupported driving types!")
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#########################################
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return x_s, f_s, R_s, x_s_info, lip_delta_before_animation, crop_info, img_rgb
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def generate_frame(self, x_s, f_s, R_s, x_s_info, lip_delta_before_animation, crop_info, img_rgb, driving_info):
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inference_cfg = self.live_portrait_wrapper.cfg # for convenience
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# Process driving info
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driving_rgb = cv2.resize(driving_info, (256, 256))
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I_d_i = self.live_portrait_wrapper.prepare_driving_videos([driving_rgb])[0]
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x_d_i_info = self.live_portrait_wrapper.get_kp_info(I_d_i)
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R_d_i = get_rotation_matrix(x_d_i_info['pitch'], x_d_i_info['yaw'], x_d_i_info['roll'])
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R_new = R_d_i @ R_s
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delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - x_s_info['exp'])
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scale_new = x_s_info['scale'] * (x_d_i_info['scale'] / x_s_info['scale'])
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t_new = x_s_info['t'] + (x_d_i_info['t'] - x_s_info['t'])
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t_new[..., 2].fill_(0) # zero tz
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x_d_i_new = scale_new * (x_s @ R_new + delta_new) + t_new
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if inference_cfg.flag_lip_zero and lip_delta_before_animation is not None:
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x_d_i_new += lip_delta_before_animation.reshape(-1, x_s.shape[1], 3)
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out = self.live_portrait_wrapper.warp_decode(f_s, x_s, x_d_i_new)
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I_p_i = self.live_portrait_wrapper.parse_output(out['out'])[0]
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######## prepare for pasteback ########
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if inference_cfg.flag_pasteback:
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mask_ori = prepare_paste_back(inference_cfg.mask_crop, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0]))
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I_p_paste_lst = []
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#########################################
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I_p_lst = []
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R_d_0, x_d_0_info = None, None
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for i in track(range(n_frames), description='Animating...', total=n_frames):
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if is_video(args.driving_info):
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# extract kp info by M
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I_d_i = I_d_lst[i]
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x_d_i_info = self.live_portrait_wrapper.get_kp_info(I_d_i)
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R_d_i = get_rotation_matrix(x_d_i_info['pitch'], x_d_i_info['yaw'], x_d_i_info['roll'])
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else:
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# from template
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x_d_i_info = template_lst[i]
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x_d_i_info = dct2cuda(x_d_i_info, inference_cfg.device_id)
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R_d_i = x_d_i_info['R_d']
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if i == 0:
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R_d_0 = R_d_i
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x_d_0_info = x_d_i_info
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if inference_cfg.flag_relative:
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R_new = (R_d_i @ R_d_0.permute(0, 2, 1)) @ R_s
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delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp'])
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scale_new = x_s_info['scale'] * (x_d_i_info['scale'] / x_d_0_info['scale'])
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t_new = x_s_info['t'] + (x_d_i_info['t'] - x_d_0_info['t'])
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else:
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R_new = R_d_i
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delta_new = x_d_i_info['exp']
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scale_new = x_s_info['scale']
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t_new = x_d_i_info['t']
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t_new[..., 2].fill_(0) # zero tz
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x_d_i_new = scale_new * (x_c_s @ R_new + delta_new) + t_new
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# Algorithm 1:
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if not inference_cfg.flag_stitching and not inference_cfg.flag_eye_retargeting and not inference_cfg.flag_lip_retargeting:
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# without stitching or retargeting
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if inference_cfg.flag_lip_zero:
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x_d_i_new += lip_delta_before_animation.reshape(-1, x_s.shape[1], 3)
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else:
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pass
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elif inference_cfg.flag_stitching and not inference_cfg.flag_eye_retargeting and not inference_cfg.flag_lip_retargeting:
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# with stitching and without retargeting
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if inference_cfg.flag_lip_zero:
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x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new) + lip_delta_before_animation.reshape(-1, x_s.shape[1], 3)
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else:
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x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new)
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else:
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eyes_delta, lip_delta = None, None
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if inference_cfg.flag_eye_retargeting:
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c_d_eyes_i = input_eye_ratio_lst[i]
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combined_eye_ratio_tensor = self.live_portrait_wrapper.calc_combined_eye_ratio(c_d_eyes_i, source_lmk)
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# ∆_eyes,i = R_eyes(x_s; c_s,eyes, c_d,eyes,i)
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eyes_delta = self.live_portrait_wrapper.retarget_eye(x_s, combined_eye_ratio_tensor)
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if inference_cfg.flag_lip_retargeting:
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c_d_lip_i = input_lip_ratio_lst[i]
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combined_lip_ratio_tensor = self.live_portrait_wrapper.calc_combined_lip_ratio(c_d_lip_i, source_lmk)
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# ∆_lip,i = R_lip(x_s; c_s,lip, c_d,lip,i)
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lip_delta = self.live_portrait_wrapper.retarget_lip(x_s, combined_lip_ratio_tensor)
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if inference_cfg.flag_relative: # use x_s
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x_d_i_new = x_s + \
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(eyes_delta.reshape(-1, x_s.shape[1], 3) if eyes_delta is not None else 0) + \
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(lip_delta.reshape(-1, x_s.shape[1], 3) if lip_delta is not None else 0)
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else: # use x_d,i
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x_d_i_new = x_d_i_new + \
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(eyes_delta.reshape(-1, x_s.shape[1], 3) if eyes_delta is not None else 0) + \
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(lip_delta.reshape(-1, x_s.shape[1], 3) if lip_delta is not None else 0)
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if inference_cfg.flag_stitching:
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x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new)
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out = self.live_portrait_wrapper.warp_decode(f_s, x_s, x_d_i_new)
|
||||
I_p_i = self.live_portrait_wrapper.parse_output(out['out'])[0]
|
||||
I_p_lst.append(I_p_i)
|
||||
|
||||
if inference_cfg.flag_pasteback:
|
||||
I_p_i_to_ori_blend = paste_back(I_p_i, crop_info['M_c2o'], img_rgb, mask_ori)
|
||||
I_p_paste_lst.append(I_p_i_to_ori_blend)
|
||||
|
||||
mkdir(args.output_dir)
|
||||
wfp_concat = None
|
||||
if is_video(args.driving_info):
|
||||
frames_concatenated = concat_frames(I_p_lst, driving_rgb_lst, img_crop_256x256)
|
||||
# save (driving frames, source image, drived frames) result
|
||||
wfp_concat = osp.join(args.output_dir, f'{basename(args.source_image)}--{basename(args.driving_info)}_concat.mp4')
|
||||
images2video(frames_concatenated, wfp=wfp_concat)
|
||||
|
||||
# save drived result
|
||||
wfp = osp.join(args.output_dir, f'{basename(args.source_image)}--{basename(args.driving_info)}.mp4')
|
||||
if inference_cfg.flag_pasteback:
|
||||
images2video(I_p_paste_lst, wfp=wfp)
|
||||
I_p_i_to_ori_blend = paste_back(I_p_i, crop_info['M_c2o'], img_rgb, mask_ori)
|
||||
return I_p_i_to_ori_blend
|
||||
else:
|
||||
images2video(I_p_lst, wfp=wfp)
|
||||
return I_p_i
|
||||
|
||||
return wfp, wfp_concat
|
||||
|
||||
Loading…
Reference in New Issue
Block a user