""" Inference on webcams: Use a model on webcam input. Once launched, the script is in background collection mode. Press B to toggle between background capture mode and matting mode. The frame shown when B is pressed is used as background for matting. Press Q to exit. Example: python inference_webcam.py \ --model-type mattingrefine \ --model-backbone resnet50 \ --model-checkpoint "PATH_TO_CHECKPOINT" \ --resolution 1280 720 """ import argparse, os, shutil, time import cv2 import torch from torch import nn from torch.utils.data import DataLoader from torchvision.transforms import Compose, ToTensor, Resize from torchvision.transforms.functional import to_pil_image from threading import Thread, Lock from tqdm import tqdm from PIL import Image from dataset import VideoDataset from model import MattingBase, MattingRefine # --------------- Arguments --------------- parser = argparse.ArgumentParser(description='Inference from web-cam') parser.add_argument('--model-type', type=str, required=True, choices=['mattingbase', 'mattingrefine']) parser.add_argument('--model-backbone', type=str, required=True, choices=['resnet101', 'resnet50', 'mobilenetv2']) parser.add_argument('--model-backbone-scale', type=float, default=0.25) parser.add_argument('--model-checkpoint', type=str, required=True) parser.add_argument('--model-refine-mode', type=str, default='sampling', choices=['full', 'sampling', 'thresholding']) parser.add_argument('--model-refine-sample-pixels', type=int, default=80_000) parser.add_argument('--model-refine-threshold', type=float, default=0.7) parser.add_argument('--hide-fps', action='store_true') parser.add_argument('--resolution', type=int, nargs=2, metavar=('width', 'height'), default=(1280, 720)) args = parser.parse_args() # ----------- Utility classes ------------- # A wrapper that reads data from cv2.VideoCapture in its own thread to optimize. # Use .read() in a tight loop to get the newest frame class Camera: def __init__(self, device_id=0, width=1280, height=720): self.capture = cv2.VideoCapture(device_id) self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, width) self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, height) self.width = int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH)) self.height = int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) # self.capture.set(cv2.CAP_PROP_BUFFERSIZE, 2) self.success_reading, self.frame = self.capture.read() self.read_lock = Lock() self.thread = Thread(target=self.__update, args=()) self.thread.daemon = True self.thread.start() def __update(self): while self.success_reading: grabbed, frame = self.capture.read() with self.read_lock: self.success_reading = grabbed self.frame = frame def read(self): with self.read_lock: frame = self.frame.copy() return frame def __exit__(self, exec_type, exc_value, traceback): self.capture.release() # An FPS tracker that computes exponentialy moving average FPS class FPSTracker: def __init__(self, ratio=0.5): self._last_tick = None self._avg_fps = None self.ratio = ratio def tick(self): if self._last_tick is None: self._last_tick = time.time() return None t_new = time.time() fps_sample = 1.0 / (t_new - self._last_tick) self._avg_fps = self.ratio * fps_sample + (1 - self.ratio) * self._avg_fps if self._avg_fps is not None else fps_sample self._last_tick = t_new return self.get() def get(self): return self._avg_fps # Wrapper for playing a stream with cv2.imshow(). It can accept an image and return keypress info for basic interactivity. # It also tracks FPS and optionally overlays info onto the stream. class Displayer: def __init__(self, title, width=None, height=None, show_info=True): self.title, self.width, self.height = title, width, height self.show_info = show_info self.fps_tracker = FPSTracker() cv2.namedWindow(self.title, cv2.WINDOW_NORMAL) if width is not None and height is not None: cv2.resizeWindow(self.title, width, height) # Update the currently showing frame and return key press char code def step(self, image): fps_estimate = self.fps_tracker.tick() if self.show_info and fps_estimate is not None: message = f"{int(fps_estimate)} fps | {self.width}x{self.height}" cv2.putText(image, message, (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0)) cv2.imshow(self.title, image) return cv2.waitKey(1) & 0xFF # --------------- Main --------------- # Load model if args.model_type == 'mattingbase': model = MattingBase(args.model_backbone) if args.model_type == 'mattingrefine': model = MattingRefine( args.model_backbone, args.model_backbone_scale, args.model_refine_mode, args.model_refine_sample_pixels, args.model_refine_threshold) model = model.cuda().eval() model.load_state_dict(torch.load(args.model_checkpoint), strict=False) width, height = args.resolution cam = Camera(width=width, height=height) dsp = Displayer('MattingV2', cam.width, cam.height, show_info=(not args.hide_fps)) def cv2_frame_to_cuda(frame): frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) return ToTensor()(Image.fromarray(frame)).unsqueeze_(0).cuda() with torch.no_grad(): while True: bgr = None while True: # grab bgr frame = cam.read() key = dsp.step(frame) if key == ord('b'): bgr = cv2_frame_to_cuda(cam.read()) break elif key == ord('q'): exit() while True: # matting frame = cam.read() src = cv2_frame_to_cuda(frame) pha, fgr = model(src, bgr)[:2] res = pha * fgr + (1 - pha) * torch.ones_like(fgr) res = res.mul(255).byte().cpu().permute(0, 2, 3, 1).numpy()[0] res = cv2.cvtColor(res, cv2.COLOR_RGB2BGR) key = dsp.step(res) if key == ord('b'): break elif key == ord('q'): exit()