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inference_webcam.py
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inference_webcam.py
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"""
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()