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generate_webcam.py
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generate_webcam.py
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import argparse
import os
from custom_transforms import *
from PIL import Image
import numpy as np
import torch.utils.data
import torch
import cv2
# Main to generate images
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", help="checkpoint location", required=True)
parser.add_argument("--device", help="device", required=True)
parser.add_argument("--resolution", type=int, nargs=2, metavar=('width', 'height'), default=(480, 640))
parser.add_argument("--show_original", type=int, default=0)
parser.add_argument("--resize", type=int, default=256)
args = parser.parse_args()
generator = (torch.load(args.checkpoint, map_location=lambda storage, loc: storage))
generator.eval()
device = args.device
print("device: " + device, flush=True)
generator = generator.to(device)
if device.lower() != "cpu":
generator = generator.type(torch.half)
transform = build_transform()
cap = cv2.VideoCapture(0)
width, height = args.resolution
cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
while True:
ret, frame = cap.read()
if not ret:
cap.release()
cv2.destroyAllWindows()
exit()
x = int(frame.shape[0] / 2)
y = int(frame.shape[1] / 2)
res = min(x, y)
frame = frame[x-res:x+res, y-res:y+res, :]
frame_resized = cv2.resize(frame, (args.resize, args.resize))
frame_resized = Image.fromarray(cv2.cvtColor(frame_resized, cv2.COLOR_BGR2RGB)) #convert to PIL.Image for torchvision transforms
net_in = transform(frame_resized).to(args.device).unsqueeze(0)
if device.lower() != "cpu":
net_in = net_in.type(torch.half)
net_out = generator(net_in)
im = ((net_out[0].clamp(-1, 1) + 1) * 127.5).permute((1, 2, 0)).cpu().data.numpy().astype(np.uint8)
im = cv2.cvtColor(cv2.resize(im, (2*res, 2*res)), cv2.COLOR_RGB2BGR)
if args.show_original == 1:
im = np.concatenate((frame, im), axis=1)
cv2.imshow("press q to exit", im)
if cv2.waitKey(1) & 0xFF == ord('q'):
cap.release()
cv2.destroyAllWindows()
exit()