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predict.py
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import argparse
import os
import numpy as np
import torch
from PIL import Image
from torch import tensor
# from utils import resize_and_crop, normalize, hwc_to_chw, dense_crf
# from utils import plot_img_and_mask
from torchvision import transforms
# def predict_img(net,
# full_img,
# scale_factor=0.5,
# out_threshold=0.5,
# use_dense_crf=True,
# use_gpu=False):
#
# img_height = full_img.size[1]
# img_width = full_img.size[0]
#
# img = resize_and_crop(full_img, scale=scale_factor)
# img = normalize(img)
#
# left_square, right_square = split_img_into_squares(img)
#
# left_square = hwc_to_chw(left_square)
# right_square = hwc_to_chw(right_square)
#
# X_left = torch.from_numpy(left_square).unsqueeze(0)
# X_right = torch.from_numpy(right_square).unsqueeze(0)
#
# if use_gpu:
# X_left = X_left.cuda()
# X_right = X_right.cuda()
#
# with torch.no_grad():
# output_left = net(X_left)
# output_right = net(X_right)
#
# left_probs = torch.sigmoid(output_left).squeeze(0)
# right_probs = torch.sigmoid(output_right).squeeze(0)
#
# tf = transforms.Compose(
# [
# transforms.ToPILImage(),
# transforms.Resize(img_height),
# transforms.ToTensor()
# ]
# )
#
# left_probs = tf(left_probs.cpu())
# right_probs = tf(right_probs.cpu())
#
# left_mask_np = left_probs.squeeze().cpu().numpy()
# right_mask_np = right_probs.squeeze().cpu().numpy()
#
# full_mask = merge_masks(left_mask_np, right_mask_np, img_width)
#
# if use_dense_crf:
# full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
#
# return full_mask > out_threshold
import config
"""
input: Tensor:(N, D=3, H, W)
output: Tensor:(N, D=1, H, W) or (N, D=3, H, W) when it is black
"""
def predict(net, image):
if config.TRAIN_GPU: image = image.cuda()
if image.mean() < config.PREDICTION_DARK_THRESHOLD:
"""WARNING: Encounter Dark Image"""
return torch.zeros((image.size()[0], 1, image.size()[2], image.size()[3])).cuda()
"""Need to repeat three times because the net will automatically reduce C when the Cs are the same"""
masks_pred = net(image)
del image
if config.TRAIN_GPU: torch.cuda.empty_cache()
return masks_pred
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model', '-m', default='MODEL.pth',
metavar='FILE',
help="Specify the file in which is stored the model"
" (default : 'MODEL.pth')")
parser.add_argument('--input', '-i', metavar='INPUT', nargs='+',
help='filenames of input images', required=True)
parser.add_argument('--output', '-o', metavar='INPUT', nargs='+',
help='filenames of ouput images')
parser.add_argument('--cpu', '-c', action='store_true',
help="Do not use the cuda version of the net",
default=False)
parser.add_argument('--viz', '-v', action='store_true',
help="Visualize the images as they are processed",
default=False)
parser.add_argument('--no-save', '-n', action='store_true',
help="Do not save the output masks",
default=False)
parser.add_argument('--no-crf', '-r', action='store_true',
help="Do not use dense CRF postprocessing",
default=False)
parser.add_argument('--mask-threshold', '-t', type=float,
help="Minimum probability value to consider a mask pixel white",
default=0.5)
parser.add_argument('--scale', '-s', type=float,
help="Scale factor for the input images",
default=0.5)
return parser.parse_args()
# def get_output_filenames(args):
# in_files = args.input
# out_files = []
#
# if not args.output:
# for f in in_files:
# pathsplit = os.path.splitext(f)
# out_files.append("{}_OUT{}".format(pathsplit[0], pathsplit[1]))
# elif len(in_files) != len(args.output):
# print("Error : Input files and output files are not of the same length")
# raise SystemExit()
# else:
# out_files = args.output
#
# return out_files
#
# def mask_to_image(mask):
# return Image.fromarray((mask * 255).astype(np.uint8))
if __name__ == "__main__":
args = get_args()
# in_files = args.input
# out_files = get_output_filenames(args)
# net = UNet(n_channels=3, n_classes=1)
#
# print("Loading model {}".format(args.model))
#
# if not args.cpu:
# print("Using CUDA version of the net, prepare your GPU !")
# net.cuda()
# net.load_state_dict(torch.load(args.model))
# else:
# net.cpu()
# net.load_state_dict(torch.load(args.model, map_location='cpu'))
# print("Using CPU version of the net, this may be very slow")
#
# print("Model loaded !")
#
# for i, fn in enumerate(in_files):
# print("\nPredicting image {} ...".format(fn))
#
# img = Image.open(fn)
# if img.size[0] < img.size[1]:
# print("Error: image height larger than the width")
#
# mask = predict_img(net=net,
# full_img=img,
# scale_factor=args.scale,
# out_threshold=args.mask_threshold,
# use_dense_crf= not args.no_crf,
# use_gpu=not args.cpu)
#
# if args.viz:
# print("Visualizing results for image {}, close to continue ...".format(fn))
# plot_img_and_mask(img, mask)
#
# if not args.no_save:
# out_fn = out_files[i]
# result = mask_to_image(mask)
# result.save(out_files[i])
#
# print("Mask saved to {}".format(out_files[i]))