mirror of
https://github.com/kritiksoman/GIMP-ML
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101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
#!/usr/bin/python
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# -*- encoding: utf-8 -*-
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from logger import setup_logger
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from model import BiSeNet
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import torch
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import os
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import os.path as osp
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import numpy as np
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from PIL import Image
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import torchvision.transforms as transforms
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import cv2
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def vis_parsing_maps(im, parsing_anno, stride, save_im=False, save_path='vis_results/parsing_map_on_im.jpg'):
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# Colors for all 20 parts
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part_colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0],
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[255, 0, 85], [255, 0, 170],
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[0, 255, 0], [85, 255, 0], [170, 255, 0],
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[0, 255, 85], [0, 255, 170],
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[0, 0, 255], [85, 0, 255], [170, 0, 255],
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[0, 85, 255], [0, 170, 255],
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[255, 255, 0], [255, 255, 85], [255, 255, 170],
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[255, 0, 255], [255, 85, 255], [255, 170, 255],
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[0, 255, 255], [85, 255, 255], [170, 255, 255]]
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im = np.array(im)
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vis_im = im.copy().astype(np.uint8)
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vis_parsing_anno = parsing_anno.copy().astype(np.uint8)
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vis_parsing_anno = cv2.resize(vis_parsing_anno, None, fx=stride, fy=stride, interpolation=cv2.INTER_NEAREST)
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vis_parsing_anno_color = np.zeros((vis_parsing_anno.shape[0], vis_parsing_anno.shape[1], 3)) + 255
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num_of_class = np.max(vis_parsing_anno)
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for pi in range(1, num_of_class + 1):
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index = np.where(vis_parsing_anno == pi)
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vis_parsing_anno_color[index[0], index[1], :] = part_colors[pi]
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vis_parsing_anno_color = vis_parsing_anno_color.astype(np.uint8)
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# print(vis_parsing_anno_color.shape, vis_im.shape)
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vis_im = cv2.addWeighted(cv2.cvtColor(vis_im, cv2.COLOR_RGB2BGR), 0.4, vis_parsing_anno_color, 0.6, 0)
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# Save result or not
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if save_im:
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cv2.imwrite(save_path[:-4] +'.png', vis_parsing_anno)
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cv2.imwrite(save_path, vis_im, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
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# return vis_im
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def evaluate(respth='./res/test_res', dspth='./data', cp='model_final_diss.pth'):
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if not os.path.exists(respth):
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os.makedirs(respth)
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n_classes = 19
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net = BiSeNet(n_classes=n_classes)
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save_pth = osp.join('res/cp', cp)
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if torch.cuda.is_available():
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net.cuda()
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net.load_state_dict(torch.load(save_pth))
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else:
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net.load_state_dict(torch.load(save_pth, map_location=lambda storage, loc: storage))
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net.eval()
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to_tensor = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
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])
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with torch.no_grad():
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for image_path in os.listdir(dspth):
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img = Image.open(osp.join(dspth, image_path))
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image = img.resize((512, 512), Image.BILINEAR)
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img = to_tensor(image)
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img = torch.unsqueeze(img, 0)
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if torch.cuda.is_available():
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img = img.cuda()
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out = net(img)[0]
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if torch.cuda.is_available():
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parsing = out.squeeze(0).cpu().numpy().argmax(0)
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else:
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parsing = out.squeeze(0).numpy().argmax(0)
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# print(parsing)
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print(np.unique(parsing))
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vis_parsing_maps(image, parsing, stride=1, save_im=True, save_path=osp.join(respth, image_path))
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if __name__ == "__main__":
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evaluate(dspth='makeup/116_ori.png', cp='79999_iter.pth')
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