if not osp.exists(out_dir1): os.mkdir(out_dir1) PIL.Image.fromarray...(img).save(out_dir1 + '\\' + save_file_name + '_img.png') PIL.Image.fromarray(lbl).save...(out_dir1 + '\\' + save_file_name + '_label.png') PIL.Image.fromarray(lbl_viz).save(...(img).save(osp.join(images_dir, '{}_img.png'.format(save_file_name))) PIL.Image.fromarray...polygons_to_mask(img_shape, polygons): mask = np.zeros(img_shape[:2], dtype=np.uint8) mask = PIL.Image.fromarray
maskvizdir = json_file + 'mask_viz' out_dir1 = maskdir PIL.Image.fromarray...(lbl).save(out_dir1 + '/' + save_file_name + '.png') PIL.Image.fromarray(lbl_viz).save(maskvizdir
') out_dir = osp.join(osp.dirname(count[i]), out_dir) if not osp.exists(out_dir): os.mkdir(out_dir) PIL.Image.fromarray...(img).save(osp.join(out_dir, 'img.png')) #PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label.png'...)) utils.lblsave(osp.join(out_dir, 'label.png'), lbl) PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir
value] = name lbl_viz = imgviz.label2rgb( lbl, imgviz.asgray(img), label_names=label_names, loc="rb" ) PIL.Image.fromarray...(img).save(osp.join(out_dir, "img.png")) utils.lblsave(osp.join(out_dir, "label.png"), lbl) PIL.Image.fromarray...value] = name lbl_viz = imgviz.label2rgb( lbl, imgviz.asgray(img), label_names=label_names, loc="rb" ) PIL.Image.fromarray...(img).save(osp.join(out_dir, "img.png")) utils.lblsave(osp.join(out_dir, "label.png"), lbl) PIL.Image.fromarray
0.225]) ]) src_img = cv2.imread("demo.png") print("src img shape: ",src_img.shape) pil_img = PIL.Image.fromarray
torchvision.transforms.functional.to_pil_image(tensor) 7、np.ndarray和PIL.Image进行转换 np.ndarray转换为PIL.Image image = PIL.Image.fromarray
# torch.Tensor -> PIL.Image. image = PIL.Image.fromarray(torch.clamp(tensor * 255, min=0, max=255...PIL.Image.open(path)) # Equivalently way np.ndarray 与 PIL.Image 转换 # np.ndarray -> PIL.Image. image = PIL.Image.fromarray
# torch.Tensor -> PIL.Image. image = PIL.Image.fromarray(torch.clamp(tensor * 255, min=0, max=255 ...PIL.Image.open(path)) # Equivalently way np.ndarray 与 PIL.Image 转换 # np.ndarray -> PIL.Image. image = PIL.Image.fromarray
= struct.pack('>H', tagcode).decode('gb2312') """ # 提取点图像, 看看什么样 if train_counter < 1000: im = PIL.Image.fromarray
image_processed + 1.0) * 127.5 image_processed = image_processed.numpy().astype(np.uint8) image_pil = PIL.Image.fromarray
0.0, 1.0) img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0) img = PIL.Image.fromarray
# torch.Tensor -> PIL.Image. image = PIL.Image.fromarray(torch.clamp(tensor * 255, min=0, max=255...PIL.Image.open(path)) # Equivalently way np.ndarray与PIL.Image转换 # np.ndarray -> PIL.Image. image = PIL.Image.fromarray
PIL.Image转换# pytorch中的张量默认采用[N, C, H, W]的顺序，并且数据范围在[0,1]，需要进行转置和规范化# torch.Tensor -> PIL.Imageimage = PIL.Image.fromarray...torchvision.transforms.functional.to_tensor(PIL.Image.open(path)) # Equivalently waynp.ndarray与PIL.Image的转换image = PIL.Image.fromarray
PIL.Image转换 # pytorch中的张量默认采用[N, C, H, W]的顺序，并且数据范围在[0,1]，需要进行转置和规范化 # torch.Tensor -> PIL.Image image = PIL.Image.fromarray...torchvision.transforms.functional.to_tensor(PIL.Image.open(path)) # Equivalently way np.ndarray与PIL.Image的转换 image = PIL.Image.fromarray
dtype=np.uint8) if np.ndim(tensor)>3: assert tensor.shape[0] == 1 tensor = tensor[0] return PIL.Image.fromarray
config.result_dir, exist_ok=True) png_filename = os.path.join(config.result_dir, f’/content/example1.png’) PIL.Image.fromarray
合并图像并保存 im_vis = numpy.concatenate((img, pred_color), axis=1) # 将原始图像和彩色编码的分割结果并排放置 display(PIL.Image.fromarray
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