| import gradio as gr | |
| import random | |
| import time | |
| import os | |
| from glob import glob | |
| from PIL import Image | |
| import torchvision.transforms as transforms | |
| num_rank = 200 | |
| image_prefix = "/deep/u/eprakash/AngioSeg/diffusion/lung_seg_synthetic_60/synth/" | |
| mask_prefix = "/deep/u/eprakash/AngioSeg/diffusion/lung_seg_synthetic_60/orig/" | |
| image_ids = [] | |
| img_list = "/deep/u/eprakash/lung_seg/train_60.csv" | |
| with open(img_list) as fp: | |
| for line in fp: | |
| image_ids.append("('" + line.strip().split(",")[0] + "',)") | |
| image_ids = image_ids[301:501] | |
| save_path = "lung_seg_ranks" | |
| def is_int(s): | |
| try: | |
| int(s) | |
| return True | |
| except ValueError: | |
| return False | |
| def load_img(img_path, size=512): | |
| img = Image.open(img_path).convert('RGB') | |
| transform_list = [transforms.Resize((size, size))] | |
| transform = transforms.Compose(transform_list) | |
| img = transform(img) | |
| return img | |
| def find_completed_idxs(save_path=save_path): | |
| files = os.listdir(save_path) | |
| incorrect_files = [] | |
| if len(files) == 0: | |
| return [-1], [] | |
| else: | |
| file_list = [] | |
| for f in files: | |
| f_name = int(f.split(".")[0]) | |
| with open(save_path + "/" + f) as fp: | |
| for line in fp: | |
| items = line.strip().split(",") | |
| if (len(items) != 5 and f_name != -1): | |
| incorrect_files.append(f_name) | |
| else: | |
| if ((not is_int(items[1].strip()) or not is_int(items[2].strip()) or not is_int(items[3].strip()) or not is_int(items[4].strip())) and f_name != -1): | |
| incorrect_files.append(f_name) | |
| file_list.append(f_name) | |
| file_list = sorted(file_list) | |
| incorrect_files = sorted(incorrect_files) | |
| return file_list, incorrect_files | |
| def load_next(rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example, ids=image_ids, image_prefix=image_prefix, save_path=save_path): | |
| file_list, incorrect_files = find_completed_idxs() | |
| print(str(file_list) + " " + str(incorrect_files)) | |
| if (int(example) not in file_list or int(example) in incorrect_files): | |
| r = str(image_ids[int(example)]).split(",")[0].split("(")[1] + "," + rank | |
| r_fp = open(save_path + "/" + str(int(example)) +".txt", "w") | |
| r_fp.write(r + "\n") | |
| r_fp.close() | |
| file_list, incorrect_files = find_completed_idxs() | |
| if (len(incorrect_files) != 0): | |
| example = incorrect_files[-1] | |
| else: | |
| example = file_list[-1] + 1 | |
| if int(example) == num_rank: | |
| rank = "DONE!" | |
| example = -1 | |
| mask_1 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| img_1 = gr.Image(label="Sample #1", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| mask_2 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| img_2 = gr.Image(label="Sample #2", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| mask_3 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| img_3 = gr.Image(label="Sample #3", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| mask_4 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| img_4 = gr.Image(label="Sample #4", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| else: | |
| rank = "" | |
| img_1 = gr.Image(label="Sample #1", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_0.png"), interactive=False) | |
| mask_1 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False) | |
| img_2 = gr.Image(label="Sample #2", value=load_img(image_prefix+ str(image_ids[int(example)]) + "_synthetic_1.png"), interactive=False) | |
| mask_2 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False) | |
| img_3 = gr.Image(label="Sample #3", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_2.png"), interactive=False) | |
| mask_3 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False) | |
| img_4 = gr.Image(label="Sample #4", value=load_img(image_prefix + str(image_ids[int(example)]) + "_synthetic_3.png"), interactive=False) | |
| mask_4 = gr.Image(label="Mask", value=load_img(mask_prefix + str(image_ids[int(example)]) + "_mask.png"), interactive=False) | |
| return [rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example] | |
| with gr.Blocks() as demo: | |
| last_idx = -1 | |
| example = gr.Number(label="Example #. Click next for #-1 (blank starting page).", value=last_idx, interactive=False) | |
| rank = gr.Textbox(label="Rankings (Best to worst, comma-separated, no spaces).") | |
| with gr.Column(scale=1): | |
| with gr.Row(): | |
| mask_1 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| img_1 = gr.Image(label="Sample #1", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| with gr.Row(): | |
| mask_2 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| img_2 = gr.Image(label="Sample #2", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| with gr.Row(): | |
| mask_3 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| img_3 = gr.Image(label="Sample #3", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| with gr.Row(): | |
| mask_4 = gr.Image(label="Mask", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| img_4 = gr.Image(label="Sample #4", value=load_img("/deep/u/eprakash/blank.jpg"), interactive=False) | |
| next_btn = gr.Button(value="Next") | |
| next_btn.click(fn=load_next, inputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], outputs=[rank, img_1, mask_1, img_2, mask_2, img_3, mask_3, img_4, mask_4, example], queue=False) | |
| demo.queue() | |
| demo.launch(share=True) | |