| import gradio as gr | |
| import random | |
| import time | |
| fp = open("/deep2/u/eprakash/MedSegDiff/data/ISIC/ISBI2016_ISIC_Part3B_Training_GroundTruth.csv") | |
| image_ids = [] | |
| for line in fp: | |
| image_ids.append(line.split(",")[0].split("_")[1]) | |
| image_ids = image_ids[750:] | |
| rankings = [] | |
| def load_next(rank, img_1, img_2, img_3, img_4, img_5, example, ids=image_ids): | |
| if example == len(image_ids): | |
| return [None, None, None, None, None, None, None] | |
| else: | |
| rankings.append(str(image_ids[int(example)-1]) + "," + rank) | |
| r_fp = open("ranks_3/isic_ranks_" + str(int(example) - 1) +".csv", "w") | |
| for r in rankings: | |
| r_fp.write(r + "\n") | |
| r_fp.close() | |
| example += 1 | |
| rank = "" | |
| img_1 = gr.Image(label="Sample #1", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_0.jpg", interactive=False) | |
| img_2 = gr.Image(label="Sample #2", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_1.jpg", interactive=False) | |
| img_3 = gr.Image(label="Sample #3", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_2.jpg", interactive=False) | |
| img_4 = gr.Image(label="Sample #4", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_3.jpg", interactive=False) | |
| img_5 = gr.Image(label="Sample #5", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[int(example)-1]) + "_synthetic_4.jpg", interactive=False) | |
| return [rank, img_1, img_2, img_3, img_4, img_5, example] | |
| with gr.Blocks() as demo: | |
| example = gr.Number(label="Example #", value=1, interactive=False) | |
| rank = gr.Textbox(label="Rankings (Best to worst, comma-separated, no spaces)") | |
| with gr.Row(): | |
| img_1 = gr.Image(label="Sample #1", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_0.jpg", interactive=False) | |
| img_2 = gr.Image(label="Sample #2", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_1.jpg", interactive=False) | |
| img_3 = gr.Image(label="Sample #3", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_2.jpg", interactive=False) | |
| img_4 = gr.Image(label="Sample #4", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_3.jpg", interactive=False) | |
| img_5 = gr.Image(label="Sample #5", value="/deep2/u/eprakash/Diffusion-based-Segmentation/isic_synthetic_data/" + str(image_ids[0]) + "_synthetic_4.jpg", interactive=False) | |
| next_btn = gr.Button(value="Next") | |
| next_btn.click(fn=load_next, inputs=[rank, img_1, img_2, img_3, img_4, img_5, example], outputs=[rank, img_1, img_2, img_3, img_4, img_5, example], queue=False) | |
| demo.queue() | |
| demo.launch(share=True) | |
| fp.close() | |