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| from pathlib import Path | |
| import numpy as np | |
| import gradio as gr | |
| import utils.shared_utils as st | |
| import torch | |
| from torch import autocast | |
| import torchvision.transforms as T | |
| from contextlib import nullcontext | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| context = autocast if device == "cuda" else nullcontext | |
| # Apply the transformations needed | |
| def select_input(input_img,webcm_img): | |
| if input_img is None: | |
| img= webcm_img | |
| else: | |
| img=input_img | |
| return img | |
| def infer(prompt,samples): | |
| images= [] | |
| selections = ["Img_{}".format(str(i+1).zfill(2)) for i in range(samples)] | |
| with context(device): | |
| for _ in range(samples): | |
| back_img = st.stableDiffusionAPICall(prompt) | |
| images.append(back_img) | |
| return images | |
| def change_bg_option(choice): | |
| if choice == "I have an Image": | |
| return gr.Image(shape=(800, 800)) | |
| elif choice == "Generate one for me": | |
| return gr.update(lines=8, visible=True, value="Please enter a text prompt") | |
| else: | |
| return gr.update(visible=False) | |
| # TEXT | |
| title = "FSDL- One-Shot, Green-Screen, Composition-Transfer" | |
| DEFAULT_TEXT = "Photorealistic scenery of bookshelf in a room" | |
| description = """ | |
| <center><a href="https://docs.google.com/document/d/1fde8XKIMT1nNU72859ytd2c58LFBxepS3od9KFBrJbM/edit?usp=sharing">[PAPER]</a> <a href="https://github.com/snknitin/FSDL-Project/blob/main/src/utils/shared_utils.py">[CODE]</a></center> | |
| <details> | |
| <summary><b>Instructions</b></summary> | |
| <p style="margin-top: -3px;">With this app, you can generate a suitable background image to overlay your portrait!<br />You have several ways to set how your final auto-edited image will look like:<br /></p> | |
| <ul style="margin-top: -20px;margin-bottom: -15px;"> | |
| <li style="margin-bottom: -10px;margin-left: 20px;">Use the "<i>Inputs</i>" tab to either upload an image from your device or allow the use of your webcam to capture</li> | |
| <li style="margin-left: 20px;">Use the "<i>Background Image Inputs</i>" to upload your own background</li> | |
| <li style="margin-left: 20px;">Use the "<i>Text prompt</i>" tab to generate a satisfactory bacground image.</li> | |
| </ul> | |
| <p>After customization, just hit "<i>Edit</i>" and wait a few seconds.<br />The final image will be available for download <br /> <b>Enjoy!<b><p> | |
| </details> | |
| """ | |
| running = """ | |
| ### Instructions for running the 3 S's in sequence | |
| * **Superimpose** - This button allows you to isolate the foreground from your image and overlay it on the background. Remove background using alpha matting | |
| * **Style-Transfer** - This button transfer the style from your original image to re-map your new background realistically. Uses Nvidia FastPhotoStyle | |
| * **Smoothing** - Given than image resolutions and clarity can be an issue, this smoothing button makes your final image crisp after the stylization transfer. Fair warning - this last process can take 5-10 mins | |
| """ | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + title + "</h1>") | |
| with gr.Box(): | |
| gr.Markdown(description) | |
| # First row - Inputs | |
| with gr.Row(scale=1): | |
| with gr.Column(): | |
| with gr.Tabs(): | |
| with gr.TabItem("Upload "): | |
| input_img = gr.Image(shape=(800, 800), interactive=True, label="You") | |
| with gr.TabItem("Webcam Capture"): | |
| webcm_img = gr.Image(source="webcam", streaming=True, shape=(800, 800), interactive=True) | |
| inp_select_btn = gr.Button("Select") | |
| with gr.Column(): | |
| with gr.Tabs(): | |
| with gr.TabItem("Upload"): | |
| bgm_img = gr.Image(shape=(800, 800), type="pil", interactive=True, label="The Background") | |
| bgm_select_btn = gr.Button("Select") | |
| with gr.TabItem("Generate via Text Prompt"): | |
| with gr.Box(): | |
| with gr.Row().style(mobile_collapse=False, equal_height=True): | |
| text = gr.Textbox(lines=7, | |
| placeholder="Enter your prompt to generate a background image... something like - Photorealistic scenery of bookshelf in a room") | |
| samples = gr.Slider(label="Number of Images", minimum=1, maximum=5, value=2, step=1) | |
| btn = gr.Button("Generate images",variant="primary").style( | |
| margin=False, | |
| rounded=(False, True, True, False), | |
| ) | |
| gallery = gr.Gallery(label="Generated images", show_label=True).style(grid=(1, 3), height="auto") | |
| # image_options = gr.Radio(label="Pick", interactive=True, choices=None, type="value") | |
| text.submit(infer, inputs=[text, samples], outputs=gallery) | |
| btn.click(infer, inputs=[text, samples], outputs=gallery, show_progress=True, status_tracker=None) | |
| # Second Row - Backgrounds | |
| with gr.Row(scale=1): | |
| with gr.Column(): | |
| final_input_img = gr.Image(shape=(800, 800), type="pil", label="Foreground") | |
| with gr.Column(): | |
| final_back_img = gr.Image(shape=(800, 800), type="pil", label="Background", interactive=True) | |
| bgm_select_btn.click(fn=lambda x: x, inputs=bgm_img, outputs=final_back_img) | |
| inp_select_btn.click(select_input, [input_img, webcm_img], final_input_img) | |
| with gr.Row(scale=1): | |
| with gr.Box(): | |
| gr.Markdown(running) | |
| with gr.Row(scale=1): | |
| with gr.Column(scale=1): | |
| supimp_btn = gr.Button("SuperImpose") | |
| overlay_img = gr.Image(shape=(800, 800), label="Overlay", type="pil") | |
| with gr.Column(scale=1): | |
| style_btn = gr.Button("Composition-Transfer",variant="primary") | |
| style_img = gr.Image(shape=(800, 800),label="Style-Transfer Image",type="pil") | |
| with gr.Column(scale=1): | |
| submit_btn = gr.Button("Smoothen",variant="primary") | |
| output_img = gr.Image(shape=(800, 800),label="FinalSmoothened Image",type="pil") | |
| supimp_btn.click(fn=st.superimpose, inputs=[final_input_img, final_back_img], outputs=[overlay_img]) | |
| style_btn.click(fn=st.style_transfer, inputs=[overlay_img,final_input_img], outputs=[style_img]) | |
| submit_btn.click(fn=st.smoother, inputs=[style_img,overlay_img], outputs=[output_img]) | |
| demo.queue() | |
| demo.launch() | |