app.py

import gradio as gr
import torch
#import spaces
import json
import base64
from io import BytesIO
from transformers import SamHQModel, SamHQProcessor, SamModel, SamProcessor
import os
import pandas as pd
from utils import *
from PIL import Image

# Carga de modelos
sam_hq_model = SamHQModel.from_pretrained("syscv-community/sam-hq-vit-huge")
sam_hq_processor = SamHQProcessor.from_pretrained("syscv-community/sam-hq-vit-huge")

sam_model = SamModel.from_pretrained("facebook/sam-vit-huge")
sam_processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")

#@spaces.GPU
def predict_masks_and_scores(model, processor, raw_image, input_points=None, input_boxes=None):
    if input_boxes is not None:
        input_boxes = [input_boxes]
    inputs = processor(raw_image, input_boxes=input_boxes, input_points=input_points, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)

    masks = processor.image_processor.post_process_masks(
        outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
    )
    scores = outputs.iou_scores
    return masks, scores

def encode_pil_to_base64(pil_image):
    buffer = BytesIO()
    pil_image.save(buffer, format="PNG")
    return base64.b64encode(buffer.getvalue()).decode("utf-8")

def compare_images_points_and_masks(user_image, input_boxes, input_points):
    for example_path, example_data in example_data_map.items():
        if example_data["size"] == list(user_image.size):
            user_image = Image.open(example_data['original_image_path'])
    input_boxes = input_boxes.values.tolist()
    input_points = input_points.values.tolist()

    input_boxes = [[[int(coord) for coord in box] for box in input_boxes if any(box)]]
    input_points = [[[int(coord) for coord in point] for point in input_points if any(point)]]

    input_boxes = input_boxes if input_boxes[0] else None
    input_points = input_points if input_points[0] else None

    sam_masks, sam_scores = predict_masks_and_scores(sam_model, sam_processor, user_image, input_boxes=input_boxes, input_points=input_points)
    sam_hq_masks, sam_hq_scores = predict_masks_and_scores(sam_hq_model, sam_hq_processor, user_image, input_boxes=input_boxes, input_points=input_points)

    if input_boxes and input_points:
        img1_b64 = show_all_annotations_on_image_base64(user_image, sam_masks[0][0], sam_scores[:, 0, :], input_boxes[0], input_points[0], model_name='SAM')
        img2_b64 = show_all_annotations_on_image_base64(user_image, sam_hq_masks[0][0], sam_hq_scores[:, 0, :], input_boxes[0], input_points[0], model_name='SAM_HQ')
    elif input_boxes:
        img1_b64 = show_all_annotations_on_image_base64(user_image, sam_masks[0][0], sam_scores[:, 0, :], input_boxes[0], None, model_name='SAM')
        img2_b64 = show_all_annotations_on_image_base64(user_image, sam_hq_masks[0][0], sam_hq_scores[:, 0, :], input_boxes[0], None, model_name='SAM_HQ')
    elif input_points:
        img1_b64 = show_all_annotations_on_image_base64(user_image, sam_masks[0][0], sam_scores[:, 0, :], None, input_points[0], model_name='SAM')
        img2_b64 = show_all_annotations_on_image_base64(user_image, sam_hq_masks[0][0], sam_hq_scores[:, 0, :], None, input_points[0], model_name='SAM_HQ')

    print('user_image', user_image)
    print("img1_b64", img1_b64)
    print("img2_b64", img2_b64)

    html_code = f"""
    <div style="position: relative; width: 100%; max-width: 600px; margin: 0 auto;" id="imageCompareContainer">
        <div style="position: relative; width: 100%;">
            <img src="data:image/png;base64,{img1_b64}" style="width:100%; display:block;">
            <div id="topWrapper" style="position:absolute; top:0; left:0; width:100%; overflow:hidden;">
                <img id="topImage" src="data:image/png;base64,{img2_b64}" style="width:100%;">
            </div>
            <div id="sliderLine" style="position:absolute; top:0; left:0; width:2px; height:100%; background-color:red; pointer-events:none;"></div>
        </div>
        <input type="range" min="0" max="100" value="0" 
            style="width:100%; margin-top: 10px;" 
            oninput="
            const val = this.value;
            const container = document.getElementById('imageCompareContainer');
            const width = container.offsetWidth;
            const clipValue = 100 - val;
            document.getElementById('topImage').style.clipPath = 'inset(0 ' + clipValue + '% 0 0)';
            document.getElementById('sliderLine').style.left = (width * val / 100) + 'px';
            ">
    </div>
    """
    return html_code

def load_examples(json_file="examples.json"):
    with open(json_file, "r") as f:
        examples = json.load(f)
    return examples

examples = load_examples()
example_paths = [example["image_path"] for example in examples]
example_data_map = {
    example["image_path"]: {
        "original_image_path": example["original_image_path"],
        "points": example["points"],
        "boxes": example["boxes"],
        "size": example["size"]
    }
    for example in examples
}

theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="emerald")
with gr.Blocks(theme=theme, title="🔍 Compare SAM vs SAM-HQ") as demo:
    image_path_box = gr.Textbox(visible=False)
    gr.Markdown("## 🔍 Compare SAM vs SAM-HQ")
    gr.Markdown("Compare the performance of SAM and SAM-HQ on various images. Click on an example to load it")
    gr.Markdown("[SAM-HQ](https://huggingface.co/syscv-community/sam-hq-vit-huge) - [SAM](https://huggingface.co/facebook/sam-vit-huge)")

    with gr.Row():
        image_input = gr.Image(
            type="pil",
            label="Example image (click below to load)",
            interactive=False,
            height=500,
            show_label=True
        )
    
    gr.Examples(
        examples=example_paths,
        inputs=[image_input],
        label="Click an example to try 👇",
    )

    result_html = gr.HTML(elem_id="result-html")

    with gr.Row():
        points_input = gr.Dataframe(
            headers=["x", "y"],
            label="Points",
            datatype=["number", "number"],
            col_count=(2, "fixed")
        )
        boxes_input = gr.Dataframe(
            headers=["x0", "y0", "x1", "y1"],
            label="Boxes",
            datatype=["number", "number", "number", "number"],
            col_count=(4, "fixed")
        )

    def on_image_change(image):
        for example_path, example_data in example_data_map.items():
            print(image.size)
            if example_data["size"] == list(image.size):
                return example_data["points"], example_data["boxes"]
        return [], []

    image_input.change(
        fn=on_image_change,
        inputs=[image_input],
        outputs=[points_input, boxes_input]
    )

    compare_button = gr.Button("Compare points and masks")
    compare_button.click(fn=compare_images_points_and_masks, inputs=[image_input, boxes_input, points_input], outputs=result_html)

    gr.HTML("""
        <style>
        #result-html {
            min-height: 500px;
            border: 1px solid #ccc;
            padding: 10px;
            box-sizing: border-box;
            background-color: #fff;
            border-radius: 8px;
            box-shadow: 0 2px 6px rgba(0, 0, 0, 0.1);
        }
        </style>
    """)

demo.launch()