app.py

import numpy as np
import cv2
from PIL import Image, ImageDraw
import mediapipe as mp
from transformers import pipeline
from skimage.measure import label, regionprops
import gradio as gr
import torch
import diffusers
import tqdm as notebook_tqdm
from diffusers import StableDiffusionInpaintPipeline
from diffusers import StableDiffusion3Pipeline
import cv2
import math
import gradio as gr
import numpy as np
import os
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from mediapipe.tasks.python.components import containers
from skimage.measure import label, regionprops
import numpy as np
import matplotlib.pyplot as plt
import cv2
from skimage.measure import label
from skimage.measure import regionprops
from PIL import Image
import torch
import requests
import tensorflow as tf


def _normalized_to_pixel_coordinates(
    normalized_x: float, normalized_y: float, image_width: int,
    image_height: int):
  """Converts normalized value pair to pixel coordinates."""

  # Checks if the float value is between 0 and 1.
  def is_valid_normalized_value(value: float) -> bool:
    return (value > 0 or math.isclose(0, value)) and (value < 1 or
                                                      math.isclose(1, value))

  if not (is_valid_normalized_value(normalized_x) and
          is_valid_normalized_value(normalized_y)):
    # TODO: Draw coordinates even if it's outside of the image bounds.
    return None
  x_px = min(math.floor(normalized_x * image_width), image_width - 1)
  y_px = min(math.floor(normalized_y * image_height), image_height - 1)
  return x_px, y_px

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

pipe = StableDiffusionInpaintPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-inpainting",
    torch_dtype=torch.float16,
).to(device)


BG_COLOR = (192, 192, 192) # gray
MASK_COLOR = (255, 255, 255) # white

RegionOfInterest = vision.InteractiveSegmenterRegionOfInterest
NormalizedKeypoint = containers.keypoint.NormalizedKeypoint

# Create the options that will be used for InteractiveSegmenter
base_options = python.BaseOptions(model_asset_path='model.tflite')
options = vision.ImageSegmenterOptions(base_options=base_options,
                                       output_category_mask=True)

def get_bounding_box(mask):
    """Generate bounding box coordinates from a binary mask."""
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    if contours:
        x, y, w, h = cv2.boundingRect(contours[0])
        return x, y, x + w, y + h
    return 0, 0, mask.shape[1], mask.shape[0]


def example_segmentation_function(image_file_path, x, y):
    OVERLAY_COLOR = (255, 105, 180)  # Rose
    base_options = python.BaseOptions(model_asset_path='model.tflite')
    options = vision.ImageSegmenterOptions(base_options=base_options, output_category_mask=True)

    with python.vision.InteractiveSegmenter.create_from_options(options) as segmenter:
        image = mp.Image.create_from_file(image_file_path)
        roi = vision.InteractiveSegmenterRegionOfInterest(
            format=vision.InteractiveSegmenterRegionOfInterest.Format.KEYPOINT,
            keypoint=containers.keypoint.NormalizedKeypoint(x, y)
        )
        segmentation_result = segmenter.segment(image, roi)
        category_mask = segmentation_result.category_mask
        segmentation_mask = category_mask.numpy_view().astype(np.uint8)

        return segmentation_mask, image



def segment(image_file_name, x, y, prompt):
    OVERLAY_COLOR = (255, 105, 180)  # Rose

    # Créer le segmenteur
    with python.vision.InteractiveSegmenter.create_from_options(options) as segmenter:

        # Créer l'image MediaPipe
        image = mp.Image.create_from_file(image_file_name)

        # Récupérer les masques de catégorie pour l'image
        roi = RegionOfInterest(format=RegionOfInterest.Format.KEYPOINT,
                               keypoint=NormalizedKeypoint(x, y))
        segmentation_result = segmenter.segment(image, roi)
        category_mask = segmentation_result.category_mask
        # Trouver la boîte englobante de la région segmentée
        mask = category_mask.numpy_view().astype(np.uint8)


        # Trouver la boîte englobante de la région segmentée
        x, y, w, h = cv2.boundingRect(mask)

        # Convertir l'image BGR en RGB
        image_data = cv2.cvtColor(image.numpy_view(), cv2.COLOR_BGR2RGB)

        # Créer une image d'incrustation avec la couleur désirée (par exemple, (255, 0, 0) pour le rouge)
        overlay_image = np.zeros(image_data.shape, dtype=np.uint8)
        overlay_image[:] = OVERLAY_COLOR

        # Créer la condition à partir du tableau category_masks
        alpha = np.stack((category_mask.numpy_view(),) * 3, axis=-1) <= 0.1

        # Créer un canal alpha à partir de la condition avec l'opacité désirée (par exemple, 0.7 pour 70%)
        alpha = alpha.astype(float) * 0.5  # Réduire l'opacité à 50%

        # Fusionner l'image originale et l'image d'incrustation en fonction du canal alpha
        output_image = image_data * (1 - alpha) + overlay_image * alpha
        output_image = output_image.astype(np.uint8)

        # Dessiner un point blanc avec une bordure noire pour indiquer le point d'intérêt
        thickness, radius = 6, -1
        keypoint_px = _normalized_to_pixel_coordinates(x, y, image.width, image.height)
        cv2.circle(output_image, keypoint_px, thickness + 5, (0, 0, 0), radius)
        cv2.circle(output_image, keypoint_px, thickness, (255, 255, 255), radius)

        # Convert the mask to binary if it's not already
        binary_mask = (mask == 255).astype(np.uint8)

        # Label the regions in the mask
        labels = label(binary_mask)

        # Obtain properties of the labeled regions
        props = regionprops(labels)

        # Initialize bounding box coordinates
        minr, minc, maxr, maxc = 0, 0, 0, 0

        for prop in props:
            minr, minc, maxr, maxc = prop.bbox

            # Add a 30-pixel margin
            minr = max(0, minr - 300)
            minc = max(0, minc - 300)
            maxr = min(binary_mask.shape[0], maxr + 400)
            maxc = min(binary_mask.shape[1], maxc + 400)

        # Create a new black image
        bbox_image = np.zeros_like(binary_mask)

        # Draw the bounding box in white
        bbox_image[minr:maxr, minc:maxc] = 255

        print(bbox_image)
        plt.imshow(bbox_image)
        plt.show()


        return output_image, bbox_image


def generate(image_file_path, x, y, prompt):
    output_image, bbox_image = segment(image_file_path, x, y, prompt)

    # Check and process images
    if image_file_path is None or bbox_image is None:
        return None

    # Read image
    img = Image.open(image_file_path).convert("RGB")

    # Generate images using images and prompts
    images = pipe(prompt=prompt,
                  image=img,
                  mask_image=bbox_image,
                  generator=torch.Generator(device="cuda").manual_seed(0),
                  num_images_per_prompt=3,
                  plms=True).images

    # Create an image grid
    def image_grid(imgs, rows, cols):
        assert len(imgs) == rows*cols

        w, h = imgs[0].size
        grid = Image.new('RGB', size=(cols*w, rows*h))
        grid_w, grid_h = grid.size

        for i, img in enumerate(imgs):
            grid.paste(img, box=(i%cols*w, i//cols*h))
        return grid

    grid_image = image_grid(images, 1, 3)
    return output_image, grid_image


webapp = gr.Interface(fn=generate,
                      inputs=[
                          gr.Image(type="filepath", label="Upload an image"),
                          gr.Slider(minimum=0, maximum=1, step=0.01, label="x"),
                          gr.Slider(minimum=0, maximum=1, step=0.01, label="y"),
                          gr.Textbox(label="Prompt")],
                      outputs=[
                          gr.Image(type="pil", label="Segmented Image"),
                          gr.Image(type="pil", label="Generated Image Grid")])
webapp.launch(debug=True)