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| import gradio as gr | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| from lang_sam import LangSAM | |
| from color_matcher import ColorMatcher | |
| from color_matcher.normalizer import Normalizer | |
| import torch | |
| # Load the LangSAM model | |
| model = LangSAM() # Use the default model or specify custom checkpoint if necessary | |
| def extract_mask(image_pil, text_prompt): | |
| masks, boxes, phrases, logits = model.predict(image_pil, text_prompt) | |
| masks_np = masks[0].cpu().numpy() | |
| mask = (masks_np > 0).astype(np.uint8) * 255 # Binary mask | |
| return mask | |
| def apply_color_matching(source_img_np, ref_img_np): | |
| # Initialize ColorMatcher | |
| cm = ColorMatcher() | |
| # Apply color matching | |
| img_res = cm.transfer(src=source_img_np, ref=ref_img_np, method='mkl') | |
| # Normalize the result | |
| img_res = Normalizer(img_res).uint8_norm() | |
| return img_res | |
| def process_image(current_image_pil, prompt, replacement_image_pil, color_ref_image_pil, apply_replacement, apply_color_grading, blending_amount, image_history): | |
| # Check if current_image_pil is None | |
| if current_image_pil is None: | |
| return None, "No current image to edit.", image_history, None | |
| if not apply_replacement and not apply_color_grading: | |
| return current_image_pil, "No changes applied. Please select at least one operation.", image_history, current_image_pil | |
| if apply_replacement and replacement_image_pil is None: | |
| return current_image_pil, "Replacement image not provided.", image_history, current_image_pil | |
| if apply_color_grading and color_ref_image_pil is None: | |
| return current_image_pil, "Color reference image not provided.", image_history, current_image_pil | |
| # Save current image to history for undo | |
| if image_history is None: | |
| image_history = [] | |
| image_history.append(current_image_pil.copy()) | |
| # Extract mask | |
| mask = extract_mask(current_image_pil, prompt) | |
| # Check if mask is valid | |
| if mask.sum() == 0: | |
| return current_image_pil, f"No mask detected for prompt: {prompt}", image_history, current_image_pil | |
| # Proceed with replacement or color matching | |
| current_image_np = np.array(current_image_pil) | |
| result_image_np = current_image_np.copy() | |
| # Create mask with blending | |
| # First, normalize mask to range [0,1] | |
| mask_normalized = mask.astype(np.float32) / 255.0 | |
| # Apply blending by blurring the mask | |
| if blending_amount > 0: | |
| # The kernel size for blurring; larger blending_amount means more blur | |
| kernel_size = int(blending_amount) | |
| if kernel_size % 2 == 0: | |
| kernel_size += 1 # Kernel size must be odd | |
| mask_blurred = cv2.GaussianBlur(mask_normalized, (kernel_size, kernel_size), 0) | |
| else: | |
| mask_blurred = mask_normalized | |
| # Convert mask to 3 channels | |
| mask_blurred_3ch = cv2.merge([mask_blurred, mask_blurred, mask_blurred]) | |
| # If apply replacement | |
| if apply_replacement: | |
| # Resize replacement image to fit the mask area | |
| # Get bounding box of the mask | |
| y_indices, x_indices = np.where(mask > 0) | |
| if y_indices.size == 0 or x_indices.size == 0: | |
| # No mask detected | |
| return current_image_pil, f"No mask detected for prompt: {prompt}", image_history, current_image_pil | |
| y_min, y_max = y_indices.min(), y_indices.max() | |
| x_min, x_max = x_indices.min(), x_indices.max() | |
| # Extract the region of interest | |
| mask_height = y_max - y_min + 1 | |
| mask_width = x_max - x_min + 1 | |
| # Resize replacement image to fit mask area | |
| replacement_image_resized = replacement_image_pil.resize((mask_width, mask_height)) | |
| replacement_image_np = np.array(replacement_image_resized) | |
| # Create a mask for the ROI | |
| mask_roi = mask_blurred[y_min:y_max+1, x_min:x_max+1] | |
| mask_roi_3ch = cv2.merge([mask_roi, mask_roi, mask_roi]) | |
| # Replace the masked area with the replacement image using blending | |
| region_to_replace = result_image_np[y_min:y_max+1, x_min:x_max+1] | |
| blended_region = (replacement_image_np.astype(np.float32) * mask_roi_3ch + region_to_replace.astype(np.float32) * (1 - mask_roi_3ch)).astype(np.uint8) | |
| result_image_np[y_min:y_max+1, x_min:x_max+1] = blended_region | |
| # If apply color grading | |
| if apply_color_grading: | |
| # Extract the masked area | |
| masked_region = (result_image_np.astype(np.float32) * mask_blurred_3ch).astype(np.uint8) | |
| # Convert color reference image to numpy | |
| color_ref_image_np = np.array(color_ref_image_pil) | |
| # Apply color matching | |
| color_matched_region = apply_color_matching(masked_region, color_ref_image_np) | |
| # Blend the color matched region back into the result image | |
| result_image_np = (color_matched_region.astype(np.float32) * mask_blurred_3ch + result_image_np.astype(np.float32) * (1 - mask_blurred_3ch)).astype(np.uint8) | |
| # Convert result back to PIL Image | |
| result_image_pil = Image.fromarray(result_image_np) | |
| # Update current_image_pil | |
| current_image_pil = result_image_pil | |
| return current_image_pil, f"Applied changes for prompt: {prompt}", image_history, current_image_pil | |
| def undo(image_history): | |
| if image_history and len(image_history) > 1: | |
| # Pop the last image | |
| image_history.pop() | |
| # Return the previous image | |
| current_image_pil = image_history[-1] | |
| return current_image_pil, image_history, current_image_pil | |
| elif image_history and len(image_history) == 1: | |
| current_image_pil = image_history[0] | |
| return current_image_pil, image_history, current_image_pil | |
| else: | |
| # Cannot undo | |
| return None, [], None | |
| def gradio_interface(): | |
| with gr.Blocks() as demo: | |
| # Define the state variables | |
| image_history = gr.State([]) | |
| current_image_pil = gr.State(None) | |
| gr.Markdown("## Continuous Image Editing with LangSAM") | |
| with gr.Row(): | |
| with gr.Column(): | |
| initial_image = gr.Image(type="pil", label="Upload Image") | |
| prompt = gr.Textbox(lines=1, placeholder="Enter prompt for object detection", label="Prompt") | |
| replacement_image = gr.Image(type="pil", label="Replacement Image (optional)") | |
| color_ref_image = gr.Image(type="pil", label="Color Reference Image (optional)") | |
| apply_replacement = gr.Checkbox(label="Apply Replacement", value=False) | |
| apply_color_grading = gr.Checkbox(label="Apply Color Grading", value=False) | |
| blending_amount = gr.Slider(minimum=0, maximum=50, step=1, label="Blending Amount", value=0) | |
| apply_button = gr.Button("Apply Changes") | |
| undo_button = gr.Button("Undo") | |
| with gr.Column(): | |
| current_image_display = gr.Image(type="pil", label="Edited Image", interactive=False) | |
| status = gr.Textbox(lines=2, interactive=False, label="Status") | |
| def initialize_image(initial_image_pil): | |
| # Initialize image history with the initial image | |
| if initial_image_pil is not None: | |
| image_history = [initial_image_pil] | |
| current_image_pil = initial_image_pil | |
| return current_image_pil, image_history, initial_image_pil | |
| else: | |
| return None, [], None | |
| # When the initial image is uploaded, initialize the image history | |
| initial_image.upload(fn=initialize_image, inputs=initial_image, outputs=[current_image_pil, image_history, current_image_display]) | |
| # Apply button click | |
| apply_button.click(fn=process_image, | |
| inputs=[current_image_pil, prompt, replacement_image, color_ref_image, apply_replacement, apply_color_grading, blending_amount, image_history], | |
| outputs=[current_image_pil, status, image_history, current_image_display]) | |
| # Undo button click | |
| undo_button.click(fn=undo, inputs=image_history, outputs=[current_image_pil, image_history, current_image_display]) | |
| demo.launch(share=True) | |
| # Run the Gradio Interface | |
| if __name__ == "__main__": | |
| gradio_interface() | |