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| import cv2 | |
| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| import tempfile | |
| def equalize_exposure(images): | |
| equalized_images = [] | |
| for img in images: | |
| img_lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) | |
| l, a, b = cv2.split(img_lab) | |
| # Apply CLAHE to L-channel | |
| clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) | |
| l_eq = clahe.apply(l) | |
| img_eq = cv2.merge((l_eq, a, b)) | |
| img_eq = cv2.cvtColor(img_eq, cv2.COLOR_LAB2BGR) | |
| equalized_images.append(img_eq) | |
| return equalized_images | |
| def stitch_images(image_files): | |
| # Load images and convert to BGR format | |
| images = [] | |
| for file in image_files: | |
| img_pil = Image.open(file).convert('RGB') | |
| img_bgr = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR) | |
| images.append(img_bgr) | |
| # Check if there are at least two images | |
| if len(images) < 2: | |
| print("Need at least two images to stitch.") | |
| return None, None | |
| # Equalize exposure of images | |
| images_eq = equalize_exposure(images) | |
| # Create a Stitcher object using the default parameters | |
| stitcher = cv2.Stitcher_create(cv2.Stitcher_PANORAMA) | |
| # Configure stitcher parameters | |
| stitcher.setPanoConfidenceThresh(0.8) | |
| stitcher.setWaveCorrection(False) | |
| # Perform stitching | |
| status, stitched = stitcher.stitch(images_eq) | |
| if status != cv2.Stitcher_OK: | |
| print(f"Image stitching failed ({status})") | |
| return None, None | |
| # Perspective correction | |
| # Convert to grayscale | |
| gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY) | |
| # Find all non-zero points (non-black areas) | |
| coords = cv2.findNonZero(gray) | |
| x, y, w, h = cv2.boundingRect(coords) | |
| # Define source and destination points for perspective transform | |
| src_pts = np.float32([ | |
| [x, y], | |
| [x + w, y], | |
| [x + w, y + h], | |
| [x, y + h] | |
| ]) | |
| dst_pts = np.float32([ | |
| [0, 0], | |
| [w, 0], | |
| [w, h], | |
| [0, h] | |
| ]) | |
| # Compute the perspective transform matrix and apply it | |
| M = cv2.getPerspectiveTransform(src_pts, dst_pts) | |
| warped = cv2.warpPerspective(stitched, M, (w, h)) | |
| # Convert corrected image back to PIL format | |
| stitched_rgb = cv2.cvtColor(warped, cv2.COLOR_BGR2RGB) | |
| stitched_image = Image.fromarray(stitched_rgb) | |
| # Save the stitched image to a temporary file for download | |
| temp_file = tempfile.NamedTemporaryFile(suffix='.png', delete=False) | |
| stitched_image.save(temp_file.name) | |
| return stitched_image, temp_file.name | |
| # Gradio Interface | |
| with gr.Blocks() as interface: | |
| gr.Markdown("<h1 style='color: #2196F3; text-align: center;'>Image Stitcher 🧵</h1>") | |
| gr.Markdown("<h3 style='color: #2196F3; text-align: center;'>Upload the images you want to stitch</h3>") | |
| image_upload = gr.Files(type="filepath", label="Upload Images") | |
| stitch_button = gr.Button("Stitch", variant="primary") | |
| stitched_image = gr.Image(type="pil", label="Stitched Image") | |
| download_button = gr.File(label="Download Stitched Image") | |
| stitch_button.click(stitch_images, inputs=image_upload, outputs=[stitched_image, download_button]) | |
| interface.launch() |