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| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| def exposure_fusion(image_paths): | |
| try: | |
| # Open images from filepaths and convert to OpenCV format (BGR) | |
| images_cv = [cv2.cvtColor(np.array(Image.open(path)), cv2.COLOR_RGB2BGR) for path in image_paths] | |
| # Align images using AlignMTB | |
| align_mtb = cv2.createAlignMTB() | |
| aligned_images = images_cv.copy() | |
| align_mtb.process(images_cv, aligned_images) | |
| # Merge images using exposure fusion (Mertens) | |
| merge_mertens = cv2.createMergeMertens() | |
| fused = merge_mertens.process(aligned_images) | |
| # Convert result from float32 to uint8 and back to RGB | |
| fused = np.clip(fused * 255, 0, 255).astype('uint8') | |
| fused = cv2.cvtColor(fused, cv2.COLOR_BGR2RGB) | |
| return fused | |
| except Exception as e: | |
| return f"Error: {e}" | |
| def stabilize_crop_and_exposure_fusion(image_paths): | |
| try: | |
| # Open images from filepaths and convert to OpenCV format (BGR) | |
| images_cv = [cv2.cvtColor(np.array(Image.open(path)), cv2.COLOR_RGB2BGR) for path in image_paths] | |
| # Align images using AlignMTB | |
| align_mtb = cv2.createAlignMTB() | |
| aligned_images = images_cv.copy() | |
| align_mtb.process(images_cv, aligned_images) | |
| # Determine valid regions in each image (to remove black borders) | |
| bounding_rects = [] | |
| for img in aligned_images: | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| _, mask = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY) | |
| coords = cv2.findNonZero(mask) | |
| if coords is not None: | |
| x, y, w, h = cv2.boundingRect(coords) | |
| bounding_rects.append((x, y, w, h)) | |
| else: | |
| bounding_rects.append((0, 0, img.shape[1], img.shape[0])) | |
| # Compute the common intersection rectangle | |
| if not bounding_rects: | |
| return "No valid images provided." | |
| x_min, y_min, w, h = bounding_rects[0] | |
| x_max = x_min + w | |
| y_max = y_min + h | |
| for (x, y, w, h) in bounding_rects[1:]: | |
| x_min = max(x_min, x) | |
| y_min = max(y_min, y) | |
| x_max = min(x_max, x + w) | |
| y_max = min(y_max, y + h) | |
| if x_max <= x_min or y_max <= y_min: | |
| return "Images do not overlap enough for cropping." | |
| # Crop each aligned image to the intersection region | |
| cropped_images = [img[y_min:y_max, x_min:x_max] for img in aligned_images] | |
| # Merge the cropped images using exposure fusion (Mertens) | |
| merge_mertens = cv2.createMergeMertens() | |
| fused = merge_mertens.process(cropped_images) | |
| fused = np.clip(fused * 255, 0, 255).astype('uint8') | |
| fused = cv2.cvtColor(fused, cv2.COLOR_BGR2RGB) | |
| return fused | |
| except Exception as e: | |
| return f"Error: {e}" | |
| def process_images(image_paths, advanced): | |
| if not image_paths: | |
| return None | |
| if advanced: | |
| return stabilize_crop_and_exposure_fusion(image_paths) | |
| else: | |
| return exposure_fusion(image_paths) | |
| # Gradio Interface: Upload multiple images and choose the processing method. | |
| inputs = [ | |
| gr.File(type="filepath", label="Upload Images", file_count="multiple"), | |
| gr.Checkbox(label="Advanced: Stabilize & Crop Before Fusion", value=False) | |
| ] | |
| iface = gr.Interface( | |
| fn=process_images, | |
| inputs=inputs, | |
| outputs="image", | |
| title="Exposure Fusion with Stabilization", | |
| description=( | |
| "Upload multiple images with varying exposures. " | |
| "If 'Advanced: Stabilize & Crop Before Fusion' is selected, " | |
| "the app aligns the images, crops out extra borders, then fuses them." | |
| ), | |
| ) | |
| iface.launch() | |