Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+
+def exposure_fusion(images):
+    try:
+        # Convert PIL images (RGB) to OpenCV format (BGR)
+        images_cv = [cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) for img in images]
+        
+        # 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(images):
+    try:
+        # Convert images from PIL (RGB) to OpenCV format (BGR)
+        images_cv = [cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) for img in images]
+
+        # 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)
+            # Pixels above a small threshold are considered valid
+            _, 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(images, advanced):
+    if not images:
+        return None
+    # If advanced option is selected, use stabilization & cropping before fusion.
+    if advanced:
+        return stabilize_crop_and_exposure_fusion(images)
+    else:
+        return exposure_fusion(images)
+
+# Gradio Interface: Upload multiple images and choose the processing method.
+inputs = [
+    gr.File(type="file", 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()