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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()
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