Update app.py

app.py

@@ -1,98 +1,98 @@
-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])
-
+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()