v1

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+
+def find_bounding_box(mask):
+    """Find the bounding box around the largest contour in the mask."""
+    mask = mask.astype(np.uint8)
+    _, binary_mask = cv2.threshold(mask, 1, 255, cv2.THRESH_BINARY)
+
+    contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if contours:
+        x, y, w, h = cv2.boundingRect(np.vstack(contours))
+        return x, y, w, h
+    return None
+
+def overlay_logo_on_image(original_img, mask, logo):
+    """Overlay a scaled logo while maintaining aspect ratio and centering it in the bounding box."""
+    bbox = find_bounding_box(mask)
+    if bbox is None:
+        print("No bounding box found. Returning original image.")
+        return original_img
+
+    x, y, w, h = bbox
+    print(f"Bounding box found at: x={x}, y={y}, w={w}, h={h}")
+
+    # Get original logo dimensions
+    logo_h, logo_w = logo.shape[:2]
+
+    # Compute the scaling factor while maintaining aspect ratio
+    scale = min(w / logo_w, h / logo_h)
+    new_w, new_h = int(logo_w * scale), int(logo_h * scale)
+
+    # Resize the logo while maintaining aspect ratio
+    logo_resized = cv2.resize(logo, (new_w, new_h), interpolation=cv2.INTER_AREA)
+
+    # Ensure logo_resized has 4 channels (RGBA)
+    if logo_resized.shape[2] == 3:
+        logo_resized = cv2.cvtColor(logo_resized, cv2.COLOR_RGB2RGBA)
+
+    # Create a blank transparent canvas (RGBA)
+    logo_canvas = np.zeros((h, w, 4), dtype=np.uint8)
+
+    # Compute centering offsets
+    x_offset = (w - new_w) // 2
+    y_offset = (h - new_h) // 2
+
+    # Place the resized logo at the center of the bounding box
+    logo_canvas[y_offset:y_offset + new_h, x_offset:x_offset + new_w] = logo_resized
+
+    # Convert original image to 4-channel if necessary
+    if original_img.shape[2] == 3:
+        original_img = cv2.cvtColor(original_img, cv2.COLOR_RGB2RGBA)
+
+    # Blend the logo into the image using alpha blending
+    alpha = logo_canvas[:, :, 3] / 255.0
+    for c in range(3):
+        original_img[y:y+h, x:x+w, c] = (
+            (1 - alpha) * original_img[y:y+h, x:x+w, c] + alpha * logo_canvas[:, :, c]
+        )
+
+    return original_img
+
+def process_images(original_img, mask, logo, progress=gr.Progress()):
+    """Process images: overlay logo using uploaded mask."""
+    original_img = cv2.cvtColor(np.array(original_img), cv2.COLOR_RGB2BGR)
+    mask = np.array(mask)
+    logo = cv2.cvtColor(np.array(logo), cv2.COLOR_RGB2BGR)
+
+
+    if len(mask.shape) == 3:
+        mask = cv2.cvtColor(mask, cv2.COLOR_RGB2GRAY)
+
+    mask = (mask > 0).astype(np.uint8) * 255  # Ensure binary mask
+
+    print(f"Mask shape: {mask.shape}, unique values: {np.unique(mask)}")
+
+    result_img = overlay_logo_on_image(original_img, mask, logo)
+    result_img = cv2.cvtColor(result_img, cv2.COLOR_BGRA2RGBA) if result_img.shape[2] == 4 else cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
+
+    return result_img
+
+with gr.Blocks() as demo :
+  gr.Markdown("## Inpaint Logo on Uploaded Mask")
+  with gr.Row() :
+    with gr.Column() :
+      with gr.Row():
+        original_img = gr.Image(label="Upload Original Image", type="numpy")
+      with gr.Row():
+        mask = gr.Image(label="Upload Mask (Binary)", type="numpy")
+        logo = gr.Image(label="Upload Logo", type="numpy")
+      btn = gr.Button("Submit")
+    with gr.Column() :
+      output = gr.Image(label="Output Image", format="png")
+  btn.click(fn=process_images, inputs=[original_img, mask, logo], outputs=output)
+
+demo.launch(share=True, debug=True)

requirements.txt

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+opencv-python
+numpy