Upload app.py

app.py

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+#######################################################################################
+#
+# MIT License
+#
+# Copyright (c) [2025] [[email protected]]
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+#######################################################################################
+#
+#
+# Source code is based on or inspired by several projects.
+# For more details and proper attribution, please refer to the following resources:
+#
+# - [stackoverflow] - [https://stackoverflow.com/questions/22656698/perspective-correction-in-opencv-using-python]
+# - [rembg] [https://huggingface.co/spaces/leonelhs/rembg]
+# - [rembg] [https://github.com/danielgatis/rembg]
+# - [Chatgpt] [https://chatgpt.com/]
+#
+# The image is first processed by an AI service.
+# This step provides a cleaner, bounded version of the image,
+# because OpenCV’s edge detection is not always reliable on raw inputs.
+# With the improved intermediate image, OpenCV can detect borders more consistently
+# and the perspective unwrap produces better results.
+
+
+import cv2
+import numpy as np
+import gradio as gr
+from gradio_client import Client, handle_file
+
+client = Client("leonelhs/rembg")
+
+def unwrap(image, mask):
+    img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+
+    _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    contours = sorted(contours, key=cv2.contourArea, reverse=True)
+
+    if len(contours) > 0:
+        cnt = contours[0]
+        peri = cv2.arcLength(cnt, True)
+        approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
+
+        if len(approx) == 4:
+            corners = approx.reshape(4, 2).astype(np.float32)
+
+            # Order points: top-left, top-right, bottom-right, bottom-left
+            rect = np.zeros((4, 2), dtype="float32")
+            s = corners.sum(axis=1)
+            rect[0] = corners[np.argmin(s)]
+            rect[2] = corners[np.argmax(s)]
+
+            diff = np.diff(corners, axis=1)
+            rect[1] = corners[np.argmin(diff)]
+            rect[3] = corners[np.argmax(diff)]
+
+            (tl, tr, br, bl) = rect
+
+            # Compute width & height
+            widthA = np.linalg.norm(br - bl)
+            widthB = np.linalg.norm(tr - tl)
+            maxWidth = int(max(widthA, widthB))
+
+            heightA = np.linalg.norm(tr - br)
+            heightB = np.linalg.norm(tl - bl)
+            maxHeight = int(max(heightA, heightB))
+
+            dst = np.array([
+                [0, 0],
+                [maxWidth - 1, 0],
+                [maxWidth - 1, maxHeight - 1],
+                [0, maxHeight - 1]
+            ], dtype="float32")
+
+            # Perspective transform
+            M = cv2.getPerspectiveTransform(rect, dst)
+            warped = cv2.warpPerspective(img, M, (maxWidth, maxHeight))
+
+            return cv2.cvtColor(warped, cv2.COLOR_BGR2RGB), mask, corners
+
+    # fallback: return original if no rectangle
+    return image, mask, contours
+
+def predict(img):
+    """
+        Unwrap an image using AI-assisted preprocessing and OpenCV.
+
+        The algorithm first leverages an AI service to generate a cleaner,
+        well-bounded intermediate image. This helps OpenCV detect borders
+        more reliably before performing the perspective unwrap.
+
+        Parameters:
+            img (string): File path to the input image to be unwrapped.
+
+        Returns:
+            path (string): File path to the generated, unwrapped image.
+    """
+
+    # Step 1: Use an AI service to preprocess the image.
+    #   - OpenCV can detect edges, but results are inconsistent depending on noise/lighting.
+    #   - The AI model generates a cleaner, well-bounded intermediate image.
+    crop, mask = client.predict(image=handle_file(img), session="U2NET", smoot=True, api_name="/predict")
+
+    # Step 2: Apply OpenCV on this intermediate image for more accurate border detection
+    #         before performing the perspective unwrap.
+    crop = cv2.imread(crop)
+    mask = cv2.imread(mask)
+    return unwrap(crop, mask)
+
+
+with gr.Blocks() as app:
+    gr.Markdown("## 🖼️ Rectangle Detection & Perspective Unwrap")
+    with gr.Row():
+        with gr.Column(scale=1):
+            inp = gr.Image(type="filepath", label="Upload Image")
+            btn_unwrap = gr.Button("📐 Perspective Unwrap")
+        with gr.Column(scale=2):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    out_unwrap = gr.Image(type="numpy", label="Unwrapped Rectangle")
+                    with gr.Accordion("See intermediates", open=False):
+                        out_mask = gr.Image(type="numpy", label="Detected Corners")
+                        out_corners = gr.JSON(label="Corners (x,y)")
+
+    btn_unwrap.click(predict, inputs=inp, outputs=[out_unwrap, out_mask, out_corners])
+
+app.launch(share=False, debug=True, show_error=True, mcp_server=True)
+app.queue()
+