Update app.py

app.py

@@ -1,80 +1,45 @@
-import cv2
-import numpy as np
 import gradio as gr
-import time
-import imutils
-
-def order_points(pts):
-    rect = np.zeros((4, 2), dtype="float32")
-    s = pts.sum(axis=1)
-    rect[0] = pts[np.argmin(s)]
-    rect[2] = pts[np.argmax(s)]
-    diff = np.diff(pts, axis=1)
-    rect[1] = pts[np.argmin(diff)]
-    rect[3] = pts[np.argmax(diff)]
-    return rect
-
-def four_point_transform(image, pts):
-    rect = order_points(pts)
-    (tl, tr, br, bl) = rect
-    widthA = np.sqrt(((br[0]-bl[0])**2)+((br[1]-bl[1])**2))
-    widthB = np.sqrt(((tr[0]-tl[0])**2)+((tr[1]-tl[1])**2))
-    maxWidth = max(int(widthA), int(widthB))
-    heightA = np.sqrt(((tr[0]-br[0])**2)+((tr[1]-br[1])**2))
-    heightB = np.sqrt(((tl[0]-bl[0])**2)+((tl[1]-bl[1])**2))
-    maxHeight = max(int(heightA), int(heightB))
-    dst = np.array([[0,0],[maxWidth-1,0],[maxWidth-1,maxHeight-1],[0,maxHeight-1]],dtype="float32")
-    M = cv2.getPerspectiveTransform(rect,dst)
-    warped = cv2.warpPerspective(image,M,(maxWidth,maxHeight))
-    return warped
-
-def document_scanner(image, threshold_block_size=21, threshold_C=10):
-    start_time = time.time()
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    blurred = cv2.GaussianBlur(gray,(5,5),0)
-    edged = cv2.Canny(blurred,75,200)
-
-    contours = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    contours = imutils.grab_contours(contours)
-    contours = sorted(contours,key=cv2.contourArea,reverse=True)
-
-    document_contour = None
-    for contour in contours:
-        peri = cv2.arcLength(contour, True)
-        approx = cv2.approxPolyDP(contour, 0.02*peri, True)
-        if len(approx) == 4:
-            document_contour = approx
-            break
-
-    if document_contour is None:
-        print("Warning: Could not find 4 corner points. Returning thresholded image.")
-        T = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,21,10)
-        return cv2.cvtColor(T,cv2.COLOR_GRAY2BGR)
-
-    pts = document_contour.reshape(4,2)
-    warped = four_point_transform(image, pts)
-    warped_gray = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
-    if threshold_block_size % 2 == 0:
-        threshold_block_size += 1
-    T = cv2.adaptiveThreshold(warped_gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,threshold_block_size,threshold_C)
-
-    print(f"Processing time: {time.time()-start_time:.4f} seconds.")
-    return cv2.cvtColor(T,cv2.COLOR_GRAY2BGR)
-
-input_image = gr.Image(type="numpy", label="📄 صورة المستند الأصلية (المائلة)")
-output_image = gr.Image(type="numpy", label="📘 المستند الممسوح ضوئيًا والمُصحَّح")
-
-iface = gr.Interface(
-    fn=document_scanner,
-    inputs=[input_image,
-            gr.Slider(3,41,step=2,value=21,label="حجم الكتلة"),
-            gr.Slider(0,50,step=1,value=10,label="قيمة C")],
-    outputs=output_image,
-    title="📄 ماسح ضوئي للمستندات بتقنية OpenCV",
-    description="حوّل أي صورة مستند ملتقطة بالكاميرا إلى نسخة ممسوحة نظيفة ومستقيمة.",
-    theme="soft",
-    flagging_mode="never"
+import cv2
+import os
+from scan import DocScanner
+
+OUTPUT_DIR = "output"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# دالة الفحص
+def scan_document(image, interactive=False):
+    scanner = DocScanner(interactive)
+    
+    # نحفظ الصورة مؤقتًا
+    temp_path = "temp_input.jpg"
+    cv2.imwrite(temp_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
+    
+    # نفذ عملية المسح
+    scanner.scan(temp_path)
+    
+    # نحصل على اسم الملف الناتج
+    output_path = os.path.join(OUTPUT_DIR, os.path.basename(temp_path))
+    
+    # نقرأ النتيجة لو الملف اتولد
+    if os.path.exists(output_path):
+        scanned = cv2.imread(output_path)
+        scanned = cv2.cvtColor(scanned, cv2.COLOR_BGR2RGB)
+        return scanned
+    else:
+        return None
+
+# واجهة Gradio
+app = gr.Interface(
+    fn=scan_document,
+    inputs=[
+        gr.Image(label="📸 ارفع الصورة", type="numpy"),
+        gr.Checkbox(label="تفعيل الوضع التفاعلي (تعديل الزوايا يدويًا)", value=False)
+    ],
+    outputs=gr.Image(label="📄 النتيجة بعد المسح"),
+    title="AI Document Scanner",
+    description="برنامج لمسح المستندات الورقية والمكتوبة بخط اليد (عربي/إنجليزي) باستخدام OpenCV + LSD + PyImageSearch",
+    examples=[["sample_images/desk.JPG", False]],
 )
 
 if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860)
+    app.launch(server_name="0.0.0.0", server_port=7860)