Estiliza pagina de upload externo - responsiva (novo47)

document_detector.py

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+import cv2
+import numpy as np
+import sys
+import json
+from pathlib import Path
+import os
+from PIL import Image
+
+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 process_document(image_path, doc_type, output_dir):
+    try:
+        # Carrega e verifica a imagem
+        image = cv2.imread(str(image_path))
+        if image is None:
+            raise Exception("Não foi possível carregar a imagem")
+
+        original = image.copy()
+        
+        # Pré-processamento
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+        edges = cv2.Canny(blurred, 50, 150)
+
+        # Encontra contornos
+        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if not contours:
+            raise Exception("Nenhum contorno encontrado")
+
+        # Ordena contornos por área
+        contours = sorted(contours, key=cv2.contourArea, reverse=True)
+        doc_corners = None
+
+        # Procura o documento
+        for contour in contours:
+            peri = cv2.arcLength(contour, True)
+            approx = cv2.approxPolyDP(contour, 0.02 * peri, True)
+            if len(approx) == 4:
+                doc_corners = approx
+                break
+
+        if doc_corners is None:
+            raise Exception("Documento não encontrado")
+
+        # Processa cantos e aplica transformação
+        doc_corners = doc_corners.reshape(4, 2)
+        ordered_corners = order_points(doc_corners)
+
+        (tl, tr, br, bl) = ordered_corners
+        widthA = np.linalg.norm(br - bl)
+        widthB = np.linalg.norm(tr - tl)
+        maxWidth = max(int(widthA), int(widthB))
+
+        heightA = np.linalg.norm(tr - br)
+        heightB = np.linalg.norm(tl - bl)
+        maxHeight = max(int(heightA), int(heightB))
+
+        dst = np.array([
+            [0, 0],
+            [maxWidth - 1, 0],
+            [maxWidth - 1, maxHeight - 1],
+            [0, maxHeight - 1]
+        ], dtype="float32")
+
+        # Aplica transformação perspectiva
+        matrix = cv2.getPerspectiveTransform(ordered_corners, dst)
+        warped = cv2.warpPerspective(original, matrix, (maxWidth, maxHeight))
+
+        # Salva resultado
+        output_path = os.path.join(output_dir, f"{doc_type}_{Path(image_path).name}")
+        cv2.imwrite(str(output_path), warped)
+        
+        return {
+            "success": True,
+            "message": "Processamento concluído com sucesso",
+            "width": maxWidth,
+            "height": maxHeight
+        }
+
+    except Exception as e:
+        return {
+            "success": False,
+            "message": str(e)
+        }
+
+if __name__ == "__main__":
+    if len(sys.argv) != 4:
+        print(json.dumps({"success": False, "message": "Uso: python document_detector.py <input_path> <doc_type> <output_dir>"}))
+        sys.exit(1)
+
+    result = process_document(Path(sys.argv[1]), sys.argv[2], Path(sys.argv[3]))
+    print(json.dumps(result))