init repo

ocr_captcha.py

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+#!/usr/bin/env python3
+"""
+验证码识别脚本
+输入：验证码图片路径
+输出：识别结果（打印到 stdout）
+
+预处理流程：
+1. 灰度化
+2. 去除水平干扰线（形态学开运算）
+3. 二值化
+4. 去噪
+5. ddddocr 识别
+"""
+import sys
+import cv2
+import numpy as np
+import ddddocr
+
+def preprocess(img_path):
+    """多种预处理方案"""
+    img = cv2.imread(img_path)
+    if img is None:
+        return []
+
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    results = []
+
+    # 方案1：去水平线 + 二值化
+    # 用水平核做形态学开运算，提取水平线
+    h_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 1))
+    h_lines = cv2.morphologyEx(gray, cv2.MORPH_OPEN, h_kernel)
+    # 从原图减去水平线
+    no_lines = cv2.subtract(gray, h_lines)
+    # 二值化
+    _, binary1 = cv2.threshold(no_lines, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    results.append(binary1)
+
+    # 方案2：自适应阈值
+    adaptive = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                      cv2.THRESH_BINARY, 11, 2)
+    results.append(adaptive)
+
+    # 方案3：去水平线 + 自适应阈值
+    adaptive2 = cv2.adaptiveThreshold(no_lines, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                       cv2.THRESH_BINARY, 11, 2)
+    results.append(adaptive2)
+
+    # 方案4：反色 + 去线 + 二值化
+    inverted = cv2.bitwise_not(gray)
+    h_lines_inv = cv2.morphologyEx(inverted, cv2.MORPH_OPEN, h_kernel)
+    no_lines_inv = cv2.subtract(inverted, h_lines_inv)
+    _, binary4 = cv2.threshold(no_lines_inv, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    results.append(binary4)
+
+    # 方案5：中值滤波去噪 + OTSU
+    median = cv2.medianBlur(gray, 3)
+    _, binary5 = cv2.threshold(median, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    results.append(binary5)
+
+    # 方案6：放大 + 去线 + 锐化
+    big = cv2.resize(gray, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
+    h_kernel_big = cv2.getStructuringElement(cv2.MORPH_RECT, (75, 1))
+    h_lines_big = cv2.morphologyEx(big, cv2.MORPH_OPEN, h_kernel_big)
+    no_lines_big = cv2.subtract(big, h_lines_big)
+    _, binary6 = cv2.threshold(no_lines_big, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    # 去小噪点
+    kernel_small = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
+    binary6 = cv2.morphologyEx(binary6, cv2.MORPH_OPEN, kernel_small)
+    results.append(binary6)
+
+    # 方案7：原图直接用
+    results.append(gray)
+
+    # 方案8: 原始彩色图（ddddocr 有时对彩色图效果更好）
+    results.append(img)
+
+    return results
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("用法: python3 ocr_captcha.py <图片路径>", file=sys.stderr)
+        sys.exit(1)
+
+    img_path = sys.argv[1]
+
+    ocr = ddddocr.DdddOcr(show_ad=False)
+
+    # 先直接用原图试
+    with open(img_path, 'rb') as f:
+        raw_result = ocr.classification(f.read())
+
+    if raw_result and len(raw_result) == 4 and raw_result.isalnum():
+        print(raw_result)
+        return
+
+    # 多种预处理方案
+    preprocessed = preprocess(img_path)
+
+    best = raw_result
+    for i, processed in enumerate(preprocessed):
+        try:
+            if len(processed.shape) == 3:
+                _, buf = cv2.imencode('.png', processed)
+            else:
+                _, buf = cv2.imencode('.png', processed)
+            result = ocr.classification(buf.tobytes())
+
+            if result and len(result) == 4 and result.isalnum():
+                print(result)
+                return
+
+            # 记录最佳结果
+            if result and len(result) >= 3 and (not best or len(result) == 4):
+                best = result
+        except Exception:
+            continue
+
+    # 返回最佳结果
+    if best:
+        print(best[:4] if len(best) > 4 else best)
+    else:
+        print("FAIL", file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    main()