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