284 lines
9.3 KiB
Python
284 lines
9.3 KiB
Python
import cv2
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import numpy as np
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from pathlib import Path
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def preprocess_image(image):
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"""
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预处理图像：转换为HSV色彩空间，提取V通道（明度通道）中的低亮度区域
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"""
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# 转换为HSV色彩空间
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hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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# 提取V通道（明度通道）
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v_channel = hsv[:, :, 2]
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# 对V通道进行二值化，提取低亮度区域（蓝色部分）
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# 使用较低的阈值来提取暗色区域
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_, binary = cv2.threshold(v_channel, 120, 255, cv2.THRESH_BINARY_INV)
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# 形态学操作：开运算去除小噪点，闭运算填充小孔
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
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cleaned = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
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cleaned = cv2.morphologyEx(cleaned, cv2.MORPH_CLOSE, kernel)
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return cleaned, v_channel
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def find_text_regions(binary_image):
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"""
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查找可能的文字区域
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"""
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contours, _ = cv2.findContours(
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binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
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)
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# 筛选合适的文字区域轮廓
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valid_text_regions = []
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for contour in contours:
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area = cv2.contourArea(contour)
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if area > 100: # 过滤太小的区域
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# 计算轮廓的边界矩形
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x, y, w, h = cv2.boundingRect(contour)
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# 计算宽高比
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aspect_ratio = w / h
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# 文字区域通常有合理的宽高比
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if 0.1 < aspect_ratio < 10.0:
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valid_text_regions.append((contour, (x, y, w, h)))
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return valid_text_regions
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def analyze_text_region(roi, original_roi):
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"""
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分析文字区域的特征
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"""
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# 计算黑色像素比例
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total_pixels = roi.shape[0] * roi.shape[1]
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black_pixels = np.sum(roi == 255)
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black_ratio = black_pixels / total_pixels
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# 计算区域的形状特征
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height, width = roi.shape
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aspect_ratio = width / height
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# 计算边缘强度
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edges = cv2.Canny(original_roi, 50, 150)
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edge_density = np.sum(edges > 0) / total_pixels
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return {
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'black_ratio': black_ratio,
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'aspect_ratio': aspect_ratio,
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'edge_density': edge_density,
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'area': total_pixels
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}
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def classify_digit_1_or_2(roi):
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"""
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使用OpenCV基本方法判断是数字1还是2
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"""
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# 调整ROI大小到统一尺寸进行分析
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roi_resized = cv2.resize(roi, (20, 20))
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# 计算像素密度特征
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total_pixels = roi_resized.shape[0] * roi_resized.shape[1]
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black_pixels = np.sum(roi_resized == 255)
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density = black_pixels / total_pixels
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# 计算宽高比
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aspect_ratio = roi_resized.shape[1] / roi_resized.shape[0]
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# 计算水平投影（每行的黑色像素数）
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horizontal_projection = np.sum(roi_resized == 255, axis=1)
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# 计算垂直投影（每列的黑色像素数）
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vertical_projection = np.sum(roi_resized == 255, axis=0)
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# 计算中心区域的像素分布
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center_region = roi_resized[5:15, 5:15]
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center_density = np.sum(center_region == 255) / (10 * 10)
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# 计算垂直投影的方差（数字1方差较小，数字2方差较大）
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vertical_variance = np.var(vertical_projection)
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# 计算水平投影的方差
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horizontal_variance = np.var(horizontal_projection)
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# 基于多个特征的综合判断
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digit1_score = 0
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digit2_score = 0
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# 数字1的判断条件
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if vertical_variance < 15: # 垂直投影方差小
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digit1_score += 2
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if center_density < 0.3: # 中心区域密度较低
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digit1_score += 1
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if density < 0.4: # 整体密度较低
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digit1_score += 1
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if aspect_ratio < 0.8: # 数字1通常较细
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digit1_score += 1
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# 数字2的判断条件
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if vertical_variance > 15: # 垂直投影方差大
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digit2_score += 2
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if center_density > 0.3: # 中心区域密度较高
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digit2_score += 1
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if density > 0.4: # 整体密度较高
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digit2_score += 1
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if horizontal_variance > 8: # 水平投影方差大
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digit2_score += 1
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if aspect_ratio > 0.8: # 数字2通常较宽
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digit2_score += 1
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# 返回得分更高的数字
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if digit1_score > digit2_score:
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return 1
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elif digit2_score > digit1_score:
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return 2
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else:
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# 如果得分相同，使用更复杂的判断
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if vertical_variance < 12:
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return 1
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else:
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return 2
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def detect_text_in_image(image_path, show_process=True):
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"""
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检测图片中的文字区域
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"""
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# 读取图像
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image = cv2.imread(image_path)
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if image is None:
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print(f"无法读取图像: {image_path}")
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return []
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# 预处理
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binary, v_channel = preprocess_image(image)
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# 查找文字区域
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valid_text_regions = find_text_regions(binary)
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results = []
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for contour, (x, y, w, h) in valid_text_regions:
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# 提取ROI
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roi_binary = binary[y:y+h, x:x+w]
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roi_original = image[y:y+h, x:x+w]
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# 分析特征
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features = analyze_text_region(roi_binary, roi_original)
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# 判断是否为有效的低亮度区域
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is_valid_text_region = (
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features['black_ratio'] > 0.05 and # 低亮度像素比例要求降低
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features['edge_density'] > 0.003 # 边缘密度要求降低
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)
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if is_valid_text_region:
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# 使用OpenCV方法识别数字1或2
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predicted_digit = classify_digit_1_or_2(roi_binary)
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results.append({
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'type': 'digit_region',
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'position': (x, y, w, h),
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'digit': predicted_digit,
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'features': features,
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'confidence': 'high' if features['black_ratio'] > 0.3 else 'medium'
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})
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# 在图像上绘制结果
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cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
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cv2.putText(image, f"数字{predicted_digit}", (x, y - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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# 如果需要显示中间过程
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if show_process:
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# 创建过程图像
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process_images = []
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# 1. 原始图片
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process_images.append(("原始图片", image.copy()))
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# 2. HSV-V通道（明度通道）
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v_display = cv2.applyColorMap(v_channel, cv2.COLORMAP_JET)
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process_images.append(("HSV-V通道", v_display))
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# 3. 二值化后的低亮度区域
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process_images.append(("低亮度区域掩码", binary))
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# 4. 轮廓检测结果
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contour_image = image.copy()
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for contour, (x, y, w, h) in valid_text_regions:
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cv2.rectangle(contour_image, (x, y), (x + w, y + h), (0, 0, 255), 2)
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process_images.append(("轮廓检测", contour_image))
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# 5. 最终结果
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process_images.append(("最终结果", image))
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# 保存过程图像
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img_dir = Path(image_path).parent
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base_name = Path(image_path).stem
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for i, (title, img) in enumerate(process_images):
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if len(img.shape) == 2: # 如果是灰度图，转换为BGR
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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output_path = img_dir / f"{base_name}_process_{i+1}_{title}.jpg"
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cv2.imwrite(str(output_path), img)
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print(f"过程图像已保存: {output_path}")
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return image, results
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def main():
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"""
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主函数：测试文字区域检测
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"""
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# 图片路径
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img_dir = Path("imgs")
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img1_path = img_dir / "1.jpg"
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print("开始检测图片中的文字区域...")
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print("=" * 50)
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# 检测第一张图片
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if img1_path.exists():
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print(f"\n检测图片: {img1_path}")
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print("检测过程包括以下步骤：")
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print("1. 原始图片")
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print("2. HSV色彩空间转换")
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print("3. 明度通道提取")
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print("4. 二值化（低亮度区域）")
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print("5. 轮廓检测")
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print("6. 特征分析")
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print("7. 数字识别")
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print("8. 最终结果")
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print("-" * 30)
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result_img1, results1 = detect_text_in_image(str(img1_path), show_process=True)
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print("\n检测结果:")
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if results1:
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for i, result in enumerate(results1):
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print(f" 文字区域 {i+1}:")
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print(f" 位置: {result['position']}")
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print(f" 识别数字: {result['digit']}")
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print(f" 置信度: {result['confidence']}")
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print(f" 黑色比例: {result['features']['black_ratio']:.2f}")
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print(f" 宽高比: {result['features']['aspect_ratio']:.2f}")
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print(f" 边缘密度: {result['features']['edge_density']:.4f}")
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print(f" 面积: {result['features']['area']}")
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else:
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print(" 未检测到文字区域")
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# 保存结果图片
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output_path1 = img_dir / "result_1.jpg"
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cv2.imwrite(str(output_path1), result_img1)
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print(f"\n最终结果图片已保存到: {output_path1}")
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print("中间过程图像也已保存到imgs目录")
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else:
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print(f"图片文件不存在: {img1_path}")
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print("\n检测完成！")
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if __name__ == "__main__":
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main()
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