import cv2 import numpy as np from matplotlib import pyplot as plt from sklearn.cluster import KMeans import math # 纸张尺寸标准 (单位: mm) PAPER_SIZES = { "A4": (210, 297), "B5": (176, 250), "A5": (148, 210) } #使用Harris角点检测算法检测图像中的角点""" def detect_corners_with_harris(image, block_size=2, ksize=3, k=0.04, threshold=0.01): if len(image.shape) == 3: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray = image gray = np.float32(gray) dst = cv2.cornerHarris(gray, block_size, ksize, k) dst = cv2.dilate(dst, None) corner_img = image.copy() corners = [] threshold_val = threshold * dst.max() for i in range(dst.shape[0]): for j in range(dst.shape[1]): if dst[i, j] > threshold_val: corners.append((j, i)) cv2.circle(corner_img, (j, i), 5, (0, 0, 255), 2) return corner_img, corners #从Harris角点中找出最可能的纸张四个角点 def find_paper_corners_using_harris(corners, image_shape, top_k=4): height, width = image_shape[:2] top_left = (0, 0) top_right = (width, 0) bottom_right = (width, height) bottom_left = (0, height) candidates = { 'top_left': [], 'top_right': [], 'bottom_right': [], 'bottom_left': [] } for corner in corners: x, y = corner distances = { 'top_left': np.sqrt((x - top_left[0]) ** 2 + (y - top_left[1]) ** 2), 'top_right': np.sqrt((x - top_right[0]) ** 2 + (y - top_right[1]) ** 2), 'bottom_right': np.sqrt((x - bottom_right[0]) ** 2 + (y - bottom_right[1]) ** 2), 'bottom_left': np.sqrt((x - bottom_left[0]) ** 2 + (y - bottom_left[1]) ** 2) } closest_category = min(distances, key=distances.get) candidates[closest_category].append((corner, distances[closest_category])) selected_corners = [] for category in candidates: if candidates[category]: candidates[category].sort(key=lambda x: x[1]) selected_corners.append(candidates[category][0][0]) if len(selected_corners) < 4: return None return order_points(np.array(selected_corners)) #对点进行排序：左上，右上，右下，左下 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 #检测图像中的纸张轮廓 def detect_paper_contour(image): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (5, 5), 0) edged = cv2.Canny(blurred, 50, 200) contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours = sorted(contours, key=cv2.contourArea, reverse=True)[:5] for contour in contours: peri = cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, 0.02 * peri, True) if len(approx) == 4: return order_points(approx.reshape(4, 2)), gray return None, gray #透视变换校正图像 def perspective_transform(image, points): def max_width_height(pts): width1 = np.sqrt(((pts[0][0] - pts[1][0]) ** 2) + ((pts[0][1] - pts[1][1]) ** 2)) width2 = np.sqrt(((pts[2][0] - pts[3][0]) ** 2) + ((pts[2][1] - pts[3][1]) ** 2)) max_width = max(int(width1), int(width2)) height1 = np.sqrt(((pts[0][0] - pts[3][0]) ** 2) + ((pts[0][1] - pts[3][1]) ** 2)) height2 = np.sqrt(((pts[1][0] - pts[2][0]) ** 2) + ((pts[1][1] - pts[2][1]) ** 2)) max_height = max(int(height1), int(height2)) return max_width, max_height points = order_points(points) max_width, max_height = max_width_height(points) dst = np.array([ [0, 0], [max_width - 1, 0], [max_width - 1, max_height - 1], [0, max_height - 1] ], dtype="float32") M = cv2.getPerspectiveTransform(points.astype("float32"), dst) warped = cv2.warpPerspective(image, M, (max_width, max_height)) return warped, M, max_width, max_height, points #创建纸张区域掩码 def create_paper_mask(width, height): mask = np.ones((height, width), dtype=np.uint8) * 255 print(f"创建纸张掩码，尺寸: {mask.shape}, 类型: {mask.dtype}") return mask #预处理图像以进行形状检测 def preprocess_image_for_shape_detection(image, paper_mask=None): # 转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 可选：如果有纸张掩码，仅处理纸张内部区域 if paper_mask is not None: # 确保掩码尺寸与图像匹配 if paper_mask.shape[:2] != hsv.shape[:2]: print(f"调整掩码尺寸: {paper_mask.shape} -> {hsv.shape[:2]}") paper_mask = cv2.resize(paper_mask, (hsv.shape[1], hsv.shape[0]), interpolation=cv2.INTER_NEAREST) # 确保掩码是uint8类型 if paper_mask.dtype != np.uint8: print("转换掩码数据类型为uint8") paper_mask = paper_mask.astype(np.uint8) # 应用掩码 try: hsv = cv2.bitwise_and(hsv, hsv, mask=paper_mask) except cv2.error as e: print(f"应用掩码时出错: {e}") print(f"HSV形状: {hsv.shape}, 掩码形状: {paper_mask.shape}") # 高斯模糊去噪 blurred = cv2.GaussianBlur(hsv, (5, 5), 0) return hsv, blurred #使用颜色聚类检测图像中的颜色区域 def detect_color_regions(image, paper_mask=None, n_clusters=6, min_area_ratio=0.002): h, w = image.shape[:2] # 计算最小面积阈值（基于纸张面积比例） min_area = min_area_ratio * w * h print(f"最小面积阈值: {min_area} 像素") # 调整图像大小以提高聚类效率 if h > 500 or w > 500: scale = 500 / max(h, w) resized = cv2.resize(image, None, fx=scale, fy=scale) h_resized, w_resized = resized.shape[:2] print(f"调整图像大小: {h}x{w} -> {h_resized}x{w_resized}, 缩放比例: {scale}") else: resized = image.copy() scale = 1.0 h_resized, w_resized = h, w print(f"图像大小未调整: {h}x{w}") # 预处理图像 hsv, blurred = preprocess_image_for_shape_detection(resized, paper_mask) # 转换为2D数组 pixel_values = hsv.reshape((-1, 3)) pixel_values = np.float32(pixel_values) print(f"像素值数组形状: {pixel_values.shape}") # 排除背景颜色（白色背景：低饱和度、高亮度） not_background = np.logical_and( hsv[:, :, 1] > 30, # 饱和度高（目标颜色） hsv[:, :, 2] < 230 # 亮度不极端（背景很亮） ) if paper_mask is not None: # 调整掩码尺寸以匹配调整后的图像 paper_mask_resized = cv2.resize(paper_mask, (w_resized, h_resized), interpolation=cv2.INTER_NEAREST) not_background = np.logical_and(not_background, paper_mask_resized > 0) pixel_mask = not_background.reshape(-1) valid_pixels = pixel_values[pixel_mask] print(f"有效像素数量: {len(valid_pixels)}") # 执行KMeans聚类 regions = [] if len(valid_pixels) > 0: # 显式设置n_init参数以避免FutureWarning kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10) kmeans.fit(valid_pixels) print(f"KMeans聚类完成，聚类数: {n_clusters}") # 获取聚类标签并转换回原始图像尺寸 labels = kmeans.predict(pixel_values) labels = labels.reshape((h_resized, w_resized)) print(f"标签数组形状: {labels.shape}") # 为每个聚类创建掩码 for cluster_id in range(n_clusters): mask = np.uint8(labels == cluster_id) * 255 # 形态学操作优化掩码 kernel = np.ones((5, 5), np.uint8) mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2) # 开运算去噪 mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=2) # 闭运算连接区域 # 如果调整了图像大小，将掩码调整回原始尺寸 if scale != 1.0: mask = cv2.resize(mask, (w, h), interpolation=cv2.INTER_NEAREST) print(f"调整掩码尺寸: {mask.shape}") # 如果有纸张掩码，仅保留纸张内部区域 if paper_mask is not None: # 确保掩码尺寸匹配 if mask.shape[:2] != paper_mask.shape[:2]: paper_mask_resized = cv2.resize(paper_mask, (mask.shape[1], mask.shape[0]), interpolation=cv2.INTER_NEAREST) else: paper_mask_resized = paper_mask mask = cv2.bitwise_and(mask, paper_mask_resized) # 查找轮廓 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) print(f"聚类 {cluster_id} 检测到 {len(contours)} 个轮廓") for contour in contours: area = cv2.contourArea(contour) if area > min_area: # 过滤小区域 regions.append(contour) print(f" 保留轮廓，面积: {area} 像素") print(f"总共检测到 {len(regions)} 个颜色区域") return regions #识别物体形状，优化近似参数和判断逻辑 def identify_shape(contour): peri = cv2.arcLength(contour, True) if peri == 0: return "不规则形状" # 更严格的多边形近似（epsilon=0.02*peri） approx = cv2.approxPolyDP(contour, 0.02 * peri, True) num_sides = len(approx) area = cv2.contourArea(contour) circularity = 4 * np.pi * area / (peri ** 2) # 椭圆拟合（增加轮廓点数判断，避免报错） if len(contour) >= 5: try: ellipse = cv2.fitEllipse(contour) (center, axes, orientation) = ellipse major_axis = max(axes) minor_axis = min(axes) eccentricity = np.sqrt(1 - (minor_axis / major_axis) ** 2) if major_axis != 0 else 0 ellipse_area = np.pi * (major_axis / 2) * (minor_axis / 2) ellipse_ratio = area / ellipse_area if ellipse_area > 0 else 0 # 更严格的椭圆判定条件 if 0.8 <= ellipse_ratio <= 1.2 and 0.5 < eccentricity < 0.95: return "椭圆" except: pass # 多边形形状判断（优化长宽比计算） if num_sides == 3: return "三角形" elif num_sides == 4: rect = cv2.minAreaRect(contour) w, h = rect[1] if min(w, h) == 0: # 避免除以0 aspect_ratio = 0 else: aspect_ratio = max(w, h) / min(w, h) if 0.85 <= aspect_ratio <= 1.15: return "正方形" else: return "矩形" elif circularity > 0.85: # 圆形判定（圆度更高） return "圆形" elif 5 <= num_sides <= 10: return f"{num_sides}边形" else: return "不规则形状" #计算物体的几何属性 def calculate_properties(contour, pixel_to_mm=1.0): rect = cv2.minAreaRect(contour) width_px, height_px = rect[1] dimensions_px = sorted([width_px, height_px], reverse=True) length_px, width_px = dimensions_px length_mm = length_px * pixel_to_mm width_mm = width_px * pixel_to_mm area_px = cv2.contourArea(contour) area_mm = area_px * (pixel_to_mm ** 2) M = cv2.moments(contour) if M["m00"] != 0: cx = int(M["m10"] / M["m00"]) cy = int(M["m01"] / M["m00"]) else: cx, cy = 0, 0 return length_mm, width_mm, area_mm, (cx, cy), length_px, width_px, area_px #提取物体主要颜色(HSV) def extract_dominant_color(image, contour): mask = np.zeros(image.shape[:2], np.uint8) cv2.drawContours(mask, [contour], -1, 255, -1) hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 提取掩码区域的颜色 masked_pixels = hsv[mask == 255] if len(masked_pixels) == 0: return (0, 0, 0) # 计算主导颜色 (使用中位数更鲁棒) dominant_hsv = np.median(masked_pixels, axis=0) return tuple(map(int, dominant_hsv)) #可视化检测过程的各个步骤 def visualize_detection_steps(image, corrected_image, paper_mask, color_regions, result_img): """可视化检测过程的各个步骤，每个步骤显示在单独的窗口中""" # 显示原始图像 plt.figure(figsize=(10, 8)) plt.title('original image') plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) plt.axis('off') plt.tight_layout() plt.show() # 显示校正后的图像 plt.figure(figsize=(10, 8)) plt.title('corrected image') plt.imshow(cv2.cvtColor(corrected_image, cv2.COLOR_BGR2RGB)) plt.axis('off') plt.tight_layout() plt.show() # 显示颜色聚类区域 color_clusters = corrected_image.copy() colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0), (255, 255, 0), (0, 255, 255), (255, 0, 255), (128, 0, 0), (0, 128, 0)] for i, contour in enumerate(color_regions): color = colors[i % len(colors)] cv2.drawContours(color_clusters, [contour.astype(int)], -1, color, 3) plt.figure(figsize=(10, 8)) plt.title('Color Cluster Area') plt.imshow(cv2.cvtColor(color_clusters, cv2.COLOR_BGR2RGB)) plt.axis('off') plt.tight_layout() plt.show() # 显示二值化结果 gray = cv2.cvtColor(corrected_image, cv2.COLOR_BGR2GRAY) binary = cv2.adaptiveThreshold( gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2 ) if paper_mask is not None: binary = cv2.bitwise_and(binary, binary, mask=paper_mask) plt.figure(figsize=(10, 8)) plt.title('Binary image') plt.imshow(binary, cmap='gray') plt.axis('off') plt.tight_layout() plt.show() # 显示最终识别结果 plt.figure(figsize=(10, 8)) plt.title('Shape recognition results') plt.imshow(cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)) plt.axis('off') plt.tight_layout() plt.show() #主函数 def main(image_path): image = cv2.imread(image_path) if image is None: print(f"错误：无法读取图像 {image_path}") return # 1. 使用Harris角点检测纸张边框 corner_img, corners = detect_corners_with_harris(image) paper_points = find_paper_corners_using_harris(corners, image.shape) # 初始化变量 corrected_image = image.copy() paper_mask = None pixel_to_mm = 1.0 paper_detected = False # 2. 透视变换校正图像 if paper_points is not None: # 执行透视变换 corrected_image, M, max_width, max_height, original_points = perspective_transform(image, paper_points) paper_detected = True print("成功检测并校正纸张轮廓") # 创建纸张掩码（仅处理纸张内部区域） paper_mask = create_paper_mask(max_width, max_height) # 计算物理尺寸转换因子 paper_width_mm, paper_height_mm = PAPER_SIZES["A4"] pixel_to_mm_x = paper_width_mm / max_width pixel_to_mm_y = paper_height_mm / max_height pixel_to_mm = (pixel_to_mm_x + pixel_to_mm_y) / 2.0 else: print("未检测到纸张轮廓 - 使用原始图像进行分析") h, w = image.shape[:2] paper_mask = create_paper_mask(w, h) # 3. 基于颜色聚类和形态学操作检测几何形状 color_regions = detect_color_regions( corrected_image, paper_mask=paper_mask, n_clusters=6, # 调整为6个聚类，匹配目标颜色数量 min_area_ratio=0.002 # 提高最小面积阈值，过滤小区域 ) if not color_regions: print("未检测到颜色区域 - 尝试使用二值化方法检测") # 备用方案：使用二值化方法检测 gray = cv2.cvtColor(corrected_image, cv2.COLOR_BGR2GRAY) binary = cv2.adaptiveThreshold( gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2 ) if paper_mask is not None: binary = cv2.bitwise_and(binary, binary, mask=paper_mask) # 形态学操作 kernel = np.ones((5, 5), np.uint8) binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel, iterations=2) binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel, iterations=2) # 查找轮廓 contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) min_area = 0.001 * binary.shape[0] * binary.shape[1] color_regions = [c for c in contours if cv2.contourArea(c) > min_area] if not color_regions: print("未检测到几何图形") return # 初始化结果列表 results = [] result_img = corrected_image.copy() # 处理每个颜色区域 for i, contour in enumerate(color_regions): # 提取主导颜色 hsv_color = extract_dominant_color(corrected_image, contour) rgb_color = cv2.cvtColor(np.uint8([[hsv_color]]), cv2.COLOR_HSV2BGR)[0][0] color_name = f"RGB({rgb_color[2]}, {rgb_color[1]}, {rgb_color[0]})" # 识别形状 shape = identify_shape(contour) # 计算几何属性 length_mm, width_mm, area_mm, center_px, length_px, width_px, area_px = calculate_properties( contour, pixel_to_mm ) # 存储结果 results.append({ "id": i + 1, "shape": shape, "length_mm": length_mm, "width_mm": width_mm, "area_mm": area_mm, "color": color_name, "hsv_color": hsv_color, "center_px": center_px, "length_px": length_px, "width_px": width_px, "area_px": area_px }) # 在图像上标注物体 color = (int(rgb_color[2]), int(rgb_color[1]), int(rgb_color[0])) cv2.drawContours(result_img, [contour], -1, color, 3) cv2.putText(result_img, f"{i + 1}({shape})", (int(center_px[0]), int(center_px[1])), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) # 可视化检测步骤 visualize_detection_steps(image, corrected_image, paper_mask, color_regions, result_img) # 打印分析结果 print("\n===== 图像分析结果 =====") print(f"检测到 {len(results)} 个几何图形") print("-" * 70) for obj in results: print(f"图形 {obj['id']}:") print(f" 形状: {obj['shape']}") print(f" 长度: {obj['length_mm']:.2f} mm ({obj['length_px']:.1f} 像素)") print(f" 宽度: {obj['width_mm']:.2f} mm ({obj['width_px']:.1f} 像素)") print(f" 面积: {obj['area_mm']:.2f} mm² ({obj['area_px']:.1f} 像素²)") print(f" 颜色: {obj['color']} (HSV: {obj['hsv_color']})") print(f" 中心坐标: ({obj['center_px'][0]}, {obj['center_px'][1]})") print("-" * 70) if __name__ == "__main__": image_path = "ALL2.jpg" # 替换为实际图像路径 main(image_path) 上述代码创建纸张区域掩码后要对图像进行处理再进一步识别图像内部的不同几何形状，上述代码所采用的是提取颜色进行识别，但是对于图片中的颜色可能是分配不均匀的，所以会导致识别效果不是很好，我想换成边缘检测的方法识别。先对图片进行二值化操作再进行边缘检测。请根据我的描述修改上述代码。