OpenCV-Python实战|吃透图像特征:从基础概念到实操应用,读懂计算机“看”世界的方式

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取图像并预处理img = cv2.imread("feature_test.jpg", 0)img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1)# 提取边缘特征（Canny边缘检测，前文重点讲解）edges = cv2.Canny(img_blur, 50, 150)# 显示效果（原始图 vs 边缘特征图）plt.subplot(1, 2, 1)plt.imshow(img, cmap="gray")plt.title("原始图像")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(edges, cmap="gray")plt.title("边缘特征提取结果")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取彩色图像（颜色特征需用彩色图）img = cv2.imread("feature_test.jpg")img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# 提取颜色特征：筛选红色区域（RGB范围可调整）lower_red = np.array([150, 0, 0]) # 红色下限upper_red = np.array([255, 100, 100]) # 红色上限# 生成颜色掩码（红色区域为白色，其他为黑色）red_mask = cv2.inRange(img_rgb, lower_red, upper_red)# 显示效果（原始图 vs 红色特征掩码）plt.subplot(1, 2, 1)plt.imshow(img_rgb)plt.title("原始彩色图像")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(red_mask, cmap="gray")plt.title("红色颜色特征提取结果")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取图像并提取边缘img = cv2.imread("feature_test.jpg", 0)img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1)edges = cv2.Canny(img_blur, 50, 150)# 提取轮廓（形状特征的基础）contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# 绘制轮廓，并计算形状特征（面积、周长）img_color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)for i, contour in enumerate(contours):# 绘制轮廓（绿色，线条粗细2）cv2.drawContours(img_color, [contour], -1, (0, 255, 0), 2)# 计算形状特征：面积、周长area = cv2.contourArea(contour) # 面积perimeter = cv2.arcLength(contour, True) # 周长（True表示闭合轮廓）# 标注形状特征cv2.putText(img_color, f"Area:{int(area)}", (10, 30+i*30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)cv2.putText(img_color, f"Peri:{int(perimeter)}", (120, 30+i*30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)# 显示效果（边缘图 vs 形状特征提取结果）plt.subplot(1, 2, 1)plt.imshow(edges, cmap="gray")plt.title("边缘特征图")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("形状特征提取结果（面积+周长）")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取灰度图像（纹理特征常用灰度图）img = cv2.imread("feature_test.jpg", 0)img_blur = cv2.GaussianBlur(img, (3, 3), sigmaX=1)# 简单纹理特征提取：通过灰度差异计算纹理（新手入门版）# 计算水平方向的灰度差异（纹理越复杂，差异值越大）texture = cv2.Sobel(img_blur, cv2.CV_64F, 1, 0, ksize=3)# 归一化，便于显示texture = cv2.normalize(texture, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)# 显示效果（原始图 vs 纹理特征图）plt.subplot(1, 2, 1)plt.imshow(img_blur, cmap="gray")plt.title("原始灰度图像")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(texture, cmap="gray")plt.title("纹理特征提取结果（水平方向）")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取彩色图像img = cv2.imread("feature_test.jpg")img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)img_blur = cv2.GaussianBlur(img_gray, (3, 3), sigmaX=1)# 1. 提取边缘特征edges = cv2.Canny(img_blur, 50, 150)# 2. 提取颜色特征（筛选蓝色目标）lower_blue = np.array([0, 0, 150])upper_blue = np.array([100, 100, 255])blue_mask = cv2.inRange(img_rgb, lower_blue, upper_blue)# 3. 结合边缘和颜色特征，提取轮廓（形状特征）# 与操作：保留同时满足边缘和颜色特征的区域combined_mask = cv2.bitwise_and(edges, blue_mask)contours, _ = cv2.findContours(combined_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# 4. 绘制综合特征提取结果img_result = img.copy()for contour in contours:# 绘制轮廓（红色，线条粗细2）cv2.drawContours(img_result, [contour], -1, (0, 0, 255), 2)# 计算并标注形状特征（面积、周长）area = cv2.contourArea(contour)perimeter = cv2.arcLength(contour, True)x, y = contour[0][0]cv2.putText(img_result, f"Area:{int(area)}", (x, y-30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)cv2.putText(img_result, f"Peri:{int(perimeter)}", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)# 显示综合效果plt.figure(figsize=(15, 5))# 边缘特征plt.subplot(1, 4, 1)plt.imshow(edges, cmap="gray")plt.title("边缘特征")plt.axis("off")# 颜色特征plt.subplot(1, 4, 2)plt.imshow(blue_mask, cmap="gray")plt.title("蓝色颜色特征")plt.axis("off")# 综合掩码plt.subplot(1, 4, 3)plt.imshow(combined_mask, cmap="gray")plt.title("边缘+颜色综合掩码")plt.axis("off")# 最终结果plt.subplot(1, 4, 4)plt.imshow(cv2.cvtColor(img_result, cv2.COLOR_BGR2RGB))plt.title("综合特征应用结果")plt.axis("off")plt.tight_layout()plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 环境验证（可选）print("OpenCV版本：", cv2.__version__)# 2. 读取图像（替换为自己的图像路径）img = cv2.imread("feature_test.jpg")if img is None:print("错误：未找到图像，请检查图像路径！")else:img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)img_blur = cv2.GaussianBlur(img_gray, (3, 3), sigmaX=1)# 3. 提取4大核心特征# 3.1 边缘特征（Canny）edges = cv2.Canny(img_blur, 50, 150)# 3.2 颜色特征（红色）lower_red = np.array([150, 0, 0])upper_red = np.array([255, 100, 100])red_mask = cv2.inRange(img_rgb, lower_red, upper_red)# 3.3 形状特征（轮廓+面积+周长）contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)img_shape = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR)for i, contour in enumerate(contours):cv2.drawContours(img_shape, [contour], -1, (0, 255, 0), 2)area = cv2.contourArea(contour)perimeter = cv2.arcLength(contour, True)cv2.putText(img_shape, f"Area:{int(area)}", (10, 30+i*30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)# 3.4 纹理特征（水平方向Sobel）texture = cv2.Sobel(img_blur, cv2.CV_64F, 1, 0, ksize=3)texture = cv2.normalize(texture, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)# 4. 综合特征应用（边缘+颜色+形状）lower_blue = np.array([0, 0, 150])upper_blue = np.array([100, 100, 255])blue_mask = cv2.inRange(img_rgb, lower_blue, upper_blue)combined_mask = cv2.bitwise_and(edges, blue_mask)contours_combined, _ = cv2.findContours(combined_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)img_combined = img.copy()for contour in contours_combined:cv2.drawContours(img_combined, [contour], -1, (0, 0, 255), 2)area = cv2.contourArea(contour)perimeter = cv2.arcLength(contour, True)x, y = contour[0][0]cv2.putText(img_combined, f"Area:{int(area)}", (x, y-30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)# 5. 统一显示所有效果plt.figure(figsize=(15, 10))# 第一行：4大核心特征plt.subplot(2, 4, 1)plt.imshow(img_rgb)plt.title("原始图像")plt.axis("off")plt.subplot(2, 4, 2)plt.imshow(edges, cmap="gray")plt.title("边缘特征")plt.axis("off")plt.subplot(2, 4, 3)plt.imshow(red_mask, cmap="gray")plt.title("颜色特征（红色）")plt.axis("off")plt.subplot(2, 4, 4)plt.imshow(img_shape)plt.title("形状特征（面积）")plt.axis("off")# 第二行：纹理特征+综合应用plt.subplot(2, 4, 5)plt.imshow(texture, cmap="gray")plt.title("纹理特征")plt.axis("off")plt.subplot(2, 4, 6)plt.imshow(blue_mask, cmap="gray")plt.title("颜色特征（蓝色）")plt.axis("off")plt.subplot(2, 4, 7)plt.imshow(combined_mask, cmap="gray")plt.title("边缘+颜色综合掩码")plt.axis("off")plt.subplot(2, 4, 8)plt.imshow(cv2.cvtColor(img_combined, cv2.COLOR_BGR2RGB))plt.title("综合特征应用结果")plt.axis("off")plt.tight_layout()plt.show()# 6. 保存结果（可选）cv2.imwrite("edge_feature.jpg", edges)cv2.imwrite("color_feature.jpg", red_mask)cv2.imwrite("shape_feature.jpg", img_shape)cv2.imwrite("combined_feature.jpg", img_combined)