OpenCV-Python实战|直方图全解析:绘制、均衡、2D直方图与反投影(附完整代码)

python# 安装/升级OpenCV-Python（核心依赖）pip install opencv-python -U# 安装辅助库（图像显示、直方图绘制）pip install numpy matplotlib

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 读取图像（灰度图+彩色图，用于不同直方图测试）img_gray = cv2.imread("test.jpg", 0) # 灰度图，替换为自己的图像路径img_color = cv2.imread("test.jpg") # 彩色图（BGR格式）img_color_rgb = cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB) # 转为RGB，用于matplotlib显示# 显示测试图像plt.subplot(1, 2, 1)plt.imshow(img_gray, cmap="gray")plt.title("原始灰度图像")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(img_color_rgb)plt.title("原始彩色图像")plt.axis("off")plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取图像（沿用前面的测试图）img_gray = cv2.imread("test.jpg", 0)img_color = cv2.imread("test.jpg")img_color_rgb = cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB)# 1. 灰度图直方图查找与绘制# 查找直方图（灰度图，通道0，bins=256，范围0-256）hist_gray = cv2.calcHist([img_gray], [0], None, [256], [0, 256])# 绘制灰度直方图plt.subplot(2, 2, 1)plt.imshow(img_gray, cmap="gray")plt.title("原始灰度图像")plt.axis("off")plt.subplot(2, 2, 2)plt.plot(hist_gray, color="black")plt.title("灰度直方图")plt.xlabel("灰度值（0-255）")plt.ylabel("像素数量")plt.xlim([0, 256])# 2. 彩色图直方图查找与绘制（分B、G、R三个通道）colors = ["blue", "green", "red"]channels = [0, 1, 2] # B、G、R通道plt.subplot(2, 2, 3)plt.imshow(img_color_rgb)plt.title("原始彩色图像")plt.axis("off")plt.subplot(2, 2, 4)for channel, color in zip(channels, colors):hist_color = cv2.calcHist([img_color], [channel], None, [256], [0, 256])plt.plot(hist_color, color=color, label=color)plt.title("彩色图直方图（B/G/R）")plt.xlabel("灰度值（0-255）")plt.ylabel("像素数量")plt.xlim([0, 256])plt.legend()plt.tight_layout()plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取图像img_gray = cv2.imread("test.jpg", 0)img_color = cv2.imread("test.jpg")img_color_rgb = cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB)# 1. 灰度图直方图均衡img_gray_eq = cv2.equalizeHist(img_gray)# 查找均衡前后的直方图hist_gray = cv2.calcHist([img_gray], [0], None, [256], [0, 256])hist_gray_eq = cv2.calcHist([img_gray_eq], [0], None, [256], [0, 256])# 2. 彩色图直方图均衡（HSV格式处理，避免颜色失真）img_hsv = cv2.cvtColor(img_color, cv2.COLOR_BGR2HSV)# 对V通道（亮度通道）进行均衡img_hsv[:, :, 2] = cv2.equalizeHist(img_hsv[:, :, 2])# 转回BGR格式img_color_eq = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)img_color_eq_rgb = cv2.cvtColor(img_color_eq, cv2.COLOR_BGR2RGB)# 显示均衡效果对比plt.figure(figsize=(15, 8))# 灰度图均衡对比plt.subplot(2, 3, 1)plt.imshow(img_gray, cmap="gray")plt.title("灰度图（原始）")plt.axis("off")plt.subplot(2, 3, 2)plt.imshow(img_gray_eq, cmap="gray")plt.title("灰度图（均衡后）")plt.axis("off")plt.subplot(2, 3, 3)plt.plot(hist_gray, color="black", label="原始")plt.plot(hist_gray_eq, color="red", label="均衡后")plt.title("灰度直方图对比")plt.xlabel("灰度值")plt.ylabel("像素数量")plt.legend()plt.xlim([0, 256])# 彩色图均衡对比plt.subplot(2, 3, 4)plt.imshow(img_color_rgb)plt.title("彩色图（原始）")plt.axis("off")plt.subplot(2, 3, 5)plt.imshow(img_color_eq_rgb)plt.title("彩色图（均衡后）")plt.axis("off")# 彩色图均衡后直方图plt.subplot(2, 3, 6)colors = ["blue", "green", "red"]channels = [0, 1, 2]for channel, color in zip(channels, colors):hist_eq = cv2.calcHist([img_color_eq], [channel], None, [256], [0, 256])plt.plot(hist_eq, color=color, label=color)plt.title("彩色图均衡后直方图")plt.xlabel("灰度值")plt.ylabel("像素数量")plt.legend()plt.xlim([0, 256])plt.tight_layout()plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 读取彩色图像img_color = cv2.imread("test.jpg")img_hsv = cv2.cvtColor(img_color, cv2.COLOR_BGR2HSV) # 转为HSV格式（H：色调，S：饱和度）# 1. 计算2D直方图（H-S通道，最常用，能精准描述颜色特征）# H通道范围0-179，S通道范围0-255hist_2d = cv2.calcHist([img_hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])# 2. 绘制2D直方图（用matplotlib的imshow，呈现热力图效果）plt.figure(figsize=(10, 8))# 原始彩色图plt.subplot(1, 2, 1)plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))plt.title("原始彩色图像")plt.axis("off")# 2D直方图（热力图）plt.subplot(1, 2, 2)plt.imshow(cv2.resize(hist_2d, (400, 300)), interpolation="nearest", cmap="jet")plt.title("2D直方图（H-S通道）")plt.xlabel("饱和度S（0-255）")plt.ylabel("色调H（0-179）")plt.colorbar(label="像素数量")plt.tight_layout()plt.show()

pythonimport cv2import numpy as npimport matplotlib.pyplot as plt# 1. 读取原始图像和目标模板（模板为原始图像中的目标区域，手动截取）img = cv2.imread("test.jpg")img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)# 手动截取目标模板（这里模拟模板，实际可通过鼠标截取，后续可补充）# 假设模板为图像中左上角区域（x1,y1）到（x2,y2），可根据自己的图像调整坐标x1, y1, x2, y2 = 50, 50, 150, 150template = img_hsv[y1:y2, x1:x2] # 目标模板（HSV格式）# 2. 计算模板直方图（H-S通道，2D直方图）hist_template = cv2.calcHist([template], [0, 1], None, [180, 256], [0, 180, 0, 256])# 直方图归一化（避免像素数量差异影响结果）cv2.normalize(hist_template, hist_template, 0, 255, cv2.NORM_MINMAX)# 3. 直方图反投影（找到原始图像中与模板相似的区域）back_project = cv2.calcBackProject([img_hsv], [0, 1], hist_template, [0, 180, 0, 256], 1)# 4. 形态学处理（腐蚀+膨胀），去除噪声，优化目标区域kernel = np.ones((5, 5), np.uint8)back_project = cv2.erode(back_project, kernel, iterations=1)back_project = cv2.dilate(back_project, kernel, iterations=1)# 5. 绘制目标定位结果（在原始图像上标注目标区域）img_result = img.copy()# 找到反投影图像中的轮廓，定位目标contours, _ = cv2.findContours(back_project, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)if contours:x, y, w, h = cv2.boundingRect(contours[0])cv2.rectangle(img_result, (x, y), (x+w, y+h), (0, 0, 255), 2)cv2.putText(img_result, "Target", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)# 显示效果plt.figure(figsize=(12, 6))plt.subplot(1, 4, 1)plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))plt.title("原始图像")plt.axis("off")plt.subplot(1, 4, 2)plt.imshow(cv2.cvtColor(template, cv2.COLOR_HSV2RGB))plt.title("目标模板")plt.axis("off")plt.subplot(1, 4, 3)plt.imshow(back_project, cmap="gray")plt.title("反投影图像")plt.axis("off")plt.subplot(1, 4, 4)plt.imshow(cv2.cvtColor(img_result, cv2.COLOR_BGR2RGB))plt.title("目标定位结果")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（灰度图+彩色图）img_gray = cv2.imread("test.jpg", 0) # 替换为自己的图像路径img_color = cv2.imread("test.jpg")img_color_rgb = cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB)img_hsv = cv2.cvtColor(img_color, cv2.COLOR_BGR2HSV)# 3. 直方图查找与绘制## 3.1 灰度图直方图hist_gray = cv2.calcHist([img_gray], [0], None, [256], [0, 256])## 3.2 彩色图三通道直方图hist_b = cv2.calcHist([img_color], [0], None, [256], [0, 256])hist_g = cv2.calcHist([img_color], [1], None, [256], [0, 256])hist_r = cv2.calcHist([img_color], [2], None, [256], [0, 256])# 4. 直方图均衡## 4.1 灰度图均衡img_gray_eq = cv2.equalizeHist(img_gray)hist_gray_eq = cv2.calcHist([img_gray_eq], [0], None, [256], [0, 256])## 4.2 彩色图均衡（HSV格式）img_hsv_eq = img_hsv.copy()img_hsv_eq[:, :, 2] = cv2.equalizeHist(img_hsv_eq[:, :, 2])img_color_eq = cv2.cvtColor(img_hsv_eq, cv2.COLOR_HSV2BGR)img_color_eq_rgb = cv2.cvtColor(img_color_eq, cv2.COLOR_BGR2RGB)# 5. 2D直方图（H-S通道）hist_2d = cv2.calcHist([img_hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])# 6. 直方图反投影（目标定位）## 6.1 截取目标模板（可根据自己的图像调整坐标）x1, y1, x2, y2 = 50, 50, 150, 150template = img_hsv[y1:y2, x1:x2]## 6.2 模板直方图与归一化hist_template = cv2.calcHist([template], [0, 1], None, [180, 256], [0, 180, 0, 256])cv2.normalize(hist_template, hist_template, 0, 255, cv2.NORM_MINMAX)## 6.3 反投影与形态学优化back_project = cv2.calcBackProject([img_hsv], [0, 1], hist_template, [0, 180, 0, 256], 1)kernel = np.ones((5, 5), np.uint8)back_project = cv2.erode(back_project, kernel, iterations=1)back_project = cv2.dilate(back_project, kernel, iterations=1)## 6.4 目标定位与标注img_result = img_color.copy()contours, _ = cv2.findContours(back_project, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)if contours:x, y, w, h = cv2.boundingRect(contours[0])cv2.rectangle(img_result, (x, y), (x+w, y+h), (0, 0, 255), 2)cv2.putText(img_result, "Target", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)img_result_rgb = cv2.cvtColor(img_result, cv2.COLOR_BGR2RGB)# 7. 统一显示所有效果plt.figure(figsize=(16, 12))# 直方图查找与绘制plt.subplot(3, 4, 1)plt.imshow(img_gray, cmap="gray")plt.title("原始灰度图")plt.axis("off")plt.subplot(3, 4, 2)plt.plot(hist_gray, color="black")plt.title("灰度直方图")plt.xlim([0, 256])plt.subplot(3, 4, 3)plt.imshow(img_color_rgb)plt.title("原始彩色图")plt.axis("off")plt.subplot(3, 4, 4)plt.plot(hist_b, color="blue", label="B")plt.plot(hist_g, color="green", label="G")plt.plot(hist_r, color="red", label="R")plt.title("彩色图直方图")plt.xlim([0, 256])plt.legend()# 直方图均衡plt.subplot(3, 4, 5)plt.imshow(img_gray_eq, cmap="gray")plt.title("灰度图均衡后")plt.axis("off")plt.subplot(3, 4, 6)plt.plot(hist_gray_eq, color="red")plt.title("均衡后灰度直方图")plt.xlim([0, 256])plt.subplot(3, 4, 7)plt.imshow(img_color_eq_rgb)plt.title("彩色图均衡后")plt.axis("off")plt.subplot(3, 4, 8)plt.imshow(cv2.resize(hist_2d, (400, 300)), interpolation="nearest", cmap="jet")plt.title("2D直方图（H-S）")plt.colorbar(label="像素数量", shrink=0.8)# 直方图反投影plt.subplot(3, 4, 9)plt.imshow(cv2.cvtColor(template, cv2.COLOR_HSV2RGB))plt.title("目标模板")plt.axis("off")plt.subplot(3, 4, 10)plt.imshow(back_project, cmap="gray")plt.title("反投影图像")plt.axis("off")plt.subplot(3, 4, 11)plt.imshow(img_result_rgb)plt.title("目标定位结果")plt.axis("off")plt.subplot(3, 4, 12)plt.axis("off") # 占位，使布局整齐plt.tight_layout()plt.show()# 8. 保存结果（可选）cv2.imwrite("gray_eq.jpg", img_gray_eq)cv2.imwrite("color_eq.jpg", img_color_eq)cv2.imwrite("back_project.jpg", back_project)cv2.imwrite("target_result.jpg", img_result)