Python实现：裁剪图像最亮区域并获取颜色值范围

我有一个能检测并圈出图像最亮区域的程序，现在需要获取圈选区域内的颜色值范围。计划是先裁剪该区域，再提取其中所有像素的颜色值，但不知道怎么根据检测到的最亮点位置来裁剪图像。

当前可用代码 #

import cv2
import numpy as np
img1 = cv2.imread('opencv_frame_0.png')
img2 = cv2.imread('opencv_frame_1.png')
vis = np.concatenate((img1, img2), axis=1)
cv2.imwrite('combined.png', vis)

# 加载图像并转为灰度图
#image = cv2.imread(args["image"])
image = cv2.imread('combined.png')
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 查找图像中亮度最高区域的坐标
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)

# 显示初步检测结果
cv2.imshow("Naive", image)
# 对图像做高斯模糊后再查找最亮区域
#gray = cv2.GaussianBlur(gray, (args["radius"], args["radius"]), 0)
gray = cv2.GaussianBlur(gray, (41, 41), 0)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
image = orig.copy()
#cv2.circle(image, maxLoc, args["radius"], (255, 0, 255), 2)
cv2.circle(image, maxLoc, 41, (255, 0, 255), 2)
# 显示优化后的检测结果
cv2.imshow("Robust", image)
cv2.waitKey(0)

失败的裁剪尝试代码 #

import numpy as np
import cv2

# 加载图像并转为灰度图
image = cv2.imread('combined.png')
image1 = cv2.imread('combined.png', 0)
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 查找图像中亮度最高区域的坐标
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)

# 显示初步检测结果
#cv2.imshow("Naive", image)

# 对图像做高斯模糊后再查找最亮区域
gray = cv2.GaussianBlur(gray, (41, 41), 0)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
image = orig.copy()
cv2.circle(image, maxLoc, 41, (255, 0, 255), 2)

# 尝试裁剪圆形区域
height,width = image1.shape
mask = np.zeros((height,width), np.uint8)

circle_img = cv2.circle(mask,(maxLoc,(255,255,255))

masked_data = cv2.bitwise_and(image, image, mask=circle_img)

_,thresh = cv2.threshold(mask,1,255,cv2.THRESH_BINARY)

contours = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
x,y,w,h = cv2.boundingRect(contours[0])

crop = masked_data[y:y+h,x:x+w]

cv2.waitKey(0)

解决方案 #

你失败的代码主要是cv2.circle调用语法错误，且未正确处理mask和轮廓提取逻辑。以下是两种可行的实现方式：

方法1：直接提取圆形区域内的像素（推荐） #

无需裁剪外接矩形，直接通过mask过滤出圆形内的像素，计算颜色范围：

import cv2
import numpy as np

# 加载图像
image = cv2.imread('combined.png')
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 高斯模糊后定位最亮区域
gray = cv2.GaussianBlur(gray, (41, 41), 0)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
radius = 41

# 创建填充圆形的mask
height, width = gray.shape
mask = np.zeros((height, width), np.uint8)
cv2.circle(mask, maxLoc, radius, 255, -1)  # -1表示填充圆形

# 提取圆形区域内的所有像素
mask_bool = mask.astype(bool)
pixels = image[mask_bool]

# 计算每个颜色通道的范围
b_min, g_min, r_min = pixels.min(axis=0)
b_max, g_max, r_max = pixels.max(axis=0)

print(f"B通道范围: {b_min} ~ {b_max}")
print(f"G通道范围: {g_min} ~ {g_max}")
print(f"R通道范围: {r_min} ~ {r_max}")

# 可选：可视化裁剪后的圆形区域
x, y = maxLoc
x1 = max(0, x - radius)
y1 = max(0, y - radius)
x2 = min(width, x + radius)
y2 = min(height, y + radius)
cropped_img = image[y1:y2, x1:x2]
cropped_mask = mask[y1:y2, x1:x2]
cropped_masked = cv2.bitwise_and(cropped_img, cropped_img, mask=cropped_mask)

cv2.imshow("Cropped Bright Area", cropped_masked)
cv2.waitKey(0)
cv2.destroyAllWindows()

方法2：修复你原有的裁剪逻辑 #

修正语法错误并适配OpenCV版本差异：

import numpy as np
import cv2

image = cv2.imread('combined.png')
image1 = cv2.imread('combined.png', 0)
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

gray = cv2.GaussianBlur(gray, (41, 41), 0)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
radius = 41
cv2.circle(image, maxLoc, radius, (255, 0, 255), 2)

# 修复mask创建逻辑
height,width = image1.shape
mask = np.zeros((height,width), np.uint8)
cv2.circle(mask, maxLoc, radius, 255, -1)  # 修正参数并填充圆形

masked_data = cv2.bitwise_and(image, image, mask=mask)

_,thresh = cv2.threshold(mask,1,255,cv2.THRESH_BINARY)
# 适配OpenCV4+的findContours返回值
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
x,y,w,h = cv2.boundingRect(contours[0])

crop = masked_data[y:y+h,x:x+w]
cv2.imshow("Cropped", crop)
cv2.waitKey(0)

内容的提问来源于stack exchange，提问作者Philip Gerou