Python：根据暗图像（OpenCV）上的特定颜色在矩形周围绘制轮廓

Question

由于我正在尝试使用Python增强OpenCV的技能，所以我想知道从深色为主的图像中提取特定灰度的最佳方法是什么。

首先，我创建了一个测试图像，以便使用OpenCV测试不同的方法：

假设我要在此图像中提取特定的颜色并为其添加边框。现在，我在中间选择了带有颜色（33、33、34 RGB）的灰色矩形，请参见以下内容：

（这里是没有红色边框的图像，目的是为了测试您的想法：https://i.stack.imgur.com/Zf8Vb.png]

这是我到目前为止已经尝试过的方法，但是效果不佳：

img = cv2.imread(path) #Read input image
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # Convert from BGR to HSV color space
saturation_plane = hsv[:, :, 1] # all black/white/gray pixels are zero, and colored pixels are above zero
_, thresh = cv2.threshold(saturation_plane, 8, 255, cv2.THRESH_BINARY) # Apply threshold on s
contours = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # draw all contours 
contours = contours[0] if len(contours) == 2 else contours[1]
result = img.copy()

for contour in contours:
   (x, y, w, h) = cv2.boundingRect(contour) # compute the bounding box for the contour
   if width is equal to the width of the rectangle i want to extract:
       draw contour

如果矩形的大小不固定怎么办，这样我就无法通过其宽度/高度检测到它？此外，将图像转换为灰度而不是HSV更好吗？我刚刚接触它，我想听听您实现这一目标的方式。

提前感谢。

Answer 1

如果已知特定颜色，则可以从gray = np.all(img == (34, 33, 33), 2)开始。

结果是一个逻辑矩阵，其中True为BGR = (34, 33, 33)，否则为False。注意：OpenCV颜色顺序是BGR，而不是RGB。

• 将逻辑矩阵转换为uint8：gray = gray.astype(np.uint8)*255
• 在findContours图像上使用gray。

如果您要查找蓝色矩形而不是具有非常特定的RGB值的灰色矩形，则将图像转换为HSV不会有用。

以下代码用颜色（33、33、34 RGB）找到最大尺寸的轮廓：

import numpy as np
import cv2

# Read input image
img = cv2.imread('rectangles.png')

# Gel all pixels in the image - where BGR = (34, 33, 33), OpenCV colors order is BGR not RGB
gray = np.all(img == (34, 33, 33), 2)  # gray is a logical matrix with True where BGR = (34, 33, 33).

# Convert logical matrix to uint8
gray = gray.astype(np.uint8)*255

# Find contours
cnts = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # Use index [-2] to be compatible to OpenCV 3 and 4

# Get contour with maximum area
c = max(cnts, key=cv2.contourArea)

x, y, w, h = cv2.boundingRect(c)

# Draw green rectangle for testing
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), thickness = 2)

# Show result
cv2.imshow('gray', gray)
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

---

结果：

灰色：

img：

---

[如果您不知道大多数深色的特定颜色，则可以找到所有轮廓，并搜索灰度值最低的轮廓：

import numpy as np
import cv2

# Read input image
img = cv2.imread('rectangles.png')

# Convert from BGR to Gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Apply threshold on gray
_, thresh = cv2.threshold(gray, 8, 255, cv2.THRESH_BINARY)

# Find contours on thresh
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # Use index [-2] to be compatible to OpenCV 3 and 4

min_level = 255
min_c = []

#Iterate contours, and find the darkest:
for c in cnts:
    x, y, w, h = cv2.boundingRect(c)

    # Ignore contours that are very thin (like edges)
    if w > 5 and h > 5:
        level = gray[y+h//2, x+w//2]  # Get gray level of center pixel

        if level < min_level:
            # Update min_level abd min_c
            min_level = level
            min_c = c

x, y, w, h = cv2.boundingRect(min_c)

# Draw red rectangle for testing
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), thickness = 2)

# Show result
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

结果：