Python: Contour around rectangle based on specific color on a dark image (OpenCV)

Question

since I'm trying to enhance my skills with OpenCV in Python, I would like to know what's the best way of extracting a specific gray tone out of a image with mostly dark colors.

To start of, I created a test image in order to test different methods with OpenCV:

Lets say I want to extract a specific color in this image and add a border to it. For now I chose the gray rectangle in the middle with the color (33, 33, 34 RGB), see following:

(Here's the image without the red border in order you want to test your ideas: https://i.sstatic.net/Zf8Vb.png)

This is what I've tried so far, but it's not quite working:

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

What if the size of the rectangle is not fixed, so that I won't be able to detect it through its width/height? Moreover, is it better to convert the image into a gray scale instead of HSV? I'm just new to it and I would like to hear your way of achieving this.

Thanks in advance.

Answer 1

In case the specific color is known, you may start with gray = np.all(img == (34, 33, 33), 2).

The result is a logical matrix with True where BGR = (34, 33, 33), and False where it is not.
Note: OpenCV color ordering is BGR and not RGB.

• Convert the logical matrix to uint8: gray = gray.astype(np.uint8)*255
• Use findContours on gray image.

Converting the image to HSV in not going useful in case you want to find the blue rectangle, but not a gray rectangle with very specific RGB values.

The following code finds the contour with maximum size with color (33, 33, 34 RGB):

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()

---

Result:

gray:

img:

---

In case you don't know the specific color of the mostly dark colors, you may find all contours, and search for the one with the lowest gray value:

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()

Result: