Python: How to cut out an area with specific color from image (OpenCV, Numpy)

Updated Answer

I have updated my answer to cope with specks of noisy outlier pixels of the same colour as the yellow box. This works by running a 3x3 median filter over the image first to remove the spots:

#!/usr/bin/env python3

import numpy as np
from PIL import Image, ImageFilter

# Open image and make into Numpy array
im = Image.open('image.png').convert('RGB')
na = np.array(im)
orig = na.copy()    # Save original

# Median filter to remove outliers
im = im.filter(ImageFilter.MedianFilter(3))

# Find X,Y coordinates of all yellow pixels
yellowY, yellowX = np.where(np.all(na==[247,213,83],axis=2))

top, bottom = yellowY[0], yellowY[-1]
left, right = yellowX[0], yellowX[-1]
print(top,bottom,left,right)

# Extract Region of Interest from unblurred original
ROI = orig[top:bottom, left:right]

Image.fromarray(ROI).save('result.png')

Original Answer

Ok, your yellow colour is rgb(247,213,83), so we want to find the X,Y coordinates of all yellow pixels:

#!/usr/bin/env python3

from PIL import Image
import numpy as np

# Open image and make into Numpy array
im = Image.open('image.png').convert('RGB')
na = np.array(im)

# Find X,Y coordinates of all yellow pixels
yellowY, yellowX = np.where(np.all(na==[247,213,83],axis=2))

# Find first and last row containing yellow pixels
top, bottom = yellowY[0], yellowY[-1]
# Find first and last column containing yellow pixels
left, right = yellowX[0], yellowX[-1]

# Extract Region of Interest
ROI=na[top:bottom, left:right]

Image.fromarray(ROI).save('result.png')

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You can do the exact same thing in Terminal with ImageMagick:

# Get trim box of yellow pixels
trim=$(magick image.png -fill black +opaque "rgb(247,213,83)" -format %@ info:)

# Check how it looks
echo $trim
251x109+101+220

# Crop image to trim box and save as "ROI.png"
magick image.png -crop "$trim" ROI.png

If still using ImageMagick v6 rather than v7, replace magick with convert.

What I see is dark and light gray areas on sides and top, a white area, and a yellow rectangle with gray triangles inside the white area.

The first stage I suggest is converting the image from RGB color space to HSV color space.
The S color channel in HSV space, is the "color saturation channel".
All colorless (gray/black/white) are zeros and yellow pixels are above zeros in the S channel.

Next stages:

• Apply threshold on S channel (convert it to binary image).
  The yellow pixels goes to 255, and other goes to zero.
• Find contours in thresh (find only the outer contour - only the rectangle).
• Invert polarity of the pixels inside the rectangle.
  The gray triangles become 255, and other pixels are zeros.
• Find contours in thresh - find the gray triangles.

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Here is the code:

import numpy as np
import cv2

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

# Convert from BGR to HSV color space
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Get the saturation plane - all black/white/gray pixels are zero, and colored pixels are above zero.
s = hsv[:, :, 1]

# Apply threshold on s - use automatic threshold algorithm (use THRESH_OTSU).
ret, thresh = cv2.threshold(s, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Find contours in thresh (find only the outer contour - only the rectangle).
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # [-2] indexing takes return value before last (due to OpenCV compatibility issues).

# Mark rectangle with green line
cv2.drawContours(img, contours, -1, (0, 255, 0), 2)

# Assume there is only one contour, get the bounding rectangle of the contour.
x, y, w, h = cv2.boundingRect(contours[0])

# Invert polarity of the pixels inside the rectangle (on thresh image).
thresh[y:y+h, x:x+w] = 255 - thresh[y:y+h, x:x+w]

# Find contours in thresh (find the triangles).
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # [-2] indexing takes return value before last (due to OpenCV compatibility issues).

# Iterate triangle contours
for c in contours:
    if cv2.contourArea(c) > 4:  #  Ignore very small contours
        # Mark triangle with blue line
        cv2.drawContours(img, [c], -1, (255, 0, 0), 2)

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

---

S color channel in HSV color space:

thresh - S after threshold:

thresh after inverting polarity of the rectangle:

Result (rectangle and triangles are marked):

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Update:

In case there are some colored dots on the background, you can crop the largest colored contour:

import cv2
import imutils  # https://pypi.org/project/imutils/

# Read input image
img = cv2.imread('img2.png')

# Convert from BGR to HSV color space
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Get the saturation plane - all black/white/gray pixels are zero, and colored pixels are above zero.
s = hsv[:, :, 1]

cv2.imwrite('s.png', s)

# Apply threshold on s - use automatic threshold algorithm (use THRESH_OTSU).
ret, thresh = cv2.threshold(s, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Find contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = imutils.grab_contours(cnts) 

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

# Get bounding rectangle
x, y, w, h = cv2.boundingRect(c)

# Crop the bounding rectangle out of img
out = img[y:y+h, x:x+w, :].copy()

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Result: