How to extract bottom part from engineering drawing image using python?

Question

My input image

To extract highlighted part

My desired output

Please someone help and give me a suggestion. My images looks like this. This is just sample one. I need to crop the bottom template part and do OCR. I have attached my desire output picture. Please have a look. How to implement it using python?

PS: The sheet size will differ and there may the chance of template to dislocate. but mostly it will be in lower left corner

Answer 1

Here's a potential approach:

1. Obtain binary image. We convert to grayscale, Gaussian blur, then Otsu's threshold

2. Fill in potential contours. We iterate through contours and filter using contour approximation to determine if they are rectangular.

3. Perform morphological operations. We morph open to remove non-rectangular contours using a rectangular kernel.

4. Filter and extract desired contour. Find contours and filter using contour approximation, aspect ratio, and contour area to isolate the desired contour. Then extract using Numpy slicing.

---

1. Binary image

2. Filled in contours

3. Morphological operation to remove non-rectangular contours

4. Desired contour highlighted in green

Extracted ROI

Code

import cv2

# Grayscale, blur, and threshold
image = cv2.imread('1.png')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Fill in potential contours
cnts = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.05 * peri, True)
    if len(approx) == 4:
        cv2.drawContours(thresh, [c], -1, (255,255,255), -1)

# Remove non rectangular contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40,10))
close = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)

# Filtered for desired contour
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.05 * peri, True)
    x,y,w,h = cv2.boundingRect(approx)
    aspect_ratio = w / float(h)
    area = cv2.contourArea(approx)
    if len(approx) == 4 and w > h and aspect_ratio > 2.75 and area > 45000:
        cv2.drawContours(image, [c], -1, (36,255,12), -1)
        ROI = original[y:y+h, x:x+w]

cv2.imwrite('image.png', image)
cv2.imwrite('ROI.png', ROI)
cv2.waitKey()