Python - cv2 find Contours

Question

I would like to find all the big elements in the document, but I do not know how to control the size (the document is downloaded from the Internet :))

I have a document

And I wrote a simple code

import cv2
import pytesseract

image = cv2.imread('2.png')

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (7, 7), 0)
thresh = cv2.threshold(
    blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

kernal = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 50))
dilate = cv2.dilate(thresh, kernal, iterations=1)

cv2.imwrite('1_dilated.png', dilate)

cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

cnts = cnts[0] if len(cnts) == 2 else cnts[1]

cnts = sorted(cnts, key=lambda x: cv2.boundingRect(x)[1])

for c in cnts:
    x, y, w, h = cv2.boundingRect(c)
    if h > 100 and w > 100:
        roi = image[y:y+h, x:x+w]
        cv2.rectangle(image, (x, y), (x+w, y+h), (36, 255, 12), 2)
        # ocr = pytesseract.image_to_string(roi)
        # print(ocr)
cv2.imwrite('1_boxes4.png', image)

But only detects it

And I would like this

How to control the size of the detected area ?

Thank you very much for all your comments

Answer 1

You are close, but you need to increase the number of iterations of the dilate operation. Also, a rectangular structuring element might help better forming the blobs of text. Let's check out some possible improvements of your code:

# imports:
import cv2
import numpy as np

# Set image path
imagePath = "D://opencvImages//"
imageName = "F74Yq.png"    

# Read image:
inputImage = cv2.imread(imagePath + imageName)

# Store a deeep copy for results:
inputCopy = inputImage.copy()

# Convert BGR to grayscale:
grayInput = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)

# Threshold via Otsu
_, binaryImage = cv2.threshold(grayInput, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

The first part produces the binary image of the input image, there's nothing fancy going on here - just a direct thresholding via Otsu 's method. This is the binary image obtained:

Now, let's apply the dilate operation. Let's use a 9 x 9 rectangular kernel and set the number of iterations to 5 . Gotta be careful you don't dilate too much, because blobs of text from different portions of the document could end up joined:

# Set kernel (structuring element) size:
kernelSize = (9, 9)

# Set operation iterations:
opIterations = 5

# Get the structuring element:
morphKernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernelSize)

# Perform Dilate:
dilateImage = cv2.morphologyEx(binaryImage, cv2.MORPH_DILATE, morphKernel, None, None, opIterations, cv2.BORDER_REFLECT101)

This is the result:

Ok, now let's just detect external contours and get their bounding boxes so we can draw rectangles around the target areas. Note that I'm drawing the rectangles on a deep copy of the input:

# Find the contours on the binary image:
contours, hierarchy = cv2.findContours(dilateImage, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Look for the outer bounding boxes (no children):
for _, c in enumerate(contours):

    # Get the contours bounding rectangle:
    boundRect = cv2.boundingRect(c)

    # Get the dimensions of the bounding rectangle:
    rectX = boundRect[0]
    rectY = boundRect[1]
    rectWidth = boundRect[2]
    rectHeight = boundRect[3]

    # Set bounding rectangle:
    color = (0, 0, 255)
    cv2.rectangle( inputCopy, (int(rectX), int(rectY)),
                   (int(rectX + rectWidth), int(rectY + rectHeight)), color, 5 )

    cv2.imshow("Bounding Rectangles", inputCopy)
    cv2.waitKey()

This is the final result: