Find box in the image and save as an image cv2-CodePudding

i am new in computer vision i want to create a program which help me to detect box in the image and save as an image.

and etc... i try some code but did not get my result. here is my code and its output.

import cv2
# Load iamge, grayscale, adaptive threshold
image = cv2.imread('image.jpeg')
result = image.copy()
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,51,9)

# Fill rectangular contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(thresh, [c], -1, (255,255,255), -1)

# Morph open
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=4)

# Draw rectangles
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (x, y), (x   w, y   h), (36,255,12), 3)

cv2.imshow('thresh', thresh)
cv2.imshow('opening', opening)
cv2.imshow('image', image)
cv2.waitKey()

output:

code output

CodePudding user response：

All you need to do is simply first remove the outermost white area, that is, make it black so that we can detect the boxes without any issues using the cv2.RETR_EXTERNAL flag as they are not touching. Then we'll just extract the boxes one by one.

To remove the outmost area, I have used the point polygon test of the contours. If the point (1, 1) lies inside or on a contour, it is not drawn and every other contour will be drawn on a new image. From this new image, I have read the box contours and extracted them.

import cv2
import numpy as np

img = cv2.imread("2lscp.png", cv2.IMREAD_GRAYSCALE)
ret, img = cv2.threshold(img, 50, 255, cv2.THRESH_BINARY cv2.THRESH_OTSU)

Contours = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2]

newImg = np.zeros(img.shape, dtype=np.uint8)
for Contour in Contours:
    if cv2.pointPolygonTest(Contour, (1, 1), False) == -1:
        cv2.drawContours(newImg, [Contour], -1, 255, 1)

Contours = cv2.findContours(newImg, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
for Contour in Contours:
    [x, y, w, h] = cv2.boundingRect(Contour)

    cv2.imshow("box extracted", img[y:y h, x:x w])
    cv2.waitKey(0)

cv2.destroyAllWindows()

CodePudding user response：

This case seems particularly simple because the image is quasi-binary. Detect the contours of the white regions and select those that have an area like 10 to 15% of the whole image. These are the desired boxes. Then fit a rectangle or rotated rectangle.

No need for additional processing.