Как обрезать обнаруженный штрих-код с изображения в python?

Question

Я пытаюсь обнаружить штрих-код на изображении и обрезать форму, обнаруженную штрих-кодом на изображении

import numpy as np
import cv2

# load the image and convert it to grayscale
image = cv2.imread("img_00100.jpg")

#resize image
image = cv2.resize(image,None,fx=0.7, fy=0.7, interpolation = cv2.INTER_CUBIC)

#convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

#calculate x & y gradient
gradX = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 1, dy = 0, ksize = -1)
gradY = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 0, dy = 1, ksize = -1)

# subtract the y-gradient from the x-gradient
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)

# blur the image
blurred = cv2.blur(gradient, (3, 3))

# threshold the image
(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)

# construct a closing kernel and apply it to the thresholded image
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)


# perform a series of erosions and dilations
closed = cv2.erode(closed, None, iterations = 4)
closed = cv2.dilate(closed, None, iterations = 4)


# find the contours in the thresholded image, then sort the contours
# by their area, keeping only the largest one
cnts,hierarchy = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
c1 = sorted(cnts, key = cv2.contourArea, reverse = True)[1]

# compute the rotated bounding box of the largest contour
rect = cv2.minAreaRect(c)
x,y,w,h=cv2.boxPoints(rect)
image1=image.copy()
cropped = image1[int(round(y[0])):int(round(y[0]))+int(round(h[0])),int(round(x[0])):int(round(x[0]))+int(round(w[0]))]
box = np.int0(cv2.boxPoints(rect))
cv2.imshow("image",cropped)
# draw a bounding box arounded the detected barcode and display the
# image
cv2.drawContours(image, [box], -1, (0, 255, 0), 3)

#image = cv2.resize(image, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)

cv2.imshow("Image2", image)
cv2.waitKey(0)

из приведенного выше кода, он успешно обнаруживает штрих-код, но не обрезает и не отображает обрезанное изображение. помогите мне с этим:

Изображение

Answer 1

Вам необходимо вычислить значения min и max вершин в переменной box и использовать их для нарезки изображения, чтобы получить обрезанный штрих-код, например:

min_y = int(np.min(box[:,-1]))
max_y = int(np.max(box[:,-1]))
min_x = int(np.min(box[:,0]))
max_x = int(np.max(box[:,0]))
image = image[min_y:max_y, min_x:max_x]

Вот полный пример:
ИСПОЛЬЗОВАНИЕ: python detect_barcode.py --image path_to_image

# import the necessary packages
import numpy as np
import argparse
import imutils
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "path to the image file")
args = vars(ap.parse_args())

# load the image
image = cv2.imread(args["image"])
# convert it to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# compute the Scharr gradient magnitude representation of the images
# in both the x and y direction using OpenCV 2.4
ddepth = cv2.cv.CV_32F if imutils.is_cv2() else cv2.CV_32F
gradX = cv2.Sobel(gray, ddepth = ddepth, dx = 1, dy = 0, ksize = -1)
gradY = cv2.Sobel(gray, ddepth = ddepth, dx = 0, dy = 1, ksize = -1)

# subtract the y-gradient from the x-gradient
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)

# blur and threshold the image
blurred = cv2.blur(gradient, (9, 9))
(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)

# construct a closing kernel and apply it to the thresholded image
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

# perform a series of erosions and dilations
closed = cv2.erode(closed, None, iterations = 4)
closed = cv2.dilate(closed, None, iterations = 4)

# find the contours in the thresholded image, then sort the contours
# by their area, keeping only the largest one
cnts = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]

# compute the rotated bounding box of the largest contour
rect = cv2.minAreaRect(c)
box = cv2.cv.BoxPoints(rect) if imutils.is_cv2() else cv2.boxPoints(rect)
box = np.int0(box)

# draw a bounding box arounded the detected barcode and display the
min_y = int(np.min(box[:,-1]))
max_y = int(np.max(box[:,-1]))
min_x = int(np.min(box[:,0]))
max_x = int(np.max(box[:,0]))
image = image[min_y:max_y, min_x:max_x]
# save cropped image
cv2.imwrite("cropped.jpg", image)

Вывод: