如何从python中的图像中裁剪检测到的条形码？

Question

我正在尝试从图像中检测条形码并从图像中裁剪检测到的条形码形状

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)

从上面的代码中，它成功地检测到了条形码，但它没有裁剪和显示裁剪的图像，请帮助我：

图片

Answer 1

您需要计算box 变量中顶点的min 和max 值，并使用它们对图像进行切片以获得裁剪后的条形码，如下所示：

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]

这是完整的示例：
用法： 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)

输出：