Python - 从图像中检测二维码并使用 OpenCV 进行裁剪

Question

我正在使用 Python(3.7) 和 OpenCV 处理一个项目，其中我有一个文档的图像(使用相机捕获)，上面放置了 QR 码。

此二维码有 6 个变量，分别为:

二维码图片尺寸

顶部

右

底部

左

单位

最新更新:

Here are the steps I need to perform in the same order:

1. Detect the qr code & decode it to read size values
2. So, if the size of QR-code(image) is not equal to the size which is mentioned inside it then scale the image to equal both size values.
3. Then crop the image towards all sides from QR code image according to the values mentioned inside qr code.

I have tried this code:

def decodeAndCrop(inputImage):
    print(str(inputImage))
    image = cv2.imread(str(inputImage))
    qrCodeDetector = cv2.QRCodeDetector()
    decodedText, points, _ = qrCodeDetector.detectAndDecode(image)
    qr_data = decodedText.split(",")
    print("qr data from fucntion: {}".format(qr_data))
    if points is not None:
        pts = len(points)
    # print(pts)
    for i in range(pts):
        nextPointIndex = (i + 1) % pts
        if str(inputImage) == "scaled_img.jpg":
            cv2.line(
                image,
                tuple(points[i][0]),
                tuple(points[nextPointIndex][0]),
                (255, 0, 0),
                5,
            )
        print(points[i][0])
        width = int(
            math.sqrt(
                (points[0][0][0] - points[1][0][0]) ** 2
                + (points[0][0][1] - points[1][0][1]) ** 2
            )
        )
        height = int(
            math.sqrt(
                (points[1][0][0] - points[2][0][0]) ** 2
                + (points[1][0][1] - points[2][0][1]) ** 2
            )
        )
        print("height and width after scaling: {} {}".format(height, width))
        if not str(inputImage) == "scaled_img.jpg":
            scaled_img = None
            if width == qr_data[0] and height == qr_data[0]:
                print("Sizes are equal")
                # Add the extension values to points and crop
                y = int(points[0][0][1]) - int(qr_data[1])
                x = int(points[0][0][0]) - int(qr_data[4])
                roi = image[
                    y : y + height + int(qr_data[3]), x : x + width + int(qr_data[2])
                ]
                scaled_img = cv2.imwrite("scaled_img.jpg", roi)
                return scaled_img
            else:
                print(
                    "Width and height  "
                    + str(width)
                    + "x"
                    + str(height)
                    + "  not equal to "
                    + str(qr_data[0])
                    + "x"
                    + str(qr_data[0])
                )
                if height > int(qr_data[0]):
                    scale_width = int(width) - int(qr_data[0])
                    scale_height = int(height) - int(qr_data[0])
                    print(f"scaled width: {scale_width} scaled height: {scale_height}")
                    dimension = (scale_width, scale_height)
                    scaled_img = cv2.resize(
                        image, dimension, interpolation=cv2.INTER_AREA
                    )
                    print("new img dims: {}".format(scaled_img.shape))
                    cv2.imshow("scaled image:", scaled_img)
                    cv2.imwrite("scaled_img.jpg", scaled_img)
                elif height < int(qr_data[0]):
                    scale_width = int(qr_data[0]) - width
                    scale_height = int(qr_data[0] - height)
                    print(f"scaled width: {scale_width} scaled height: {scale_height}")
                    dimension = (scale_width, scale_height)
                    scaled_img = cv2.resize(
                        image, dimension, interpolation=cv2.INTER_AREA
                    )
                    print("new img dims: {}".format(scaled_img.shape))
                    cv2.imshow("scaled image:", scaled_img)
                    cv2.imwrite("scaled_img.jpg", scaled_img)
                    cv2.imshow("final output:", roi)
                return scaled_img

        else:
            y = int(points[0][0][1]) - int(qr_data[1])
            x = int(points[0][0][0]) - int(qr_data[4])
            print(" x and y")
            print(x)
            print(y)
            roi = image[
                y : y + height + int(qr_data[3]), x : x + width + int(qr_data[2])
            ]
            final_img = cv2.imwrite("finalized_image.jpg", roi)
            cv2.imshow("finalized image:", final_img)
            return final_img


if __name__ == "__main__":
    image_to_crop = decodeAndCrop("example_input_1.jpg")
    final_image = decodeAndCrop("scaled_img.jpg")
    cv2.imshow("Cropped:", image_to_crop)
    # cv2.imshow("Final: ", final_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

The code above gives an error as: final_img = cv2.imwrite("finalized_image.jpg", roi) cv2.error: OpenCV(4.2.0) /Users/travis/build/skvark/opencv-python/opencv/modules/imgcodecs/src/loadsave.cpp:715: error: (-215:Assertion failed) !_img.empty() in function 'imwrite'

最新更新结束:

二维码的解码信息示例为:100, 20, 40, 60, 20, px

现在，我需要从这个文档图像中检测二维码，在第一步中，我需要将捕获的文档图像中二维码的大小与解码信息中提到的大小进行比较，例如，如果在捕获的图像中QR 图像的大小为 90X90 像素，解码信息的大小为 100X100 像素，我们需要进行比较。

然后，在第二步中，我必须相应地使用 Top、Right、Bottom 和 Left 变量来裁剪完整的图像。根据上面的示例，我们需要将图像从检测到的二维码位置裁剪为 20 像素顶部、40 像素右侧、60 像素底部和 20 像素右侧。我在下面添加了一个示例图片。

我已经完成了对二维码信息的解码，但是如何将检测到的二维码区域作为单独的图像并将其大小与提到的大小进行比较，然后相应地裁剪图像？

Here's what I have tried so far:

import cv2

image = cv2.imread('/Users/abdul/PycharmProjects/QScanner/images/second.jpg')

qrCodeDetector = cv2.QRCodeDetector()
decodedText, points, _ = qrCodeDetector.detectAndDecode(image)
qr_data = decodedText.split(',')
qr_size = qr_data[0]
top = qr_data[1]
right = qr_data[2]
bottom = qr_data[3]
left = qr_data[4]

print(f'Size: {qr_size}' + str(qr_data[5]))
print(f'Top: {top}')
print(f'Right: {right}')
print(f'Bottom: {bottom}')
print(f'Left: {left}')
if points is not None:
    pts = len(points)
    print(pts)
    for i in range(pts):
        nextPointIndex = (i+1) % pts
        cv2.line(image, tuple(points[i][0]), tuple(points[nextPointIndex][0]), (255,0,0), 5)
        print(points[i][0])
    print(decodedText)    
    cv2.imshow("Image", image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
else:
    print("QR code not detected")

Here's an example Image:

and here's a sample of input image:

Answer 1

这是使用阈值、形态学操作和轮廓过滤的简单方法。

获取二值图像。 Load image , grayscale , Gaussian blur , Otsu's threshold

连接各个 QR 轮廓。 使用 cv2.getStructuringElement() 创建一个矩形结构内核然后执行 morphological operations与 cv2.MORPH_CLOSE .

过滤二维码。 Find contours
并使用 contour approximation 过滤, contour area , 和 aspect ratio .

检测到的二维码

提取的二维码

从这里您可以将二维码与您的引用信息进行比较
代码

import cv2
import numpy as np

# Load imgae, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.jpg')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (9,9), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Morph close
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)

# Find contours and filter for QR code
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, 0.04 * peri, True)
    x,y,w,h = cv2.boundingRect(approx)
    area = cv2.contourArea(c)
    ar = w / float(h)
    if len(approx) == 4 and area > 1000 and (ar > .85 and ar < 1.3):
        cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 3)
        ROI = original[y:y+h, x:x+w]
        cv2.imwrite('ROI.png', ROI)

cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.imshow('image', image)
cv2.imshow('ROI', ROI)
cv2.waitKey()