Project practice: parameters of pycharm configuration for credit card digital recognition and how to use opencv in Anaconda

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Preface

I'm in the front anaconda Installed in opencv,(anaconda3-5.2.0+python3.6 install opencv-python3.4.1.15 Steps and processes ), Now want to be in pycharm It is used in opencv, I don't want to install it again , Want to use anaconda Medium , besides ,anaconda There are many other things that have been configured in the .

One 、 New projects

Click on the taskbar file->new project, stay Location Select the folder for the new project （ Create an empty folder in advance ）.
Then it should be the first button by default , Do you want us to use anaconda Things in , So you have to choose anaconda Interpreter .
And then in Interpreter look for anaconda Medium python.exe


One way is , After the project is built as usual , Go again file->setting Choose from anaconda Medium python.exe, I tried , This can only be done with anaconda Medium python, But the bag inside can't be used ,opencv Can not use . Only in pycharm Reload in , Or use pip loading .

Choose when you are building a project anaconda Interpreter , Then I'll go to file->setting see , You will see that it has been selected , And there's a lot of anaconda Your bag can be used .

Reference article ： Want to be in pycharm Use in anaconda Installed opencv package

Two 、 Configuration parameters

I can't find the file name by right clicking here Edit project, So look for the buttons in the picture , Run the file and you will have the information of the page .

Enter the parameters in the red box .

--image The path name where you store your pictures \ Specific picture name --template The path name where you store your pictures \ Specific picture name
Here's the thing to note , The path for storing pictures should not appear in Chinese , Use... In the back cv2.imread Function time , An error will be reported when the program is running .（ Specific reference article ： Path error reporting ）

Reference article ：pychram How to configure corresponding parameters Step two of

3、 ... and 、 Points to note for successful operation

1、 No, imutils package , It needs to be installed ：
pip install imutils
2、cv2.findContours This function , In the new opencv And old opencv It's different . The new version does not have the first parameter , I use the old version .（ Refer to the article here ：python call cv2.findContours Times wrong ）
ref_, refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

Four 、 The overall code

#  Import toolkit 
from imutils import contours
import numpy as np
import argparse
import cv2
#import myutils
 
#  Set parameters 
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="path to input image")
ap.add_argument("-t", "--template", required=True,
                help="path to template OCR-A image")
args = vars(ap.parse_args())
 
#  Specify the type of credit card 
FIRST_NUMBER = {
    "3": "American Express",
    "4": "Visa",
    "5": "MasterCard",
    "6": "Discover Card"
}
 
def sort_contours(cnts, method="left-to-right"):
    reverse = False
    i = 0
 
    if method == "right-to-left" or method == "bottom-to-top":
        reverse = True
 
    if method == "top-to-bottom" or method == "bottom-to-top":
        i = 1
    boundingBoxes = [cv2.boundingRect(c) for c in cnts] # Use the smallest rectangle , Wrap the shapes you find x,y,h,w
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
                                        key=lambda b: b[1][i], reverse=reverse))
 
    return cnts, boundingBoxes
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    dim = None
    (h, w) = image.shape[:2]
    if width is None and height is None:
        return image
    if width is None:
        r = height / float(h)
        dim = (int(w * r), height)
    else:
        r = width / float(w)
        dim = (width, int(h * r))
    resized = cv2.resize(image, dim, interpolation=inter)
    return resized
 
#  Graphic display 
def cv_show(name, img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
 
 
#  Read a template image 
img = cv2.imread(args["template"])
cv_show('img', img)
#  grayscale 
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv_show('ref', ref)
#  Binary image 
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
cv_show('ref', ref)
 
#  Calculate the contour 
# cv2.findContours() The parameters accepted by the function are binary graphs , Black and white （ It's not grayscale ）,cv2.RETR_EXTERNAL Only the outer contour is detected ,cv2.CHAIN_APPROX_SIMPLE Keep only the end coordinates 
#  Back to list Each element in is an outline in the image 
 
ref_, refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
cv2.drawContours(img, refCnts, -1, (0, 0, 255), 3)
cv_show('img', img)
print(np.array(refCnts).shape)
#refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]  #  Sort , From left to right , From top to bottom 
refCnts = sort_contours(refCnts, method="left-to-right")[0]  #  Sort , From left to right , From top to bottom 
digits = {}
 
#  Traverse every contour 
for (i, c) in enumerate(refCnts):
    #  Calculate the circumscribed rectangle and resize To the right size 
    (x, y, w, h) = cv2.boundingRect(c)
    roi = ref[y:y + h, x:x + w]
    roi = cv2.resize(roi, (57, 88))
 
    #  Each number corresponds to each template 
    digits[i] = roi
 
#  Initialize convolution kernel 
rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
 
#  Read input image , Preprocessing 
image = cv2.imread(args["image"])
cv_show('image', image)
image = resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray', gray)
 
#  Hat operation , Highlight brighter areas 
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel)
cv_show('tophat', tophat)
#
gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0,  # ksize=-1 Equivalent to using 3*3 Of 
                  ksize=-1)
 
gradX = np.absolute(gradX)
(minVal, maxVal) = (np.min(gradX), np.max(gradX))
gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
gradX = gradX.astype("uint8")
 
print(np.array(gradX).shape)
cv_show('gradX', gradX)
 
#  By closing （ Inflate first , Corrode again ） Put the numbers together 
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
cv_show('gradX', gradX)
# THRESH_OTSU Will automatically find the right threshold , Suitable for bimodal , The threshold parameter needs to be set to 0
thresh = cv2.threshold(gradX, 0, 255,
                       cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('thresh', thresh)
 
#  Another close operation 
 
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel)  #  Another close operation 
cv_show('thresh', thresh)
 
#  Calculate the contour 
 
thresh_,threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
 
cnts = threshCnts
cur_img = image.copy()
cv2.drawContours(cur_img, cnts, -1, (0, 0, 255), 3)
cv_show('img', cur_img)
locs = []
 
#  Traverse the outline 
for (i, c) in enumerate(cnts):
    #  Calculate rectangle 
    (x, y, w, h) = cv2.boundingRect(c)
    ar = w / float(h)
 
    #  Choose the right area , According to the actual task , It's basically a set of four numbers 
    if ar > 2.5 and ar < 4.0:
 
        if (w > 40 and w < 55) and (h > 10 and h < 20):
            #  The right ones stay 
            locs.append((x, y, w, h))
 
#  Sort the matching contours from left to right 
locs = sorted(locs, key=lambda x: x[0])
output = []
 
#  Go through the numbers in each profile 
for (i, (gX, gY, gW, gH)) in enumerate(locs):
    # initialize the list of group digits
    groupOutput = []
 
    #  Extract each group from the coordinates 
    group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]
    cv_show('group', group)
    #  Preprocessing 
    group = cv2.threshold(group, 0, 255,
                          cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    cv_show('group', group)
    #  Calculate the outline of each group 
    group_,digitCnts, hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
                                                    cv2.CHAIN_APPROX_SIMPLE)
    digitCnts = contours.sort_contours(digitCnts,
                                       method="left-to-right")[0]
 
    #  Calculate each value in each group 
    for c in digitCnts:
        #  Find the outline of the current value ,resize To the right size 
        (x, y, w, h) = cv2.boundingRect(c)
        roi = group[y:y + h, x:x + w]
        roi = cv2.resize(roi, (57, 88))
        cv_show('roi', roi)
 
        #  Calculate the match score 
        scores = []
 
        #  Calculate each score in the template 
        for (digit, digitROI) in digits.items():
            #  Template matching 
            result = cv2.matchTemplate(roi, digitROI,
                                       cv2.TM_CCOEFF)
            (_, score, _, _) = cv2.minMaxLoc(result)
            scores.append(score)
 
        #  Get the most appropriate number 
        groupOutput.append(str(np.argmax(scores)))
 
    #  Draw out 
    cv2.rectangle(image, (gX - 5, gY - 5),
                  (gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)
    cv2.putText(image, "".join(groupOutput), (gX, gY - 15),
                cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
 
    #  Get the results 
    output.extend(groupOutput)
 
#  Print the results 
print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #: {}".format("".join(output)))
cv2.imshow("Image", image)
cv2.waitKey(0)