Find contours based on edges

问题

I want to detect the contours of an equipment label. Although the code runs correctly, it never quite detects the contours of the label.

Original Image

Using this code:

import numpy as np
import cv2
import imutils #resizeimage
import pytesseract # convert img to string
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

# Read the image file
image = cv2.imread('Car Images/5.JPG')

# Resize the image - change width to 500
image = imutils.resize(image, width=500)


# Display the original image
cv2.imshow("Original Image", image)
cv2.waitKey(0)

# RGB to Gray scale conversion
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("1 - Grayscale Conversion", gray)
cv2.waitKey(0)

# Noise removal with iterative bilateral filter(removes noise while preserving edges)
gray = cv2.bilateralFilter(gray, 11, 17, 17)
cv2.imshow("2 - Bilateral Filter", gray)
cv2.waitKey(0)

# Find Edges of the grayscale image
edged = cv2.Canny(gray, 170, 200)
cv2.imshow("3 - Canny Edges", edged)
cv2.waitKey(0)

# Find contours based on Edges
cnts, new  = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

# Create copy of original image to draw all contours
img1 = image.copy()
cv2.drawContours(img1, cnts, -1, (0,255,0), 3)
cv2.imshow("4- All Contours", img1)
cv2.waitKey(0)

#sort contours based on their area keeping minimum required area as '30' (anything smaller than this will not be considered)
cnts=sorted(cnts, key = cv2.contourArea, reverse = True)[:30]
NumberPlateCnt = None #we currently have no Number plate contour

# Top 30 Contours
img2 = image.copy()
cv2.drawContours(img2, cnts, -1, (0,255,0), 3)
cv2.imshow("5- Top 30 Contours", img2)
cv2.waitKey(0)

# loop over our contours to find the best possible approximate contour of number plate
count = 0
idx =7
for c in cnts:
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)
        # print ("approx = ",approx)
        if len(approx) == 4:  # Select the contour with 4 corners
            NumberPlateCnt = approx #This is our approx Number Plate Contour

            # Crop those contours and store it in Cropped Images folder
            x, y, w, h = cv2.boundingRect(c) #This will find out co-ord for plate
            new_img = gray[y:y + h, x:x + w] #Create new image
            cv2.imwrite('Cropped Images-Text/' + str(idx) + '.png', new_img) #Store new image
            idx+=1

            break


# Drawing the selected contour on the original image
#print(NumberPlateCnt)
cv2.drawContours(image, [NumberPlateCnt], -1, (0,255,0), 3)
cv2.imshow("Final Image With Number Plate Detected", image)
cv2.waitKey(0)

Cropped_img_loc = 'Cropped Images-Text/7.png'
cv2.imshow("Cropped Image ", cv2.imread(Cropped_img_loc))

# Use tesseract to covert image into string
text = pytesseract.image_to_string(Cropped_img_loc, lang='eng')
print("Equipment Number is :", text)

cv2.waitKey(0) #Wait for user input before closing the images displayed

Displayed output

Is there a better way to narrow down the contour to the equipment label?

Here is the code for your reference on github: https://github.com/AjayAndData/Licence-plate-detection-and-recognition---using-openCV-only/blob/master/Car%20Number%20Plate%20Detection.py

回答1:

I think this code may help you

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('C:/Users/DELL/Desktop/download (5).png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

corners = cv2.goodFeaturesToTrack(gray,60,0.001,10)

corners = np.int0(corners)

for i in corners:
    x,y = i.ravel()
    cv2.circle(img,(x,y),0,255,-1)
    coord = np.where(np.all(img == (255, 0, 0),axis=-1))

plt.imshow(img)

plt.show()

来源：https://stackoverflow.com/questions/61203364/find-contours-based-on-edges

标签

python

OpenCV

image-processing

contour