CODEWITHSUNDEEP
pythonimageopencvimage-processing
Aryagm
Aryagm

Reputation: 550

Grayscaled image has dark lower border

I am using opencv to take images using my webcam.

 cam = cv2.VideoCapture(0)
 cv2.namedWindow("Handwritten Number Recognition")
 img_counter = 0

    while True:
        ret, frame = cam.read()
        if not ret:
            print("failed to grab frame")
            break
        cv2.imshow("Handwritten Number Recognition", frame)

        k = cv2.waitKey(1)
        if k%256 == 27:
            # ESC pressed
            print("Prediction is underway...")
            break
        elif k%256 == 32:
            # SPACE pressed
            img_name = "opencv_frame_{}.png".format(img_counter)
            cv2.imwrite(img_name, frame)
            print("Image taken!")
            img_counter += 1

    cam.release()
    cv2.destroyAllWindows()

I then convert the image into grayscale and downsize it:

user_test = img_name
col = Image.open(user_test)
gray = col.convert('L')
bw = gray.point(lambda x: 0 if x<100 else 255, '1')
bw.save("bw_image.jpg")
bw
img_array = cv2.imread("bw_image.jpg", cv2.IMREAD_GRAYSCALE)
img_array = cv2.bitwise_not(img_array)
plt.imshow(img_array, cmap = plt.cm.binary)
plt.show()
img_size = 28
new_array = cv2.resize(img_array, (img_size,img_size))
final_array = new_array.reshape(1,-1)
plt.imshow(new_array, cmap = plt.cm.binary)
plt.show()

But the images have a very dark patch in the bottom which hampers the predictions I want to make with my data: enter image description here

Original image: enter image description here

What can I do to get past this problem? Interestingly this only happens if I click the image using opencv. If I use an image clicked through the same webcam but through the camera appliucation the error is not visible (Adding path of the image for preprocessing).

Upvotes: 0

Views: 249

Answers (3)

fmw42
fmw42

Reputation: 53081

Here is one way to do that in Python/OpenCV using division normalization, thresholding and some morphology.

Input:

enter image description here

import cv2
import numpy as np

# read the image
img = cv2.imread('five.jpg')

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

# blur
smooth = cv2.GaussianBlur(gray, (555,555), 0)

# divide smooth by gray image
division = cv2.divide(smooth, gray, scale=255)

# invert
division = 255 - division

# threshold
thresh = cv2.threshold(division, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

# add white border to help morphology close
border = cv2.copyMakeBorder(thresh, 60,60,60,60, cv2.BORDER_CONSTANT, value=(255,255,255))
hh, ww = border.shape

# morphology close
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (29,29))
result = cv2.morphologyEx(border, cv2.MORPH_CLOSE, kernel)

# remove border
result = result[60:hh-60, 60:ww-60]

# save results
cv2.imwrite('five_division_threshold.jpg',result)

# show results
cv2.imshow('smooth', smooth)  
cv2.imshow('division', division)  
cv2.imshow('result', result)  
cv2.waitKey(0)
cv2.destroyAllWindows()

Result:

enter image description here

Upvotes: 2

Baraa
Baraa

Reputation: 1526

You have two options:

  1. Choosing a better global threshold for the gray values. This is the easier less generic solution. Normally, people would choose the Otsu method to automatically select the optimal threshold. Have a look at: Opencv Thresholding Tutorial

    threshold, dst_img = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)

  2. Using an adaptive threshold. Adaptive simply means using a calculated threshold for each sliding window location based on some criteria. Have a look at: Niblack's Binarization methods

Using option one:

img = cv2.imread("thresh.jpg", cv2.IMREAD_GRAYSCALE)
threshold, img = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)
cv2.imwrite("thresh_bin.jpg", img)

Output: enter image description here

Upvotes: 3

Farhan Alfin
Farhan Alfin

Reputation: 94

this problem because of used thresholding method

bw = gray.point(lambda x: 0 if x<100 else 255, '1')

to solve this you can change the low limit value (100) to 75 or using opencv auto threshold

the, bw = cv2.threshold(gray_img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

Upvotes: 2

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