I am trying to recognize six digits from a meter using python-OpenCV. It's surprising how incredibly hard it is to set morphological operations working in the right way, given the time I have spent adjusting the focus/distance of my raspberry pi camera to the meter screen and I even have bought a separate led lamp to have as much uniform light as possible. This is a template image
and I've tried using and adjusting the code from these two sources: 
import cv2
import numpy as np
# load image
img = cv2.imread("digits.jpg")
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# blur
blur = cv2.GaussianBlur(gray, (0,0), sigmaX=51, sigmaY=51)
# divide
divide = cv2.divide(gray, blur, scale=255)
# threshold
thresh = cv2.threshold(divide, 235, 255, cv2.THRESH_BINARY)[1]
# apply morphology
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11,11))
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11,11))
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)
# write result to disk
cv2.imwrite("digits_division.jpg", divide)
cv2.imwrite("digits_threshold.jpg", thresh)
cv2.imwrite("digits_morph.jpg", morph)
# display it
cv2.imshow("divide", divide)
cv2.imshow("thresh", thresh)
cv2.imshow("morph", morph)
cv2.waitKey(0)
cv2.destroyAllWindows()
Division normalized image:
Thresholded image:
Morphology processed image:
You can then clean up further by getting contours and removing small contours and very long horizontal contours.
CodePudding user response：
The key to getting this working is cleaning the image up which I have done to a good enough level to get it to work. I've done this using scikit image library.
I then look at certain squares on the image and take an average reading from that area. On the right hand-side image I've marked some of the locations with red squares.
My script I used to get this result:
import numpy as np
from pathlib import Path
import imageio.v3 as iio
import skimage.filters as skif
from skimage.color import rgb2gray
from skimage.util import img_as_ubyte
from skimage.restoration import denoise_bilateral
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import matplotlib.cm as cm
threshold = 125
digit_loc = [1600, 1300, 1000, 730, 420, 155]
size = 20
x_mid = 80
x_right = 160
y_top = 130
y_mt = 250
y_mid = 380
y_bm = 520
y_bot = 630
def img_with_threshold(orig_img):
block_size = 255
local_thresh = skif.threshold_local(
orig_img,
block_size,
method="mean",
)
binary_local = orig_img > local_thresh
u8_val = img_as_ubyte(binary_local)
return u8_val
def image_denoise(orig_img):
return denoise_bilateral(orig_img, win_size=10, bins=10, )
def plot_imgs(orig_img, mod_img):
# Display the image
fig, axes = plt.subplots(1, 2, figsize=(8, 8), sharex=True, sharey=True)
ax = axes.ravel()
ax[0].imshow(orig_img, cmap=cm.Greys_r)
ax[1].imshow(mod_img, cmap=cm.Greys_r)
# Create a Rectangle patch
for x_loc in digit_loc:
rect1 = Rectangle((x_loc x_mid, y_top), size, size, linewidth=1, edgecolor='r', facecolor='none')
rect2 = Rectangle((x_loc, y_mt), size, size, linewidth=1, edgecolor='r', facecolor='none')
rect3 = Rectangle((x_loc x_right, y_mt), size, size, linewidth=1, edgecolor='r', facecolor='none')
rect4 = Rectangle((x_loc x_mid, y_mid), size, size, linewidth=1, edgecolor='r', facecolor='none')
rect5 = Rectangle((x_loc, y_bm), size, size, linewidth=1, edgecolor='r', facecolor='none')
rect6 = Rectangle((x_loc x_right, y_bm), size, size, linewidth=1, edgecolor='r', facecolor='none')
rect7 = Rectangle((x_loc x_mid, y_bot), size, size, linewidth=1, edgecolor='r', facecolor='none')
# Add the patch to the Axes
ax[1].add_patch(rect1)
ax[1].add_patch(rect2)
ax[1].add_patch(rect3)
ax[1].add_patch(rect4)
ax[1].add_patch(rect5)
ax[1].add_patch(rect6)
ax[1].add_patch(rect7)
plt.show()
def seg_to_digit(segments, location):
digit_values = {0b1110111: 0,
0b0010010: 1,
0b1011101: 2,
0b1011011: 3,
0b0111010: 4,
0b1101011: 5,
0b1101111: 6,
0b1110010: 7,
0b1111111: 8,
0b1111011: 9,
}
result = int("".join(["1" if i < threshold else "0" for i in segments]), 2)
# print("score:", result)
return digit_values.get(result, 0) * 10 ** location
def get_digit(location, mod_img):
"""
a
b c
d
e f
g
"""
x_loc = digit_loc[location]
m_loc = (x_loc x_mid, x_loc x_mid size)
l_loc = (x_loc, x_loc size)
r_loc = (x_loc x_right, x_loc x_right size)
seg_a = np.average(mod_img[y_top:y_top size, m_loc[0]:m_loc[1]])
seg_b = np.average(mod_img[y_mt:y_mt size, l_loc[0]:l_loc[1]])
seg_c = np.average(mod_img[y_mt:y_mt size, r_loc[0]:r_loc[1]])
seg_d = np.average(mod_img[y_mid:y_mid size, m_loc[0]:m_loc[1]])
seg_e = np.average(mod_img[y_bm:y_bm size, l_loc[0]:l_loc[1]])
seg_f = np.average(mod_img[y_bm:y_bm size, r_loc[0]:r_loc[1]])
seg_g = np.average(mod_img[y_bot:y_bot size, m_loc[0]:m_loc[1]])
segments = [seg_a, seg_b, seg_c, seg_d, seg_e, seg_f, seg_g]
# print(f"x loc: {x_loc}, digit index: {location}, segment values: {segments}")
# create an integer from the bits
# print('value:', result)
return seg_to_digit(segments, location)
def main():
data_dir = Path(__file__).parent.joinpath('data')
meter_img = data_dir.joinpath('meter_test.jpg')
img = iio.imread(meter_img)
gray_img = img_as_ubyte(rgb2gray(img))
img_result = image_denoise(gray_img)
img_result1 = img_with_threshold(img_result)
reading = 0
for dig_loc in range(6):
reading = get_digit(dig_loc, img_result1)
print(f"{reading:>21}")
print("Final reading:", reading)
plot_imgs(gray_img, img_result1)
if __name__ == '__main__':
main()
This gave the following output:
7
77
677
4677
24677
924677
Final reading: 924677