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I am currently working on lines extraction from a binary image. I initially performed a few image processing steps including threshold segmentation and obtained the following binary image.
As can be seen in the binary image the lines are splitted or broken. And I wanted to join the broken line as shown in the image below marked in red. I marked the red line manually for a demonstration.
FYI, I used the following code to perform the preprocessing.
img = cv2.imread('original_image.jpg') # loading image
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # coverting to gray scale
median_filter = cv2.medianBlur (gray_image, ksize = 5) # median filtering
th, thresh = cv2.threshold (median_filter, median_filter.mean(), 255, cv2.THRESH_BINARY) # theshold segmentation
# small dots and noise removing
nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(thresh, None, None, None, 8, cv2.CV_32S)
areas = stats[1:,cv2.CC_STAT_AREA]
result = np.zeros((labels.shape), np.uint8)
min_size = 150
for i in range(0, nlabels - 1):
if areas[i] >= min_size: #keep
result[labels == i + 1] = 255
fig, ax = plt.subplots(2,1, figsize=(30,20))
ax[0].imshow(img)
ax[0].set_title('Original image')
ax[1].imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
ax[1].set_title('preprocessed image')
I would really appreciate it if you have any suggestions or steps on how to connect the lines? Thank you
Using the following sequence of methods I was able to get a rough approximation. It is a very simple solution and might not work for all cases.
1. Morphological operations
To merge neighboring lines perform morphological (dilation) operations on the binary image.
img = cv2.imread('image_path', 0) # grayscale image
img1 = cv2.imread('image_path', 1) # color image
th = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (19, 19))
morph = cv2.morphologyEx(th, cv2.MORPH_DILATE, kernel)
2. Finding contours and extreme points
My idea now is to find contours.
Then find the extreme points of each contour.
Finally find the closest distance among these extreme points between neighboring contours. And draw a line between them.
cnts1 = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts1[0] # storing contours in a variable
Lets take a quick detour to visualize where these extreme points are present:
# visualize extreme points for each contour
for c in cnts:
left = tuple(c[c[:, :, 0].argmin()][0])
right = tuple(c[c[:, :, 0].argmax()][0])
top = tuple(c[c[:, :, 1].argmin()][0])
bottom = tuple(c[c[:, :, 1].argmax()][0])
# Draw dots onto image
cv2.circle(img1, left, 8, (0, 50, 255), -1)
cv2.circle(img1, right, 8, (0, 255, 255), -1)
cv2.circle(img1, top, 8, (255, 50, 0), -1)
cv2.circle(img1, bottom, 8, (255, 255, 0), -1)
(Note: The extreme points points are based of contours from morphological operations, but drawn on the original image)
3. Finding closest distances between neighboring contours
Sorry for the many loops.
First, iterate through every contour (split line) in the image.
Find the extreme points for them. Extreme points mean top-most, bottom-most, right-most and left-most points based on its respective bounding box.
Compare the distance between every extreme point of a contour with those of every other contour. And draw a line between points with the least distance.
for i in range(len(cnts)):
min_dist = max(img.shape[0], img.shape[1])
cl = []
ci = cnts[i]
ci_left = tuple(ci[ci[:, :, 0].argmin()][0])
ci_right = tuple(ci[ci[:, :, 0].argmax()][0])
ci_top = tuple(ci[ci[:, :, 1].argmin()][0])
ci_bottom = tuple(ci[ci[:, :, 1].argmax()][0])
ci_list = [ci_bottom, ci_left, ci_right, ci_top]
for j in range(i + 1, len(cnts)):
cj = cnts[j]
cj_left = tuple(cj[cj[:, :, 0].argmin()][0])
cj_right = tuple(cj[cj[:, :, 0].argmax()][0])
cj_top = tuple(cj[cj[:, :, 1].argmin()][0])
cj_bottom = tuple(cj[cj[:, :, 1].argmax()][0])
cj_list = [cj_bottom, cj_left, cj_right, cj_top]
for pt1 in ci_list:
for pt2 in cj_list:
dist = int(np.linalg.norm(np.array(pt1) - np.array(pt2))) #dist = sqrt( (x2 - x1)**2 + (y2 - y1)**2 )
if dist < min_dist:
min_dist = dist
cl = []
cl.append([pt1, pt2, min_dist])
if len(cl) > 0:
cv2.line(img1, cl[0][0], cl[0][1], (255, 255, 255), thickness = 5)
4. Post-processing
Since the final output is not perfect, you can perform additional morphology operations and then skeletonize it.
Note: I am migrating this question from Data Science Stack Exchange, where it received little exposure.
I am trying to implement an OCR solution to identify the numbers read from the picture of a screen.
I am adapting this pyimagesearch tutorial to my problem.
Because I am dealing with a dark background, I first invert the image, before converting it to grayscale and thresholding it:
inverted_cropped_image = cv2.bitwise_not(cropped_image)
gray = get_grayscale(inverted_cropped_image)
thresholded_image = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY)[1]
Then I call pytesseract's image_to_data function to output a dictionary containing the different text regions and their confidence intervals:
from pytesseract import Output
results = pytesseract.image_to_data(thresholded_image, output_type=Output.DICT)
Finally I iterate over results and plot them when their confidence exceeds a user defined threshold (70%). What bothers me, is that my script identifies everything in the image except the number that I would like to recognize (1227.938).
My first guess is that the image_to_data parameters are not set properly.
Checking this website, I selected a page segmentation mode (psm) of 11 (sparse text) and tried whitelisting numbers only (tessedit_char_whitelist=0123456789m.'):
results = pytesseract.image_to_data(thresholded_image, config='--psm 11 --oem 3 -c tessedit_char_whitelist=0123456789m.', output_type=Output.DICT)
Alas, this is even worse, and the script now identifies nothing at all!
Do you have any suggestion? Am I missing something obvious here?
EDIT #1:
At Ann Zen's request, here's the code used to obtain the first image:
import imutils
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pytesseract
from pytesseract import Output
def get_grayscale(image):
return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
filename = "IMAGE.JPG"
cropped_image = cv2.imread(filename)
inverted_cropped_image = cv2.bitwise_not(cropped_image)
gray = get_grayscale(inverted_cropped_image)
thresholded_image = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY)[1]
results = pytesseract.image_to_data(thresholded_image, config='--psm 11 --oem 3 -c tessedit_char_whitelist=0123456789m.', output_type=Output.DICT)
color = (255, 255, 255)
for i in range(0, len(results["text"])):
x = results["left"][i]
y = results["top"][i]
w = results["width"][i]
h = results["height"][i]
text = results["text"][i]
conf = int(results["conf"][i])
print("Confidence: {}".format(conf))
if conf > 70:
print("Confidence: {}".format(conf))
print("Text: {}".format(text))
print("")
text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
cv2.rectangle(cropped_image, (x, y), (x + w, y + h), color, 2)
cv2.putText(cropped_image, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,1.2, color, 3)
cv2.imshow('Image', cropped_image)
cv2.waitKey(0)
EDIT #2:
Rarely have I spent reputation points so well! All three replies posted so far helped me refine my algorithm.
First, I wrote a Tkinter program allowing me to manually crop the image around the number of interest (modifying the one found in this SO post)
Then I used Ann Zen's idea of narrowing down the search area around the fractional part. I am using her nifty process function to prepare my grayscale image for contour extraction: contours, _ = cv2.findContours(process(img_gray), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE). I am using RETR_EXTERNAL to avoid dealing with overlapping bounding rectangles.
I then sorted my contours from left to right. Bounding rectangles exceeding a user-defined threshold are associated with the integral part (white rectangles); otherwise they are associated with the fractional part (black rectangles).
I then extracted the characters using Esraa's approach i.e. applying a Gaussian blur prior to calling Tesseract. I used a much larger kernel (15x15 vs 3x3) to achieve this.
I am not out of the woods yet, but hopefully I will get better results by using Ahx's adaptive thresholding.
The Concept
As you have probably heard, pytesseract is not good at detecting text of different sizes on the same line as one piece of text. In your case, you want to detect the 1227.938, where the 1227 is much larger than the .938.
One way to go about solving this is to have the program estimate where the .938 is, and enlarge that part of the image. After that, pytesseract will have no problem in returning the text.
The Code
import cv2
import numpy as np
import pytesseract
def process(img):
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(img_gray, 200, 255, cv2.THRESH_BINARY)
img_canny = cv2.Canny(thresh, 100, 100)
kernel = np.ones((3, 3))
img_dilate = cv2.dilate(img_canny, kernel, iterations=2)
return cv2.erode(img_dilate, kernel, iterations=2)
img = cv2.imread("image.png")
img_copy = img.copy()
hh = 50
contours, _ = cv2.findContours(process(img), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
for cnt in contours:
if 20 * hh < cv2.contourArea(cnt) < 30 * hh:
x, y, w, h = cv2.boundingRect(cnt)
ww = int(hh / h * w)
src_seg = img[y: y + h, x: x + w]
dst_seg = img_copy[y: y + hh, x: x + ww]
h_seg, w_seg = dst_seg.shape[:2]
dst_seg[:] = cv2.resize(src_seg, (ww, hh))[:h_seg, :w_seg]
gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
results = pytesseract.image_to_data(thresh)
for b in map(str.split, results.splitlines()[1:]):
if len(b) == 12:
x, y, w, h = map(int, b[6: 10])
cv2.putText(img, b[11], (x, y + h + 15), cv2.FONT_HERSHEY_COMPLEX, 0.6, 0)
cv2.imshow("Result", img)
cv2.waitKey(0)
The Output
Here is the input image:
And here is the output image:
As you have said in your post, the only part you need the the decimal 1227.938. If you want to filter out the rest of the detected text, you can try tweaking some parameters. For example, replacing the 180 from _, thresh = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY) with 230 will result in the output image:
The Explanation
Import the necessary libraries:
import cv2
import numpy as np
import pytesseract
Define a function, process(), that will take in an image array, and return a binary image array that is the processed version of the image that will allow proper contour detection:
def process(img):
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(img_gray, 200, 255, cv2.THRESH_BINARY)
img_canny = cv2.Canny(thresh, 100, 100)
kernel = np.ones((3, 3))
img_dilate = cv2.dilate(img_canny, kernel, iterations=2)
return cv2.erode(img_dilate, kernel, iterations=2)
I'm sure that you don't have to do this, but due to a problem in my environment, I have to add pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe' before I can call the pytesseract.image_to_data() method, or it throws an error:
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
Read in the original image, make a copy of it, and define the rough height of the large part of the decimal:
img = cv2.imread("image.png")
img_copy = img.copy()
hh = 50
Detect the contours of the processed version of the image, and add a filter that roughly filters out the contours so that the small text remains:
contours, _ = cv2.findContours(process(img), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
for cnt in contours:
if 20 * hh < cv2.contourArea(cnt) < 30 * hh:
Define the bounding box of each contour that didn't get filtered out, and use the properties to enlarge those parts of the image to the height defined for the large text (making sure to also scale the width accordingly):
x, y, w, h = cv2.boundingRect(cnt)
ww = int(hh / h * w)
src_seg = img[y: y + h, x: x + w]
dst_seg = img_copy[y: y + hh, x: x + ww]
h_seg, w_seg = dst_seg.shape[:2]
dst_seg[:] = cv2.resize(src_seg, (ww, hh))[:h_seg, :w_seg]
Finally, we can use the pytesseract.image_to_data() method to detect the text. Of course, we'll need to threshold the image again:
gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
results = pytesseract.image_to_data(thresh)
for b in map(str.split, results.splitlines()[1:]):
if len(b) == 12:
x, y, w, h = map(int, b[6: 10])
cv2.putText(img, b[11], (x, y + h + 15), cv2.FONT_HERSHEY_COMPLEX, 0.6, 0)
cv2.imshow("Result", img)
cv2.waitKey(0)
I have been working with Tesseract for quite some time, so let me clarify something for you. Tesseract is extremely helpful if you're trying to recognize text in documents more than any other computer vision projects. It usually needs a binarized image to get a good output. Therefore, you will always need some image pre-processing.
However, after several trials in the past with all page segmentation modes, I realized that it fails when font size differs on the same line without having a space. Sometimes PSM 6 is helpful if the difference is low, but in your condition, you may try an alternative. If you don't care about the decimals, you may try the following solution:
img = cv2.imread(r'E:\Downloads\Iwzrg.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.GaussianBlur(gray, (3,3),0)
_,thresh = cv2.threshold(img_blur,200,255,cv2.THRESH_BINARY_INV)
# If using a fixed camera
new_img = thresh[0:100, 80:320]
text = pytesseract.image_to_string(new_img, lang='eng', config='--psm 6 --oem 3 -c tessedit_char_whitelist=0123456789')
OUTPUT: 1227
I would like to recommend applying another image processing method.
Because I am dealing with a dark background, I first invert the image, before converting it to grayscale and thresholding it:
You applied global thresholding and couldn't achieve the desired result.
Then you can apply either adaptive-thresholding or inRange
For the given image, if we apply the inRange threshold:
To be able to recognize the image as accurately as possible we can add a border to the top of the image and resize the image (Optional)
In the OCR section, check if the detected region contains a digit
if text.isdigit():
Then display on the image:
The result is nearly the desired value. Now you can try with the other suggested methods to find the exact value.
The problem is .938 recognized as 235, maybe resizing using different values might improve the result.
Code:
from cv2 import imread, cvtColor, COLOR_BGR2HSV as HSV, inRange, getStructuringElement, resize
from cv2 import imshow, waitKey, MORPH_RECT, dilate, bitwise_and, rectangle, putText
from cv2 import copyMakeBorder as addBorder, BORDER_CONSTANT as CONSTANT, FONT_HERSHEY_SIMPLEX
from numpy import array
from pytesseract import image_to_data, Output
bgr = imread("Iwzrg.png")
resized = resize(bgr, (800, 600), fx=0.75, fy=0.75)
bordered = addBorder(resized, 200, 0, 0, 0, CONSTANT, value=0)
hsv = cvtColor(bordered, HSV)
mask = inRange(hsv, array([0, 0, 250]), array([179, 255, 255]))
kernel = getStructuringElement(MORPH_RECT, (50, 30))
dilated = dilate(mask, kernel, iterations=1)
thresh = 255 - bitwise_and(dilated, mask)
data = image_to_data(thresh, output_type=Output.DICT)
for i in range(0, len(data["text"])):
x = data["left"][i]
y = data["top"][i]
w = data["width"][i]
h = data["height"][i]
text = data["text"][i]
if text.isdigit():
print("Text: {}".format(text))
print("")
text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
rectangle(thresh, (x, y), (x + w, y + h), (0, 255, 0), 2)
putText(thresh, text, (x, y - 10), FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
imshow("", thresh)
waitKey(0)
In this image I am trying to detect horizontal lines. The code works well when image is not skewed. However, it is not working on such skewed images. I have tried this method to detect the right angle by histogram but many times is actually making it more skewed - python-opencv-skew-correction-for-ocr
Below is code to detect horizontal lines:
gray=cv2.cvtColor(img_final_bin,cv2.COLOR_BGR2GRAY)
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (100,1))
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts, hierarchy = cv2.findContours(detected_lines, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
boundingBoxes = [list(cv2.boundingRect(c)) for c in cnts]
Below is the code for skew correction, which is giving wrong results to me:
def correct_skew(image, delta=0.001, limit=3):
def determine_score(arr, angle):
data = inter.rotate(arr, angle, reshape=False, order=0)
histogram = np.sum(data, axis=1)
score = np.sum((histogram[1:] - histogram[:-1]) ** 2)
return histogram, score
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
print("thresh", thresh.shape)
thresh1 = thresh[0:500, 0:500]
print("thresh1", thresh1.shape)
scores = []
angles = np.arange(-limit, limit + delta, delta)
for i, angle in enumerate(angles):
histogram, score = determine_score(thresh1, angle)
scores.append(score)
# if i%100 == 0:
# print(score, angle, len(angles), i)
best_angle = angles[scores.index(max(scores))]
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, best_angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, \
borderMode=cv2.BORDER_REPLICATE)
return best_angle, rotated
Python Wand, which is based upon ImageMagick has a deskew function.
Input:
from wand.image import Image
from wand.display import display
with Image(filename='table.png') as img:
img.deskew(0.4*img.quantum_range)
img.save(filename='table_deskew.png')
display(img)
Result:
I'm working on a project about recognizing moroccan license plates which look like this image :
Moroccan License Plate
Please how can I use OpenCV to cut the license plate out and Tesseract to read the numbers and arabic letter in the middle.
I have looked into this research paper : https://www.researchgate.net/publication/323808469_Moroccan_License_Plate_recognition_using_a_hybrid_method_and_license_plate_features
I have installed OpenCV and Tesseract for python in Windows 10. When I run the tesseract on the text only part of the license plate using "fra" language I get 7714315l Bv. How can I separate the data?
Edit:
The arabic letters we use in Morocco are :
أ ب ت ج ح د هـ
The expected result is : 77143 د 6
The vertical lines are irrelevant, I have to use them to separate the image and read data separately.
Thanks in advance!
You can use HoughTransform since the two vertical lines are irrelevant, to crop the image:
import numpy as np
import cv2
image = cv2.imread("lines.jpg")
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
dst = cv2.Canny(grayImage, 0, 150)
cv2.imwrite("canny.jpg", dst)
lines = cv2.HoughLinesP(dst, 1, np.pi / 180, 50, None, 60, 20)
lines_x = []
# Get height and width to constrain detected lines
height, width, channels = image.shape
for i in range(0, len(lines)):
l = lines[i][0]
# Check if the lines are vertical or not
angle = np.arctan2(l[3] - l[1], l[2] - l[0]) * 180.0 / np.pi
if (l[2] > width / 4) and (l[0] > width / 4) and (70 < angle < 100):
lines_x.append(l[2])
# To draw the detected lines
#cv2.line(image, (l[0], l[1]), (l[2], l[3]), (0, 0, 255), 3, cv2.LINE_AA)
#cv2.imwrite("lines_found.jpg", image)
# Sorting to get the line with the maximum x-coordinate for proper cropping
lines_x.sort(reverse=True)
crop_image = "cropped_lines"
for i in range(0, len(lines_x)):
if i == 0:
# Cropping to the end
img = image[0:height, lines_x[i]:width]
else:
# Cropping from the start
img = image[0:height, 0:lines_x[i]]
cv2.imwrite(crop_image + str(i) + ".jpg", img)
I am sure you know now how to get the middle part ;)
Hope it helps!
EDIT:
Using some morphological operations, you can also extract the characters individually:
import numpy as np
import cv2
image = cv2.imread("lines.jpg")
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
dst = cv2.Canny(grayImage, 50, 100)
dst = cv2.morphologyEx(dst, cv2.MORPH_RECT, np.zeros((5,5), np.uint8),
iterations=1)
cv2.imwrite("canny.jpg", dst)
im2, contours, heirarchy = cv2.findContours(dst, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for i in range(0, len(contours)):
if cv2.contourArea(contours[i]) > 200:
x,y,w,h = cv2.boundingRect(contours[i])
# The w constrain to remove the vertical lines
if w > 10:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 1)
cv2.imwrite("contour.jpg", image)
Result:
This what I achieved by now...
The detection on second image was made by using the code found here: License plate detection with OpenCV and Python
Full code (which work from the third image an on) is this:
import cv2
import numpy as np
import tesserocr as tr
from PIL import Image
image = cv2.imread("cropped.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('gray', image)
thresh = cv2.adaptiveThreshold(gray, 250, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 255, 1)
cv2.imshow('thresh', thresh)
kernel = np.ones((1, 1), np.uint8)
img_dilation = cv2.dilate(thresh, kernel, iterations=1)
im2, ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
clean_plate = 255 * np.ones_like(img_dilation)
for i, ctr in enumerate(sorted_ctrs):
x, y, w, h = cv2.boundingRect(ctr)
roi = img_dilation[y:y + h, x:x + w]
# these are very specific values made for this image only - it's not a factotum code
if h > 70 and w > 100:
rect = cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
clean_plate[y:y + h, x:x + w] = roi
cv2.imshow('ROI', rect)
cv2.imwrite('roi.png', roi)
img = cv2.imread("roi.png")
blur = cv2.medianBlur(img, 1)
cv2.imshow('4 - blur', blur)
pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
api = tr.PyTessBaseAPI()
try:
api.SetImage(pil_img)
boxes = api.GetComponentImages(tr.RIL.TEXTLINE, True)
text = api.GetUTF8Text()
finally:
api.End()
# clean the string a bit
text = str(text).strip()
plate = ""
# 77143-1916 ---> NNNNN|symbol|N
for char in text:
firstSection = text[:5]
# the arabic symbol is easy because it's nearly impossible for the OCR to misunderstood the last 2 digit
# so we have that the symbol is always the third char from the end (right to left)
symbol = text[-3]
lastChar = text[-1]
plate = firstSection + "[" + symbol + "]" + lastChar
print(plate)
cv2.waitKey(0)
For arabic symbols you should install additional languages from TesseractOCR (and possibly use the version 4 of it).
Output: 77143[9]6
The number between brackets is the arabic symbol (undetected).
Hope I helped you.
I am trying to detect edges from the products on a shelf using histogram projections. But I am stuck at 2 levels.
The challenges that I m facing are:
How to get the longest non shelf segment from the image i.e Detect the width of the widest product on the shelf from the available one.
How to achieve morphological reconstruction using custom markers.To eliminate
all small horizontal segments, I am generating 2 markers which can be seen in 'markers.png' (Attached). With them, I am calculating the minimum of the reconstruction outputs from both the markers.
Need assistance on this.
Thanks a lot
Below is my python code for the same.
Below is my python code
********************************************************************************
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
import math
# Read the input image
img = cv.imread('C:\\Users\\672059\\Desktop\\p2.png')
# Converting from BGR to RGB. Default is BGR.
# img_rgb = cv.cvtColor(img, cv.COLOR_BGR2RGB)
# Resize the image to 150,150
img_resize = cv.resize(img, (150, 150))
# Get the dimensions of the image
img_h, img_w, img_c = img_resize.shape
# Split the image on channels
red = img[:, :, 0]
green = img[:, :, 1]
blue = img[:, :, 2]
# Defining a vse for erosion
vse = np.ones((img_h, img_w), dtype=np.uint8)
# Morphological Erosion for red channel
red_erode = cv.erode(red, vse, iterations=1)
grad_red = cv.subtract(red, red_erode)
# Morphological Erosion for green channel
green_erode = cv.erode(green, vse, iterations=1)
grad_green = cv.subtract(green, green_erode)
# Morphological Erosion for blue channel
blue_erode = cv.erode(blue, vse, iterations=1)
grad_blue = cv.subtract(blue, blue_erode)
# Stacking the individual channels into one processed image
grad = [grad_red, grad_green, grad_blue]
retrieved_img = np.stack(grad, axis=-1)
retrieved_img = retrieved_img.astype(np.uint8)
retrieved_img_gray = cv.cvtColor(retrieved_img, cv.COLOR_RGB2GRAY)
plt.title('Figure 1')
plt.imshow(cv.bitwise_not(retrieved_img_gray), cmap=plt.get_cmap('gray'))
plt.show()
# Hough Transform of the image to get the longest non shelf boundary from the image!
edges = cv.Canny(retrieved_img_gray, 127, 255)
minLineLength = img_w
maxLineGap = 10
lines = cv.HoughLinesP(edges, 1, np.pi/180, 127, minLineLength=1, maxLineGap=1)
temp = img.copy()
l = []
for x in range(0, len(lines)):
for x1, y1, x2, y2 in lines[x]:
cv.line(temp, (x1, y1), (x2, y2), (0, 255, 0), 2)
d = math.sqrt((x2-x1)**2 + (y2-y1)**2)
l.append(d)
# Defining a hse for erosion
hse = np.ones((1, 7), dtype=np.uint8)
opening = cv.morphologyEx(retrieved_img_gray, cv.MORPH_OPEN, hse)
plt.title('Figure 2')
plt.subplot(1, 2, 1), plt.imshow(img)
plt.subplot(1, 2, 2), plt.imshow(cv.bitwise_not(opening), 'gray')
plt.show()
# Dilation with disk shaped structuring element
horizontal_size = 7
horizontalstructure = cv.getStructuringElement(cv.MORPH_ELLIPSE, (horizontal_size, 1))
dilation = cv.dilate(opening, horizontalstructure)
plt.title('Figure 3')
plt.imshow(cv.bitwise_not(dilation), 'gray')
plt.show()
# Doing canny edge on dilated image
edge = cv.Canny(dilation, 127, 255)
plt.title('Figure 4')
plt.imshow(edges, cmap='gray')
plt.show()
h_projection = edge.sum(axis=1)
print(h_projection)
plt.title('Projection')
plt.plot(h_projection)
plt.show()
listing = []
for i in range(1, len(h_projection)-1):
if h_projection[i-1] == 0 and h_projection[i] == 0:
listing.append(dilation[i])
listing.append(dilation[i-1])
a = np.array([np.array(b) for b in l])
h = len(l)
_, contours, _ = cv.findContours(a, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
x, y, w, h = cv.boundingRect(contours[0])
y = y + i - h
cv.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
l.clear()
plt.imshow(img)
plt.show()
# Generating a mask
black_bg = np.ones([img_h, img_w], dtype=np.uint8)
# Clone the black bgd image
left = black_bg.copy()
right = black_bg.copy()
# Taking 10% of the image width
ten = int(0.1 * img_w)
left[:, 0:ten+1] = 0
right[:, img_w-ten:img_w+1] = 0
plt.title('Figure 4')
plt.subplot(121), plt.imshow(left, 'gray')
plt.subplot(122), plt.imshow(right, 'gray')
plt.show()
# Marker = left and right. Mask = dilation
mask = dilation
marker_left = left
marker_right = right
********************************************************************************
markers.png link: https://i.stack.imgur.com/45WJ6.png
********************************************************************************
Based on you input image, I would :
take a picture of an empty fridge
then compare the current image with the empty one.
play with morphological operations
get connected components > size N
If you can't take a empty fridge image:
segment the shelves (threshold white parts)
undo do the rotation of the image by using image moments of the shelves
for each shelve:
Threshold on saturation
Do a vertical projection
Count maxima.
Tresholded:
Erode-dilate:
Connected componens (width > 10 * height + > minsize):
And you have shelves.
Now take the average Y form each shelf and cut the original image in pieces:
Dither to 8 colors:
and threshold:
Connected components (h>1.5*w, minsize... this is hard here, I played with it :)