i want to detect a car number!
see this photo
using this codes :
import numpy as np
import imutils
import cv2
# Read the image file
image = cv2.imread('Car_Image_1.jpg')
# Resize the image - change width to 500
image = imutils.resize(image, width=500)
# Display the original image
cv2.imshow("Original Image", image)
# RGB to Gray scale conversion
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("1 - Grayscale Conversion", gray)
# Noise removal with iterative bilateral filter(removes noise while preserving edges)
gray = cv2.bilateralFilter(gray, 11, 17, 17)
cv2.imshow("2 - Bilateral Filter", gray)
# Find Edges of the grayscale image
edged = cv2.Canny(gray, 170, 200)
cv2.imshow("4 - Canny Edges", edged)
# Find contours based on Edges
(new, cnts , _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
# 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]
# we currently have no Number plate contour
NumberPlateCnt = None
# loop over our contours to find the best possible approximate contour of number plate
count = 0
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
if len(approx) == 4: # Select the contour with 4 corners
NumberPlateCnt = approx #This is our approx Number Plate Contour
break
# Drawing the selected contour on the original image
cv2.drawContours(image, [NumberPlateCnt], -1, (0,255,0), 3)
cv2.imshow("Final Image With Number Plate Detected", image)
cv2.waitKey(0) #Wait for user input before closing the images displayed
but when i run my code , got this error :
C:\Python27\python.exe C:/Users/Crypt/PycharmProjects/MyDetector/CarPlateDetection.py
Traceback (most recent call last):
File "C:/Users/Crypt/PycharmProjects/MyDetector/CarPlateDetection.py", line 27, in <module>
(new, cnts , _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
ValueError: need more than 2 values to unpack
Process finished with exit code 1
i change this line of code
(new, cnts , _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
to
(new, cnts) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
then run , the error is :
C:\Python27\python.exe C:/Users/Crypt/PycharmProjects/MyDetector/CarPlateDetection.py
Traceback (most recent call last):
File "C:/Users/Crypt/PycharmProjects/MyDetector/CarPlateDetection.py", line 29, in <module>
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:30]
cv2.error: OpenCV(4.0.0) C:\projects\opencv-python\opencv\modules\imgproc\src\shapedescr.cpp:272: error: (-215:Assertion failed) npoints >= 0 && (depth == CV_32F || depth == CV_32S) in function 'cv::contourArea'
Process finished with exit code 1
here is the code on github :
https://github.com/Aqsa-K/Car-Number-Plate-Detection-OpenCV-Python
From OpenCV4, findContours returns 2 values contours and hierachy, so now, contours are in the new in your code. It should be
cnts, hier = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
Related
I have an image that contains a table, the table can be in many sizes and the image too, and the table can be fully gridded (with only some blank spot that needs to be filled), it can be with only vertical grid lines and can be only with horizontal grid lines.
I've searched the web for a long time and found no solution that worked for me.
I found the following questions that seem to be suitable for me:
Python & OpenCV: How to add lines to the gridless table
Draw a line on a gridless image Python Opencv
How to repair incomplete grid cells and fix missing sections in image
Python & OpenCV: How to crop half-formed bounding boxes
My code is taken from the answers to the above questions and the "best" result I got from the above question codes is that it drew 2 lines one at the rightmost part and one on the leftmost part.
I'm kind of new to OpenCV and the image processing field so I am not sure how can I fix the above questions codes to suit my needs or how to accomplish my needs exactly, I would appreciate any help you can provide.
Example of an image table:
Update:
To remove the horizontal lines I use exactly the code you can find in here, but the result I get on the example image is this:
as you can see it removed most of them but not all of them, and then when I try to apply the same for the vertical ones (I tried the same code with rotation, or flipping the kernel) it does not work at all...
I also tried this code but it didn't work at all also.
Update 2:
I was able to remove the lines using this code:
def removeLines(result, axis) -> np.ndarray:
img = result.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
if axis == "horizontal":
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 25))
elif axis == "vertical":
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 1))
else:
raise ValueError("Axis must be either 'horizontal' or 'vertical'")
detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
result = img.copy()
for c in cnts:
cv2.drawContours(result, [c], -1, (255, 255, 255), 2)
return result
gridless = removeLines(removeLines(cv2.imread(image_path), 'horizontal'), 'vertical')
Result:
Problem:
After I remove lines, when I try to draw the vertical lines using this code:
# read image
img = old_image.copy() # cv2.imread(image_path1)
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# average gray image to one column
column = cv2.resize(gray, (ww,1), interpolation = cv2.INTER_AREA)
# threshold on white
thresh = cv2.threshold(column, 248, 255, cv2.THRESH_BINARY)[1]
# get contours
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# Draw vertical
for cntr in contours_v:
x,y,w,h = cv2.boundingRect(cntr)
xcenter = x+w//2
cv2.line(original_image, (xcenter,0), (xcenter,hh-1), (0, 0, 0), 1)
I get this result:
Update 3:
when I try even thresh = cv2.threshold(column, 254, 255, cv2.THRESH_BINARY)[1] (I tried lowering it 1 by 1 until 245, for both the max value and the threshold value, each time I get a different or similar result but always too much lines or too less lines) I get the following:
Input:
Output:
It's putting too many lines instead of just 1 line in each column
Code:
# read image
img = old_image.copy() # cv2.imread(image_path1)
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# average gray image to one column
column = cv2.resize(gray, (ww, 1), interpolation = cv2.INTER_AREA)
# threshold on white
thresh = cv2.threshold(column, 254, 255, cv2.THRESH_BINARY)[1]
# get contours
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
for cntr in contours:
x, y, w, h = cv2.boundingRect(cntr)
xcenter = x + w // 2
cv2.line(original_image, (xcenter,0), (xcenter, hh_-1), (0, 0, 0), 1)
Here is one way to get the lines in Python/OpenCV. Average the image down to 1 column. Then threshold and get the contours. Then get the bounding boxes and find the vertical centers. Draw lines at those places.
If you do not want the extra lines, crop your image first to get the inside of the table.
Input:
import cv2
import numpy as np
# read image
img = cv2.imread("table4.png")
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# average gray image to one column
column = cv2.resize(gray, (1,hh), interpolation = cv2.INTER_AREA)
# threshold on white
thresh = cv2.threshold(column, 248, 255, cv2.THRESH_BINARY)[1]
# get contours
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# loop over contours and get bounding boxes and ycenter and draw horizontal line at ycenter
result = img.copy()
for cntr in contours:
x,y,w,h = cv2.boundingRect(cntr)
ycenter = y+h//2
cv2.line(result, (0,ycenter), (ww-1,ycenter), (0, 0, 255), 1)
# write results
cv2.imwrite("table4_lines3.png", result)
# display results
cv2.imshow("RESULT", result)
cv2.waitKey(0)
Result:
You wrote that you tried to remove the lines using the code, but it did not work.
It works fine for me in Python/OpenCV.
Read the input
Convert to grayscale
Threshold to show the horizontal lines
Apply morphology open with a horizontal kernel to isolate the horizontal lines
Get their contours
Draw the contours on a copy of the input as white to cover over the black horizontal lines
Save the results
Input:
import cv2
import numpy as np
# read the input
img = cv2.imread('table4.png')
# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# threshold
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# do morphology to detect lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))
detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
# get contours
cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
# draw contours as white on copy of input
result = img.copy()
for c in cnts:
cv2.drawContours(result, [c], -1, (255,255,255), 2)
# save results
cv2.imwrite('table4_horizontal_lines_threshold.png', thresh)
cv2.imwrite('table4_horizontal_lines_detected.png', detected_lines)
cv2.imwrite('table4_horizontal_lines_removed.png', result)
# show results
cv2.imshow('thresh', thresh)
cv2.imshow('morphology', detected_lines)
cv2.imshow('result', result)
cv2.waitKey(0)
Threshold Image:
Morphology Detected Lines Image:
Result:
I have the following table:
I want to write a script that creates lines based on the natural breakages on the table text. The result would look like this:
Is there an OpenCV implementation that makes drawing these lines possible? I looked at the answers to the questions here and here, but neither worked. What is the best approach to solving this problem?
Here is one way to get the horizontal lines in Python/OpenCV by counting the number of white pixels in each row of the image to find their center y values. The vertical lines can be added by a similar process.
Input:
import cv2
import numpy as np
# read image
img = cv2.imread("table.png")
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# threshold gray image
thresh = cv2.threshold(gray, 254, 255, cv2.THRESH_BINARY)[1]
# count number of non-zero pixels in each row
count = np.count_nonzero(thresh, axis=1)
# threshold count at ww (width of image)
count_thresh = count.copy()
count_thresh[count==ww] = 255
count_thresh[count<ww] = 0
count_thresh = count_thresh.astype(np.uint8)
# get contours
contours = cv2.findContours(count_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# loop over contours and get bounding boxes and ycenter and draw horizontal line at ycenter
result = img.copy()
for cntr in contours:
x,y,w,h = cv2.boundingRect(cntr)
ycenter = y+h//2
cv2.line(result, (0,ycenter), (ww-1,ycenter), (0, 0, 0), 2)
# write results
cv2.imwrite("table_thresh.png", thresh)
cv2.imwrite("table_lines.png", result)
# display results
cv2.imshow("THRESHOLD", thresh)
cv2.imshow("RESULT", result)
cv2.waitKey(0)
Threshold Image:
Result with lines:
ADDITION
Here is an alternate method that is slightly simpler. It averages the image down to one column rather than counting white pixels.
import cv2
import numpy as np
# read image
img = cv2.imread("table.png")
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# average gray image to one column
column = cv2.resize(gray, (1,hh), interpolation = cv2.INTER_AREA)
# threshold on white
thresh = cv2.threshold(column, 254, 255, cv2.THRESH_BINARY)[1]
# get contours
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
# loop over contours and get bounding boxes and ycenter and draw horizontal line at ycenter
result = img.copy()
for cntr in contours:
x,y,w,h = cv2.boundingRect(cntr)
ycenter = y+h//2
cv2.line(result, (0,ycenter), (ww-1,ycenter), (0, 0, 0), 2)
# write results
cv2.imwrite("table_lines2.png", result)
# display results
cv2.imshow("RESULT", result)
cv2.waitKey(0)
Result:
Hey guys i'm from Viet Nam so my English is not good, forgive me if my words confuse you.
I have some lines with 1 pixel wide (after i skeletonize the binary image). How can I make a loop that erase all the endpoints of curves until there is no pixel that connected in all eight directions is more than two? (I just want to keep the longest line). Thank you for your help :(
Here is the thinned image link:
I'm trying to determine the length of shrimp using image processing. My idea is first thresholding the image, then thinning the object to make a curve represent for the lenght of object, finally measure the lenght of that curve.
This is my code:
import cv2
import numpy as np
from skimage import morphology
from skimage import io
kernel = np.ones((3,3), np.uint8)
kernel2 = np.ones((30,30), np.uint8)
img_ori = cv2.imread("0523.1212.06b.png")
img = cv2.cvtColor(img_ori,cv2.COLOR_BGR2GRAY)
# Threshold the image
ret,mask = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY_INV)
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel2)
#closing_display = cv2.resize(closing, (605,645))
#cv2.imshow("Closing", closing_display)
# Erase small objects
cnts = cv2.findContours(closing, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
area = cv2.contourArea(c)
if area < 5500:
cv2.drawContours(closing, [c], -1, (0,0,0), -1)
closing_display = cv2.resize(closing, (605,645))
cv2.imshow("Shimp Detecting", closing)
# Determine contours
contours, hierachy = cv2.findContours(closing, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
#Thinning
thinned = cv2.ximgproc.thinning(closing)
thinned_display = cv2.resize(thinned, (605,645))
#Draw contours on thinned
cv2.drawContours(thinned, contours, -1, (321, 100, 100), 2)
cv2.imshow("Thinned Image", thinned)
cv2.waitKey(0)
cv2.destroyAllWindows()
I am trying to extract a hand out of an image. I am using OpenCV 4.0 and Python 3.6.
cnts = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# return None, if no contours detected
if len(cnts) == 0:
return
else:
# based on contour area, get the maximum contour which is the hand
segmented = max(cnts, key=cv2.contourArea)
And it gives me this error:
Traceback (most recent call last):
File "test.py", line 85, in <module>
hand = segment(gray)
File "test.py", line 37, in segment
segmented = max(cnts, key=cv2.contourArea)
TypeError: Expected cv::UMat for argument 'contour'
Since that worked like half a year ago, I am assuming that the error occurs because of some kind of a module change. How can that be fixed?
You didn't pay attention to the outputs of cv2.findContours. For any OpenCV version >= 4.0, it's
contours, hierarchy = cv2.findContours(...)
I made up an example:
import cv2
import numpy as np
# Set up dummy image
image = np.zeros((400, 400), np.uint8)
cv2.circle(image, (150, 250), 100, 255, cv2.FILLED)
# Find contours: OpenCV 4.x
cnts, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 0:
# return None, if no contours detected
segmented = None
else:
# based on contour area, get the maximum contour which is the hand
segmented = max(cnts, key=cv2.contourArea)
print('Number of contour points:', segmented.shape[0])
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
The example image looks like this:
The output:
Number of contour points: 292
Hope that helps!
Getting an error:
Traceback (most recent call last):
File "motion_detector.py", line 21, in <module>
(_, cnts, _) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
ValueError: not enough values to unpack (expected 3, got 2)
Having problems with detecting contours in an image. Have been double checking from the tutorial and also looking from stack overflow to understand where I miss something, but can't find the solution. Using Python 3.6.4 and OpenCV 4.0.0. Thanks for the help!
Code here:
import cv2, time
first_frame = None
video = cv2.VideoCapture(0)
while True:
check, frame = video.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray,(21,21),0)
if first_frame is None:
first_frame = gray
delta_frame = cv2.absdiff(first_frame, gray)
thresh_frame = cv2.threshold(delta_frame, 30, 255, cv2.THRESH_BINARY)[1]
thresh_frame = cv2.dilate(thresh_frame, None, iterations = 2)
(_, cnts, _) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in cnts:
if cv2.contourArea(contour) < 1000:
continue
(x, y, w, h) = cv2.boundingRect(contour)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
cv2.imshow("Gray Frame", gray)
cv2.imshow("Delta Frame", delta_frame)
cv2.imshow("Threshold Frame", thresh_frame)
cv2.imshow("Color Frame", frame)
key = cv2.waitKey(1)
print(gray)
print(delta_frame)
if key == ord('q'):
break
video.release()
cv2.destroyAllWindows
I also encountered same problem, if you are using an old tutorial cv2.findContours() function returns 3 value but if you are using later versions it returns 2 value so you can remove first variable assignment and use like that
cnts, _ = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
Well in Python version 2 findContours() used to return 3 values so we save it in (_,cnts,_) however in python 3 it returns 2 values which are countours and hierarchy. so we need to save it in (cnts,_).
So for python 2 people the code goes like:
(_,cnts,_) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
And for Python 3 people the code goes like :
(cnts,_) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
Its just about version guys,nothing to worry just change it this way and I am sure you will get the desired output.
If you are using cv 4.0 then findContours is returning two values. See the example here or the documentation for findContours. The function signature looks like this:
contours, hierarchy = cv.findContours(image, mode, method[, contours[, hierarchy[, offset]]])
As pointed problem is with that line:
(_, cnts, _) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
According to documentation cv2.findCountours returns two things: contours, hierarchy, so when you try to unpack it to (_, cnts, _) having 3 elements error appears. Please try to replace mentioned line with
cnts = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
and check if that would solve you problem.