I'm working in a script using different OpenCV operations for processing an image with solar panels in a house roof. My original image is the following:
After processing the image, I get the edges of the panels as follows:
It can be seen how some rectangles are broken due to reflection of the Sun in the picture.
I would like to know if it's possible to fix those broken rectangles, maybe by using the pattern of those which are not broken.
My code is the following:
# Load image
color_image = cv2.imread("google6.jpg")
cv2.imshow("Original", color_image)
# Convert to gray
img = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
# Apply various filters
img = cv2.GaussianBlur(img, (5, 5), 0)
img = cv2.medianBlur(img, 5)
img = img & 0x88 # 0x88
img = cv2.fastNlMeansDenoising(img, h=10)
# Invert to binary
ret, thresh = cv2.threshold(img, 127, 255, 1)
# Perform morphological erosion
kernel = np.ones((5, 5),np.uint8)
erosion = cv2.morphologyEx(thresh, cv2.MORPH_ERODE, kernel, iterations=2)
# Invert image and blur it
ret, thresh1 = cv2.threshold(erosion, 127, 255, 1)
blur = cv2.blur(thresh1, (10, 10))
# Perform another threshold on blurred image to get the central portion of the edge
ret, thresh2 = cv2.threshold(blur, 145, 255, 0)
# Perform morphological erosion to thin the edge by ellipse structuring element
kernel1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
contour = cv2.morphologyEx(thresh2, cv2.MORPH_ERODE, kernel1, iterations=2)
# Get edges
final = cv2.Canny(contour, 249, 250)
cv2.imshow("final", final)
I have tried to modify all the filters I'm using in order to reduce as much as possible the effect of the Sun in the original picture, but that is as far as I have been able to go.
I'm in general happy with the result of all those filters (although any advice is welcome), so I'd like to work on the black/white imaged I showed, which is already smooth enough for the post-processing I need to do.
Thansk!
The pattern is not broken in the original image, so it being broken in your binarized result must mean your binarization is not optimal.
You apply threshold() to binarize the image, and then Canny() to the binary image. The problems here are:
Thresholding removes a lot of information, this should always be the last step of any processing pipeline. Anything you lose here, you've lost for good.
Canny() should be applied to a gray-scale image, not a binary image.
The Canny edge detector is an edge detector, but you want to detect lines, not edges. See here for the difference.
So, I suggest starting from scratch.
The Laplacian of Gaussian is a very simple line detector. I took these steps:
Read in image, convert to grayscale.
Apply Laplacian of Gaussian with sigma = 2.
Invert (negate) the result and then set negative values to 0.
This is the output:
From here, it should be relatively straight-forward to identify the grid pattern.
I don't post code because I used MATLAB for this, but you can accomplish the same result in Python with OpenCV, here is a demo for applying the Laplacian of Gaussian in OpenCV.
This is Python + OpenCV code to replicate the above:
import cv2
color_image = cv2.imread("/Users/cris/Downloads/L3RVh.jpg")
img = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
out = cv2.GaussianBlur(img, (0, 0), 2) # Note! Specify size of Gaussian by the sigma, not the kernel size
out = cv2.Laplacian(out, cv2.CV_32F)
_, out = cv2.threshold(-out, 0, 1e9, cv2.THRESH_TOZERO)
However, it looks like OpenCV doesn't linearize (apply gamma correction) when converting from BGR to gray, as the conversion function does that I used when creating the image above. I think this gamma correction might have improved the results a bit by reducing the response to the roof tiles.
Related
Using the threshold functions in open CV on an image to get a binary image, with Otsu's thresholding I get a image that has white spots due to different lighting conditions in parts of the image
or with adaptive threshold to fix the lighting conditions, it fails to accurately represent the pencil-filled bubbles that Otsu actually can represent.
How can I get both the filled bubbles represented and a fixed lighting conditions without patches?
Here's the original image
Here is my code
#binary image conversion
thresh2 = cv2.adaptiveThreshold(papergray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 21, 13)
thresh = cv2.threshold(papergray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cv2.imshow("Binary", thresh) #Otsu's
cv2.imshow("Adpative",thresh2)
An alternative approach would be to apply a morphological closing, which would remove all the drawing, yielding an estimate of the illumination level. Dividing the image by the illumination level gives you an image of the sheet corrected for illumination:
In this image we can easily apply a global threshold:
I used the following code:
import diplib as dip
img = dip.ImageRead('tlCw6.jpg')(1)
corrected = img / dip.Closing(img, dip.SE(40, 'rectangular'))
out = dip.Threshold(corrected, method='triangle')[0]
This can be done with cv2.ADAPTIVE_THRESH_MEAN_C:
import cv2
img = cv2.imread("omr.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
cv2.imshow("Mean Adaptive Thresholding", thresh)
cv2.waitKey(0)
The output is:
Problems with your approach:
The methods you have tried out:
Otsu threshold is decided based on all the pixel values in the entire image (global technique). If you look at the bottom-left of your image, there is a gray shade which can have an adverse effect in deciding the threshold value.
Adaptive threshold: here is a recent answer on why it isn't helpful. In short, it acts like an edge detector for smaller kernel sizes
What you can try:
OpenCV's ximgproc module has specialized binary image generation methods. One such method is the popular Niblack threshold technique.
This is a local threshold technique that depends on statistical measures. It divides the image into blocks (sub-images) of size predefined by the user. A threshold is set based on the mean minus k times standard deviation of pixel values for each block. The k is decided by the user.
Code:
img =cv2.imread('omr_sheet.jpg')
blur = cv2.GaussianBlur(img, (3, 3), 0)
gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
niblack = cv2.ximgproc.niBlackThreshold(gray, 255, cv2.THRESH_BINARY, 41, -0.1, binarizationMethod=cv2.ximgproc.BINARIZATION_NICK)
Result:
Links:
To know more about cv2.ximgproc.niBlackThreshold
There are other binarization techniques available that you may want to explore. It also contains links to research papers that explain each of these techniques on detail.
Edit:
Adaptive threshold actually works if you know what you are working with. You can decide the kernel size beforehand.
See Prashant's answer.
So I have images of a bunch of straight lines forming various shapes. However these straight line have a tendency to not be quite straight because the underlying source often has these lines go between pixels. In that case the underlying source produces pixels that are next to a line. In that case I would like for these extra pixels to be removed.
Here we have a source image:
And here we have the same image denoised:
.
I'm messing around with houghline transforms or de-noising algorithms but none of these are working well and it feels like there should be a good way of fixing this that uses the specific fact that these are lines rather than just normal pepper and salt noise.
I'm working with python right now but other language answers are acceptable.
I thought of the same solution as commented by Cris:
Convert the image to binary image.
Apply morphological opening with np.ones((3, 15)) kernel - keeping only horizontal lines.
Apply morphological opening with np.ones((15, 3)) kernel - keeping only vertical lines.
Apply bitwise or between the above matrices to form a mask.
Apply mask on image - zero pixels in the mask gets zero values.
Here is a code sample:
import numpy as np
import cv2
img = cv2.imread('input.png') # Read input image (BGR color format)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Convert to grayscale
thresh = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)[1] # Convert to binary (only zeros and 255)
horz = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, np.ones((3, 15))) # Keep only horizontal lines
vert = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, np.ones((15, 3))) # Keep only vertical lines
mask = cv2.bitwise_or(horz, vert) # Unite horizontal and vertical lines to form a mask
res = cv2.bitwise_and(img, img, mask=mask) # Place zeros where mask is zero
# Show the result
cv2.imshow('mask', mask)
cv2.imshow('res', res)
cv2.waitKey()
cv2.destroyAllWindows()
Result:
mask:
res:
The result is not perfect, and not generalized.
You may get better result using for loops.
Example: Delete pixels that has many horizontal pixels below them, but only few next to them (repeat for left, right, and bottom).
I've tried to binarize passport images for OCR using following steps :
img = cv2.medianBlur(nid_aligned_image,3)
img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
this methods works well for better background image but not given type of images.
Here is the output and OCR can't read this
Can anyone suggest me a better approch ?
My approach for the problem is:
1- Apply adaptive thresholding
2- Apply Morphological Transformation
3- Apply bitwise operation
Step 1: Adaptive Threshold
From the documentation:
if an image has different lighting conditions in different areas. In that case, adaptive thresholding can help. Here, the algorithm determines the threshold for a pixel based on a small region around it. So we get different thresholds for different regions of the same image which gives better results for images with varying illumination.
To summarize: when a global value used as a threshold is not performing well, you will use adaptive thresholding.
img2 = cv2.imread("BESFs.png")
gry2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
flt = cv2.adaptiveThreshold(gry2,
100, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 13, 16)
Result:
Step 2: Morphological Transformation
From the documentation:
It needs two inputs, one is our original image, second one is called structuring element or kernel which decides the nature of operation
We need to define a kernel (filter) for processing image.
krn = np.ones((3, 3), np.uint8)
We will use opening and closing:
Opening is just another name of erosion followed by dilation. It is useful in removing noise
Closing is reverse of Opening, Dilation followed by Erosion. It is useful in closing small holes inside the foreground objects, or small black points on the object.
opn = cv2.morphologyEx(flt, cv2.MORPH_OPEN, krn)
cls = cv2.morphologyEx(opn, cv2.MORPH_CLOSE, krn)
Step 3: Bitwise Operation
From the documentation
They will be highly useful while extracting any part of the image
gry2 = cv2.bitwise_or(gry2, cls)
Result:
Now if we use pytesseract for extracting the text
txt = pytesseract.image_to_string(gry2)
txt = txt.rstrip().split('\n\n')[1].split(' ')[1]
print("Passport number: {}".format(txt))
Result:
Passport number: BC0874168
Optional
For your future OCR problem, you can try to enhance the image resolution. For instance:
from PIL import Image
img = Image.open("BESFs.png")
h, w = img.size
fct = min(1, int(1024.0/h))
sz = int(fct * h), int(fct * w)
im_rsz = img.resize(sz, Image.ANTIALIAS)
im_rsz.save("out_dpi_300.png", dpi=(300, 300))
For this problem it has no effect, but maybe it may help you in the future.
Code for the problem:
import cv2
import pytesseract
import numpy as np
img2 = cv2.imread("BESFs.png")
gry2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
flt = cv2.adaptiveThreshold(gry2,
100, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 13, 16)
krn = np.ones((3, 3), np.uint8)
opn = cv2.morphologyEx(flt, cv2.MORPH_OPEN, krn)
cls = cv2.morphologyEx(opn, cv2.MORPH_CLOSE, krn)
gry2 = cv2.bitwise_or(gry2, cls)
txt = pytesseract.image_to_string(gry2)
txt = txt.rstrip().split('\n\n')[1].split(' ')[1]
print("Passport number: {}".format(txt))
I am trying to OCR the picture of documents and my current approach is
Read an image as a grayscale
Binarize it thresholding
Wrap perspective along the contours obtained from cv2.findContours()
The above works well if image is not shadowed. Now I want to get contours of shadowed pictures. My first attempt is to use cv2.adaptiveThreshold for step 2. The adaptive threshold successfully weakened the shadow but the resulted image lost the contrast between the paper and the background. That made cv2 impossible to find contours of the paper. So I need to use other method to remove the shadow.
Is there any way to remove shadow maintaining the background colour?
For reference here is the sample picture I am processing with various approaches. From left, I did
grayscale
thresholding
adaptive thresholdin
normalization
My goal is to obtain the second picture without shadow.
Please note that I actually have a temporary solution specifically to the picture which is to process the part of the picture with shadow separately. Yet, it is not the general solution to shadowed picture as its performance depends on the size, shape and position of a shadow so please use other methods.
This is the original picture.
Here is one way in Python/OpenCV using division normalization, optionally followed by sharpening and/or thresholding.
Input:
import cv2
import numpy as np
import skimage.filters as filters
# read the image
img = cv2.imread('receipt.jpg')
# convert to gray
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# blur
smooth = cv2.GaussianBlur(gray, (95,95), 0)
# divide gray by morphology image
division = cv2.divide(gray, smooth, scale=255)
# sharpen using unsharp masking
sharp = filters.unsharp_mask(division, radius=1.5, amount=1.5, multichannel=False, preserve_range=False)
sharp = (255*sharp).clip(0,255).astype(np.uint8)
# threshold
thresh = cv2.threshold(sharp, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# save results
cv2.imwrite('receipt_division.png',division)
cv2.imwrite('receipt_division_sharp.png',sharp)
cv2.imwrite('receipt_division_thresh.png',thresh)
# show results
cv2.imshow('smooth', smooth)
cv2.imshow('division', division)
cv2.imshow('sharp', sharp)
cv2.imshow('thresh', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
Division:
Sharpened:
Thresholded:
I am trying to do characters detection, have to draw a box around them, then crop and then feed to a neural network for recognition. Everything is working but before I was using sets of characters on a single color background image and segmentation was easily done.
However with real photos I have different lighting conditions and really struggle to find the contours.
After applying some adaptive thresholding I managed to get the folowing results, but starting from that I really can't figure how to properly proceed and detect each character. I can detect half of the characters easily, but not all of them. Probably because they are surrounded by lots of small irrelevant contours.
I have a feeling there is one step left but I can't figure which one.
Find Countours is capable of finding only about half of the characters.
For now, in short, im doing:
im_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
im_gray = cv2.GaussianBlur(im_gray, (5, 5), 0)
_, th1 = cv2.threshold(im_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cim, ctrs, hier = cv2.findContours(th1.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
and
th2 = cv2.adaptiveThreshold(im_gray,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
Images below - original image and some variations of intermediate results.
Original picture:
After some thresholding:
After some thresholding:
Inverse thresholding:
So the question is - what is the step/steps after to segment the characters?
You can perform difference of gaussians. The idea is to blur the image with two different kernels and subtract their respective results:
Code:
im = cv2.imread(img, 0)
#--- it is better to take bigger kernel sizes to remove smaller edges ---
kernel1 = 15
kernel2 = 31
blur1 = cv2.GaussianBlur(im,(kernel1, kernel1), 0)
blur2 = cv2.GaussianBlur(im,(kernel2, kernel2), 0)
cv2.imshow('Difference of Gaussians',blur2 - blur1)
Result: