I'm currently in the pursue of counting the number of shrimps in a given image. I'm using this test image:
The code I have used so far is the following:
import cv2
import numpy as np
from matplotlib import pyplot as plt
#Load img
path = r'C:\Users\...' #the path to the image
original=cv2.imread(path, cv2.COLOR_BGR2RGB)
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
#Hist to proceed with the binarizarion
hist = cv2.calcHist([img],[0],None,[256],[0,256])
#do the threshold
ret,thresh = cv2.threshold(img,60,255,cv2.THRESH_BINARY_INV)
From this point I have tried different morphological transformations such a erode, dilate, open and close but they don't seem to be working and separating the objects as I want.
I've read that I can apply a Watershed transformation so separate touching elements, but I don´t have experience in this (working at this point at the moment).
After that I am planning on using a Simple Blob Detector to count the blobs, I don't know if these steps are correct.
Any help is very welcomed!
Related
I want to extract car images without using Mask RCNN. I tried a couple of methods but couldn't decide on how to proceed with any of them. I need recommendation on which method would be best and how to go through with it.
Method 1 - Using XML files and haar cascade classifier
I was thinking of using xml files to detection and crop car images. The problems I faced were:
They only detect car in square shapes. I needed car images cropped. So ultimately I ended up with better images of cropped cars. This didn't solve my problem.
The cropped image didn't detect car as a whole but small parts of it. Maybe due to XML file's config.
My code:
!wget https://raw.githubusercontent.com/shaanhk/New-GithubTest/master/cars.xml
import numpy as py
import cv2
car_cascade=cv2.CascadeClassifier('cars.xml')
img = cv2.imread('im1.jpg')
cars = car_cascade.detectMultiScale(img, 1.1, 1)
for (x,y,w,h) in cars:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)
cv2.imshow('image',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Resulting image:
Method 2 - Using Canny Edge Detection
I tried to perform canny edge detection for car. It worked to some extent that I managed to reduce edges to mostly car object. But I don't know how to proceed from there.
My code:
import cv2
import numpy as np
image= cv2.imread('im1.jpg')
imagecopy= np.copy(image)
grayimage= cv2.cvtColor(imagecopy, cv2.COLOR_RGB2GRAY)
canny= cv2.Canny(grayimage, 300,150)
cv2.imshow('Highway Edge Detection Image', canny)
cv2.waitKey(0)
cv2.destroyAllWindows()
Resulting Image:
Method 3 - Extract car image using color gradients
On googling I found a method using HSV transformation and then creating a custom mask to extract cars. But I don't know much about this method and have no idea how to go about it. I used the code provided and am posting it below.
Code:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
image = mpimg.imread('im1.jpg')
hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
# HSV channels
h = hsv[:,:,0]
s = hsv[:,:,1]
v = hsv[:,:,2]
background_hue = h[10,10]
lower_hue = np.array([background_hue-10,0,0])
upper_hue = np.array([background_hue+10,255,255])
mask = cv2.inRange(hsv, lower_hue, upper_hue)
# Mask the image to let the car show through
masked_image = np.copy(image)
masked_image[mask != 0] = [0, 0, 0]
cv2.imwrite('mask.jpg',masked_image)
# Display it!
plt.imshow(masked_image)
Image:
I'd like to mention, I'm a complete beginner in Computer Vision and am trying to learn by doing some small stuff like these. My code is probably very flawed and hopefully I can work on it on the way. Please feel absolutely free to mention any other method (except Mask RCNN) or any problems with code.
I've this python code which I use to convert a text written in a picture to a string, it does work for certain images which have large characters, but not for the one I'm trying right now which contains only digits.
This is the picture:
This is my code:
import pytesseract
from PIL import Image
img = Image.open('img.png')
pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files (x86)/Tesseract-OCR/tesseract'
result = pytesseract.image_to_string(img)
print (result)
Why is it failing at recognising this specific image and how can I solve this problem?
I have two suggestions.
First, and this is by far the most important, in OCR preprocessing images is key to obtaining good results. In your case I suggest binarization. Your images look extremely good so you shouldn't have any problem but if you do, then maybe you should try to binarize your images:
import cv2
from PIL import Image
img = cv2.imread('gradient.png')
# If your image is not already grayscale :
# img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
threshold = 180 # to be determined
_, img_binarized = cv2.threshold(img, threshold, 255, cv2.THRESH_BINARY)
pil_img = Image.fromarray(img_binarized)
And then try the ocr again with the binarized image.
Check if your image is in grayscale and uncomment if needed.
This is simple thresholding. Adaptive thresholding also exists but it is noisy and does not bring anything in your case.
Binarized images will be much easier for Tesseract to handle. This is already done internally (https://github.com/tesseract-ocr/tesseract/wiki/ImproveQuality) but sometimes things can be messed up and very often it's useful to do your own preprocessing.
You can check if the threshold value is right by looking at the images :
import matplotlib.pyplot as plt
plt.imshow(img, cmap='gray')
plt.imshow(img_binarized, cmap='gray')
Second, if what I said above still doesn't work, I know this doesn't answer "why doesn't pytesseract work here" but I suggest you try out tesserocr. It is a maintained python wrapper for Tesseract.
You could try:
import tesserocr
text_from_ocr = tesserocr.image_to_text(pil_img)
Here is the doc for tesserocr from pypi : https://pypi.org/project/tesserocr/
And for opencv : https://pypi.org/project/opencv-python/
As a side-note, black and white is treated symetrically in Tesseract so having white digits on a black background is not a problem.
I'm writing a script in Python for my image processing class, which should read a directory for images, display them, and then I will eventually add additional code to perform Otsu thresholding on these images. I can get a reference image to display properly to include Otsu thresholding; however, I run into trouble when I attempt to display the remaining images in the directory. I am not sure that my images are being read from the directory correctly, as I am trying to store them in an array; however, I can see the output window displays grey squares which correspond to the dimensions of the actual image resolutions, which suggests that they are being at least partly read correctly.
I've already attempted to isolate the script to load images and display them into a separate file and running it. I was concerned that the successful processing of my sample image (which included a black/white binarization) was somehow affecting my image display later. This was not the case, as running a separate script produced the same grey square output.
****Update****
I've managed to tweak the below script(not yet updated) to run almost correctly. By writing the full filepath directly for each file, I can get the output to display correctly. It appears there is some issue with loading images into an array, best I can tell; a potential workaround for future testing is importing file locations as a string array, and implementing that vs. loading images into an array directly.
import cv2 as cv
import numpy as np
from PIL import Image
import glob
from matplotlib import pyplot as plot
import time
image=cv.imread('Fig ref.jpg')
image2=cv.cvtColor(image, cv.COLOR_RGB2GRAY)
cv.imshow('Image', image)
# global thresholding
ret1,th1 = cv.threshold(image2,127,255,cv.THRESH_BINARY)
# Otsu's thresholding
ret2,th2 = cv.threshold(image2,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
# Otsu's thresholding after Gaussian filtering
blur = cv.GaussianBlur(image2,(5,5),0)
ret3,th3 = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
# plot all the images and their histograms
images = [image2, 0, th1,
image2, 0, th2,
blur, 0, th3]
titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)',
'Original Noisy Image','Histogram',"Otsu's Thresholding",
'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]
for i in range(3):
plot.subplot(3,3,i*3+1),plot.imshow(images[i*3],'gray')
plot.title(titles[i*3]), plot.xticks([]), plot.yticks([])
plot.subplot(3,3,i*3+2),plot.hist(images[i*3].ravel(),256)
plot.title(titles[i*3+1]), plot.xticks([]), plot.yticks([])
plot.subplot(3,3,i*3+3),plot.imshow(images[i*3+2],'gray')
plot.title(titles[i*3+2]), plot.xticks([]), plot.yticks([])
plot.show()
imageFolderPath = 'D:\Google Drive\Engineering\Senior Year\Image processing\Image processing group work'
imagePath = glob.glob(imageFolderPath + '/*.JPG')
im_array = np.array( [np.array(Image.open(img).convert('RGB')) for img in imagePath] )
temp=cv.imread("D:\Google Drive\Engineering\Senior Year\Image processing\Image processing group work\Fig ref.jpg")
cv.imshow('image', temp)
time.sleep(15)
for i in range(9):
cv.imshow('Image', im_array[i])
time.sleep(2)
plot.subplot(3,3,i*3+3),plot.imshow(images[i*3+2],'gray'): The second argument says you use gray color map. Get rid of it and you would get color displays.
Is there a function in PIL/Pillow that for a grayscale image, will separate the image into sub images containing the components that make up the original image? For example, a png grayscale image with a set of blocks in them. Here, the images types always have high contrast to the background.
I don't want to use openCV, I just need some general blob detection, and was hoping Pillow/PIL might have something that does that already.
Yes, it is possible. You can use edge detection algorithms in PIL.
Sample code:
from PIL import Image, ImageFilter
image = Image.open('/tmp/sample.png').convert('RGB')
image = image.filter(ImageFilter.FIND_EDGES)
image.save('/tmp/output.png')
sample.png :
output.png:
Not using PIL, but worth a look I think:
I start with a list of image files that I've imported as a list of numpy arrays, and I create a list of boolean versions where the threshold is > 0
from skimage.measure import label, regionprops
import numpy as np
bool_array_list= []
for image in image_files:
bool_array = np.copy(image)
bool_array[np.where(bool_array > 0)] = 1
bool_array_list.append(bool_array)
img_region_list = []
Then I use label to identify the different areas, using 8-directional connectivity, and regionprops gives me a bunch of metrics, such as size and location.
for item in bool_array_list:
tmp_region_list = regionprops(label(item,
connectivity=2
)
)
img_region_list.append(tmp_region_list)
I tried almost all filters in PIL, but failed.
Is there any function in numpy of scipy to remove the noise?
Like Bwareaopen() in Matlab()?
e.g:
PS: If there is a way to fill the letters into black, I will be grateful
Numpy/Scipy can do morphological operations just as well as Matlab can.
See scipy.ndimage.morphology, containing, among other things, binary_opening(), the equivalent of Matlab's bwareaopen().
Numpy/Scipy solution: scipy.ndimage.morphology.binary_opening. More powerful solution: use scikits-image.
from skimage import morphology
cleaned = morphology.remove_small_objects(YOUR_IMAGE, min_size=64, connectivity=2)
See http://scikit-image.org/docs/0.9.x/api/skimage.morphology.html#remove-small-objects
I don't think this is what you want, but this works (uses Opencv (which uses Numpy)):
import cv2
# load image
fname = 'Myimage.jpg'
im = cv2.imread(fname,cv2.COLOR_RGB2GRAY)
# blur image
im = cv2.blur(im,(4,4))
# apply a threshold
im = cv2.threshold(im, 175 , 250, cv2.THRESH_BINARY)
im = im[1]
# show image
cv2.imshow('',im)
cv2.waitKey(0)
Output ( image in a window ):
You can save the image using cv2.imwrite