clahe | contrast-limited adaptive histogram equalization | Computer Vision library
kandi X-RAY | clahe Summary
kandi X-RAY | clahe Summary
(Exact) contrast-limited adaptive histogram equalization
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Top functions reviewed by kandi - BETA
- Compute the dilated histogram
- Clone an image
- Set up the setup
- Prepare build extension
clahe Key Features
clahe Examples and Code Snippets
Lines of Code : 9License : Strong Copyleft (CC BY-SA 4.0)
Copydef claheImage(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = gray.astype(np.uint16)
eq = clahe.apply(gray)
eq = cv2.cvtColor(eq, cv2.COLOR_GRAY2BGR)
eq = eq.astype(np.float32)
eq = eq / np.max(eq) # thisfor i in range(frame.shape[0]):
for j in range(frame.shape[1]):
pixel = frame [i,j]
Temp_C = pixel/100 - 273.15
color = cv2.putText(color, "{:.2f}".format(Temp_C), (10,30), cv2.FONT_HERSHEY_SIMPLEX, 0.45, cv2.LIimport easyocr
reader = easyocr.Reader(['en']) # need to run only once to load model into memory
reader.readtext(path_to_file, allowlist='0123456789')
img = cv2.imread(fileName)
gray = cv2.cvtColor(img, cv2.COLOR_Bimport sys
print(sys.argv[1], float(sys.argv[2]), tuple(map(int, sys.argv[3:])))
$ python testing.py picture.jpg 3.0 8 8
> picture.jpg 3.0 (8, 8)
all_img = glob.glob('.')
other_dir = 'new_path'
for img_id, img_path in enumerate(all_img):
img = cv2.imread(img_path,0)
#create a CLAHE object (Arguments are optional).
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(20,class MySpecialDataset(datasets.ImageFolder):
def __init__(self, root, loader=default_loader, is_valid_file=None):
super(MySpecialDataset, self).__init__(root=root, loader=loader, is_valid_file=is_valid_file)
def __getitem__(self,# create an inpainting mask with "red-enough" pixels
mask = cv2.inRange(img_src_rgb, np.array([200,0,0]), np.array([255,50,50]))
# enlarge the mask to cover the borders
kernel = np.ones((3,3),np.uint8)
mask = cv2.dilate(mask,kernel,iteratiimag = QtGui.QImage(self.imageFiles1['photo_Blue'])
magick identify -verbose sample.png # omit "magick" if still using v6
Image: sample.png
Format: PNG (Portable Network Graphics)
Mime type: image/png
Class: DirectClass
Geometry: 1920x1080+0+0
from skimage.filters import threshold_yen
from skimage.exposure import rescale_intensity
from skimage.io import imread, imsave
img = imread('mY7ep.jpg')
yen_threshold = threshold_yen(img)
bright = rescale_intensity(img, (0, yen_thresholdCommunity Discussions
Trending Discussions on clahe
QUESTION
I'm starting a project with OpenCV with ROS and i need to use xfeatures2d that is in opencv_contrib. I follow this instructions and modify my CMake File in this way:
...ANSWER
Answered 2021-Dec-02 at 14:19I found a solution in Github:
In catkin_ws/src:
QUESTION
I'm trying to apply CLAHE method using OpenCV library to the green channel which has been split, but then this error appears:
...ANSWER
Answered 2021-Nov-04 at 13:35PIL and OpenCV both have different formats of image objects but they can be interchangeable, e.g. OpenCV accepts ndarray and you can change PIL image into array using NumPy. Similarly, you can get PIL image object from numpy array using PIL.Image.fromarray() method. I've modified your code and I think it will resolve your issue.
QUESTION
im trying to apply CLAHE to my dataset, but Im not sure if it is applied correctly. Im trying to visualize the result after applying CLAHE, but I can only visualize if I use plt.imshow(img[0].astype('uint8')). So I am not sure if it is being applied correctly. Ill be very grateful if someone can help me.
...ANSWER
Answered 2021-Sep-25 at 12:38This is because of Matplotlib. The range of pixel values should be between [0, 255] for integers and [0., 1.] for floating point. Your preprocessing function must not respect this.
I suggest you remove rescale=1/255, but make sure the range of values that's output from your function is between 0 and 1.
QUESTION
I have multiple images with darks bands and I want to draw a rectangle over all of the darker bands. I have tried using HSV color detection, it works reasonably well but it picks up darker regions Current output of the code, code below.
...ANSWER
Answered 2021-Sep-07 at 20:28Otsu thresholding does a pretty good job of that image. IMHO, there's little point converting a near-grey to HSL as there is no colour, so I load it as greyscale:
QUESTION
Hi everyone can you please help me i'm getting this bug with random crop augmentation. TypeError: slice indices must be integers or None or have an index method
Code is below.
...ANSWER
Answered 2021-Jun-03 at 05:20I think the error is in this line:
QUESTION
I am currently applying the Contrast Limited Adaptive Histogram Equalization algorithm together with an algorithm to perform the photo denoise.
My problem is that I am working with 360 photos. As the contrast generates different values at the edges when I join the photo, the edge line is highly noticeable. How can I mitigate that line? What changes should I make so that it is not noticeable and the algorithm is applied consistently?
Original Photo:
Code to Contrast Limited Adaptive Histogram Equalization
...ANSWER
Answered 2021-May-21 at 12:27Here's an answer for C++, you can probably convert it easily to python/numpy. The idea is to use a border region before performing CLAHE and crop the image afterwards.
These are the subimage regions in the original image:
and they will be copied the the left/right of the image like this:
Maybe you can reduce the size of the border strongly:
QUESTION
Here is the code that i'm trying to modify. To read and save all the images at once in one folder, however I got error when I tried to save it
...ANSWER
Answered 2021-May-19 at 19:37It seems like there are multiple issues with this code.
iis not defined.imageslist has all the images appended and while processing you are making use of the empty list variableimage_list
You are probably looking for a solution like this.
QUESTION
I'm using FLIR Lepton camera to capture the thermal image, then try to display the temperature on the image using PutText method but getting very strange result. Any advice is appreciated in advance.
Code:
...ANSWER
Answered 2021-Apr-16 at 02:25Have a look here:
QUESTION
I'm trying to get the edges of this object from a TEM(microscope) image and the problem is that the contact is low especially in the upper edge, I tried several things thresholding, contrast equalization... but I wasn't able to get the upper edge.
N.B: I'm trying to calculate the angle between the droplet and the tube I'm not sure if this is the best way to approach this problem.
The original image:
The Canny Edge detection I get:
the steps I got to get this result are:
- Contrast enhancement
- Thresholding
- Gauss filter
- Canny Edge detection
Code:
...ANSWER
Answered 2021-Apr-10 at 12:08We have this image and we want to detect the edges of the microphone:
Basically, I converted the image to grayscale, added a Gaussian blur, and detected the edges using the canny edge detector. One more important part is to fill in the gaps in the detected edges by dilating the edges and then eroding them.
All of the above is implemented in the process function; the draw_contours function basically utilizes the process function, and detects the greatest contour:
QUESTION
My graduation project is to use transfer learning on a CNN model that can diagnose Covid-19 from Chest X-ray images. After spending days Fine tuning the hyper parameters such as the number of fully connected layers, the number of nodes in the layers, the learning rate, and the drop rate using Keras tuner library with Bayesian Optimizer, I got some very good results, A test accuracy of 98% for multi class classification and a 99% for binary class classification. However, i froze all the layers in the original base model. I only fine tuned the last Fully connected layers after exhaustive hyper parameter optimization. Most articles and papers out there say that they fine the fully connected layers as well as some of the convolutional layers. Am i doing something wrong? I am afraid that this is too good to be true.
My data set is not that big, only 7000 images taken from the Kaggle Covid-19 competition.
I used image enhancement techniques such as N-CLAHE on the images before the training and the classification which improved the accuracy significantly compared to not enhancing the images.
I did the same for multiple State of art models, such as VGG-16 and ResNet50, and they all gave me superb results.
...ANSWER
Answered 2021-Feb-11 at 04:07If you mean by "only fine tuned the last Fully connected layers" then NO, you did not.
You can choose to fine-tune any layer of your choice but most importantly the final layers of the model, which is what you did, so you're good to go.
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Install clahe
You can use clahe like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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