imutils | convenience functions to make basic image processing | Computer Vision library
kandi X-RAY | imutils Summary
kandi X-RAY | imutils Summary
A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and both Python 2.7 and Python 3.
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imutils Key Features
imutils Examples and Code Snippets
Lines of Code : 1License : Permissive (MIT)
Copypip install imutils
pip install imutils
import cv2
from imutils import contours
# Load image, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.png')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (7,7), 0)
thdef correlation_coefficient(patch1, patch2):
product = np.mean((patch1 - patch1.mean()) * (patch2 - patch2.mean()))
stds = patch1.std() * patch2.std()
if stds == 0:
return 0
else:
product /= stds
retmanager = Manager()
knownEncodings = manager.list()
knownNames = manager.list()
no_faces = manager.list()
error_in_image = manager.list()
im=Image.open(r"specify the path of the image")`
left = "specify the value in pixels"
top = "specify the value in pixels"
right = "specify the value in pixels"
bottom = "specify the value in pixels"
# Cropped imagedef find_edges(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (3, 3), 0)
edged = cv2.Canny(image=gray, threshold1=100, threshold2=200)
cnts = cv2.findContours(edged.copy(), cv2.RETR_LISimport socketserver
open_connections = []
def newFrame(data):
# If there are no open connections, there's nothing to do.
if not open_connections:
return
decoded_data = base64.b64decode(data)
frame_decoded = np.frimport matplotlib.pyplot as plt
from tensorflow.keras.layers import BatchNormalization
Community Discussions
Trending Discussions on imutils
QUESTION
I don't know how to extract the irregular area surrounded by green lines. i.e., the left cheek and the right cheek of a face.
...ANSWER
Answered 2022-Mar-29 at 10:31You can accomplish this by two simple steps:
- Create a mask using the point coordinates you have
- Execute bitwise_and operation (crop)
Code:
QUESTION
I am trying to calculate the SSIM between corresponding images. For example, an image called 106.tif in the ground truth directory corresponds to a 'fake' generated image 106.jpg in the fake directory.
The ground truth directory absolute pathway is /home/pr/pm/zh_pix2pix/datasets/mousebrain/test/B
The fake directory absolute pathway is /home/pr/pm/zh_pix2pix/output/fake_B
The images inside correspond to each other, like this: see image
There are thousands of these images I want to compare on a one-to-one basis. I do not want to compare SSIM of one image to many others. Both the corresponding ground truth and fake images have the same file name, but different extension (i.e. 106.tif and 106.jpg) and I only want to compare them to each other.
I am struggling to edit available scripts for SSIM comparison in this way. I want to use this one: https://github.com/mostafaGwely/Structural-Similarity-Index-SSIM-/blob/master/ssim.py but other suggestions are welcome. The code is also shown below:
...ANSWER
Answered 2022-Mar-22 at 06:44Here's a working example to compare one image to another. You can expand it to compare multiple at once. Two test input images with slight differences:
Results
Highlighted differences
Similarity score
Image similarity 0.9639027981846681
Difference masks
Code
QUESTION
I have tried the similar problems' solutions on here but none seem to work. It seems that I get a memory error when installing tensorflow from requirements.txt. Does anyone know of a workaround? I believe that installing with --no-cache-dir would fix it but I can't figure out how to get EB to do that. Thank you.
Logs:
...ANSWER
Answered 2022-Feb-05 at 22:37The error says MemoryError. You must upgrade your ec2 instance to something with more memory. tensorflow is very memory hungry application.
QUESTION
I'm trying to create a program that will take a long time to explain here, so I'm gonna tell you guys the part that I need help with.
Here I need to detect a rectangle(which will be a license plate in our example). It does the recognition almost perfectly but I want it more precise. Here is the example image I used.
As you can see, It does a fairly good job at finding it but I want to take the rounded corners into consideration too.
Here is the source code
...ANSWER
Answered 2021-Dec-20 at 04:13First of all, in your find_edges function, I replaced the line screenCnt = approx with screenCnt = c, in order to keep all the coordinates in the resulting detected contour:
QUESTION
i have an import problem when executing my code:
...ANSWER
Answered 2021-Oct-06 at 20:27You're using outdated imports for tf.keras. Layers can now be imported directly from tensorflow.keras.layers:
QUESTION
I want to copy a file to a new non-existing folder:
...ANSWER
Answered 2021-Sep-16 at 17:31The destination path you printed is not what you actually passed to makedirs. The terminal subfolder was not created because you passed the parent of output. But that was not necessary, because output is derived from destination_path, which is already the (absolute) parent of the relevant folder. So all you really need is this:
QUESTION
Link to original image https://ibb.co/0VC6vkX
I am currently working with an OCR Project. I pre-processed the image, and then applied pre-trained EAST model for text detection.
...ANSWER
Answered 2021-Jun-07 at 07:02Here's a possible solution that you can try improving on by trying a few things:
- by varying Gaussian parameters
- by thresholding the blurred image to see if it improves the result
Code:
QUESTION
I have probem with this code , why ?
the code :
...ANSWER
Answered 2021-Apr-09 at 09:33Use
from tensorflow.keras. instead of from keras.
QUESTION
I'm trying to extract the coordinates of a big white region in an image as follows: Here's the original image:
Using a small square kernel, I applied a closing operation to fill small holes and help identify larger structures in the image as follows:
...ANSWER
Answered 2021-May-09 at 21:01With the one image you provided:
I came up with 2 approaches as to how this problem can be solved:
Approach 1 Contour Area ComparisonAs you can see there are 3 large contours in the image; the top rectangle and the two rectangles below it, of which you want to detect as a whole.
So I used a threshold on your image, detected the contours of the thresholded image, and indexed the second largest contour and the third largest contour (the largest is the top rectangle which you want to ignore).
Here is the thresholded image:
I stacked the two contours together and detected the bounding box of the two contours:
QUESTION
So I've been following this tutorial to detect if person wearing mask or not on camera and got everything to work when using the camera by using the following code:
...ANSWER
Answered 2021-Apr-22 at 10:09the problem is from reading your data from MSS. MSS returns raw pixels in the BGRA form (Blue, Green, Red, Alpha).you can read about it from here. you can convert into BGR via cvtColor:
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