cvpack | Utilities for OpenCV in Python | Computer Vision library
kandi X-RAY | cvpack Summary
kandi X-RAY | cvpack Summary
OpenCV extensions for more Pythonic interactions.
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Top functions reviewed by kandi - BETA
- Handle GET request
- Render a PNG image
- Write a frame to the stream
- Create a video writer
- Create a size instance from an image
- Checks if this element is inside the given rectangle
- Checks if the given point contains this rectangle
- Compute the dot product of a point
- Return the dot product of this vector
- Return the dot product of a point
cvpack Key Features
cvpack Examples and Code Snippets
Lines of Code : 9License : Permissive (MIT)
Copyimport cvpack
img = cvpack.imread("img.png")
p1 = cvpack.Point(50, 50)
p2 = cvpack.Point(100, 100)
rect = cvpack.Rect.from_points(p1, p2)
roi = img[rect.slice()]
roi_size = cvpack.Size.from_image(roi)
assert roi_size == rect.size()
import cv2
import cvpack
with cvpack.VideoCapture("video.mp4") as cap:
with cvpack.VideoWriter("reversed.mp4", fourcc=int(cap.fourcc), fps=cap.fps) as writer:
for frame in cap:
flipped = cv2.flip(frame, 0)
writer. from pathlib import Path
import cvpack
for path in Path("folder").glob("*.png"):
img = cvpack.imread(path)
big = cvpack.add_grid(cvpack.enlarge(img))
cvpack.imshow_browser(img, route=str(path))
Community Discussions
Trending Discussions on cvpack
QUESTION
I am running the following code without problem:
...ANSWER
Answered 2018-Jul-28 at 16:05The problem rises when xgboost tries to split to train/validation and in one of the splits it has no negatives or positives examples (either in the train set or the validation set).
I see 2 quick approaches you can take:
- You can check how many positives examples and negative examples you
have, and get more examples of what you miss. It'll be even easier and
faster for you, to duplicate those examples you lack. For example, if you have a 99% negative examples and 1% positive examples, you might want to duplicate each positive example, 99 times (which is the product of
99/1). - You can create the cross validation yourself, thus, gain control on the split, and force negatives and positive examples for each split.
QUESTION
I am training a XGBoostClassifier for my training set.
My training features are in the shape of (45001, 10338) which is a numpy array and my training labels are in the shape of (45001,) [I have 1161 unique labels so I have done a label encoding for the labels] which is also a numpy array.
From the documentation, it clearly says that I can create DMatrix from numpy array. So I am using the above mentioned training features and labels as numpy arrays straightaway. But I am getting the following error
...ANSWER
Answered 2017-Aug-03 at 02:59The error is b/c you are trying to use AUC evaluation metric for multiclass classification, but AUC is only applicable for two-class problems. In xgboost implementation, "auc" expects prediction size to be the same as label size, while your multiclass prediction size would be 45001*1161. Use either "mlogloss" or "merror" multiclass metrics.
P.S.: currently, xgboost would be rather slow with so many classes, as there is some inefficiency with predictions caching during training.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install cvpack
You can use cvpack 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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