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Description:

opencvwrap 0.1.2

opencv_wrap
A collection of decorators for opencv and helper functions for multiple
opencv tasks.
Working with opencv can be quite a hussel, a lot of boiler code, nested
functions for specific use cases, this package is designed to make it
easier to work with opencv, while focusing on the main task in hand.
best for prototyping and quick testing. second part is speed and
performance, this package is designed to be fast and efficient.

Built with ☕ by [@rishi23root](https://github.com/rishi23root/)
rishi23root/opencv_wrap/

Installation
pip install opencv-wrap

Very basic example of reading camera feed and displaying it. with
just 5 lines of code. 😎

from opencv_wrap import cv2Decorator

@cv2Decorator.TotalTimeTaken(show=True)
@cv2Decorator.AccessCamOrVideo(show=True)
@cv2Decorator.CalculateFps(draw=True)
def all_actions(**kwargs):
return kwargs

all_actions()

Advance example of face detection and smart viewer. with just 23
lines of code. 😊

from opencv_wrap import cv2Decorator
import cv2
from opencv_wrap.detectors import Face
from opencv_wrap.utils.helper import show_all_frames, clipImage

@cv2Decorator.DetectInEachFrame(detector=Face(verbose=True),name="face")
@cv2Decorator.TotalTimeTaken(show=True)
@cv2Decorator.AccessCamOrVideo(show=False,videoPath="./opencv_wrap/testMedia/test.mp4") # path to video
@cv2Decorator.CalculateFps(draw=False)
@cv2Decorator.MirrorFrame()
@cv2Decorator.ConvertCOLOR(converter=cv2.COLOR_RGB2BGR, frameName="bgr_frame")
def all_actions(**kwargs):
mainFrameCopy = kwargs["frame"].copy()
processed = kwargs["face"].detect(kwargs["bgr_frame"])
face_coordinate = kwargs["face"].getDetectionBox(
processed, kwargs["frame"], draw=True
)
kwargs["face"].getLandmarks(processed, kwargs["frame"], draw=True)
kwargs["detected"] = [clipImage(mainFrameCopy, i) for i in face_coordinate]
show_all_frames(kwargs, keysToShow=["frame", "detected"])
return kwargs

all_actions()

image

Features with decorators
from opencv_wrap import cv2Decorator

@cv2Decorator.TotalTimeTaken(show=True)
...

TotalTimeTaken
CalculateFps
MirrorFrame
ConvertCOLOR
AccessCamOrVideo
DetectInEachFrame

Utils to help you with opencv tasks
from opencv_wrap.utils import DetectorClass
from opencv_wrap.utils.helper import detectionBox

Detector Parent
DetectorClass is a base class for all the detectors. provide some basic
functions like Singleton and isVerbose.

Helper functions

saveFrames
detectionBox
detectionBox
resizeImage
clipImage
added_title
combine_images

Detection Classes
from opencv_wrap.detectors import Face , Hand, Pose

Face detection
Hand detection
Pose detection
eye detection (yet to be added)

you can reconstruct the detector classes as per your need. 😊

like extend the class and add more functions to it. like action of
certain detections.

example, blur everything but face. can be useful when you want to
hide the background and just fucus on the object, here Face.

import cv2
from opencv_wrap import cv2Decorator
from opencv_wrap.detectors import Face

class FaceExtented(Face):
def blurEverytingButFace(self, frame, face_coordinate):
# make a copy of the frame
frameCopy = frame.copy()
frame = cv2.blur(frame, (50,50))
for (x, y, w, h) in face_coordinate:
frame[y : y + h, x : x + w] = frameCopy[y : y + h, x : x + w]
return frame

@cv2Decorator.DetectInEachFrame(detector=FaceExtented(verbose=True),name="face")
@cv2Decorator.AccessCamOrVideo(show=True,videoPath="./opencv_wrap/testMedia/test.mp4")
@cv2Decorator.ConvertCOLOR(converter=cv2.COLOR_RGB2BGR, frameName="bgr_frame")
def all_actions(\*\*kwargs):
processed = kwargs["face"].detect(kwargs["bgr_frame"])
face_coordinate = kwargs["face"].getDetectionBox(
processed, kwargs["frame"], draw=False,padding_ratio=0.4)
kwargs["frame"] = kwargs["face"].blurEverytingButFace(kwargs["frame"], face_coordinate)
return kwargs

all_actions()

image

OPEN FOR CONTRIBUTIONS 🤝

Steps to start contributing

Star the repo 🌟
Fork the repo 👨‍💻
Clone the repo 📂
Create a new issue 🔖
Make changes 📜
Push the changes 🚀
Create a pull request 🌐

More Usage Examples

Example 1 : Reading a single frame from the directory

@cv2Decorator.DetectInEachFrame(
detector=cv2.CascadeClassifier(cv2.data.haarcascades+"haarcascade_frontalface_default.xml"),
name='face')
@cv2Decorator.MirrorFrame()
@cv2Decorator.ConvertCOLOR(converter=cv2.COLOR_BGR2GRAY)
def all_actions(**kwargs):
frame = kwargs['frame']
# detect face from trainerd data and detectMultiScale use to deteat every size of face
face_coordinate = kwargs['face'].detectMultiScale(kwargs['greyScale'],1.3,5)
detectionBox(detectedArr=face_coordinate, frame=frame)
return kwargs

frame = cv2.imread('./opencv_wrap/testMedia/test.jpg')

kwargs = all_actions(frame=frame)
cv2.imshow('frame',kwargs['frame'])
key = cv2.waitKey(0)

Example 2 : Reading cam and detecting Hand in each frame

@cv2Decorator.DetectInEachFrame(
detector=Hand(verbose=True),
name="hand",
)
@cv2Decorator.TotalTimeTaken(show=True)
@cv2Decorator.AccessCamOrVideo(show=False, fps=12)
@cv2Decorator.CalculateFps(draw=True)
@cv2Decorator.ConvertCOLOR(converter=cv2.COLOR_RGB2BGR, frameName="bgr_frame")
def all_actions(**kwargs):
mainFrameCopy = kwargs["frame"].copy()
processed = kwargs["hand"].detect(kwargs["bgr_frame"])
face_coordinate = kwargs["hand"].getDetectionBox(
processed, kwargs["frame"], draw=True
)
kwargs["hand"].getLandmarks(processed, kwargs["frame"],draw=True)
# print(len(face_coordinate))
kwargs["detected"] = [clipImage(mainFrameCopy, i) for i in face_coordinate]
show_all_frames(kwargs, keysToShow=["frame", "detected"])
return kwargs

kwargs = all_actions()

image

Example 3 : Reading video and detecting Pose in each frame

@cv2Decorator.DetectInEachFrame(
detector=Pose(verbose=True),
name="pose",
)
@cv2Decorator.TotalTimeTaken(show=True)
@cv2Decorator.AccessCamOrVideo(show=False, videoPath="./opencv_wrap/testMedia/test.mp4", fps=12)
@cv2Decorator.CalculateFps(draw=True)
@cv2Decorator.MirrorFrame()
@cv2Decorator.ConvertCOLOR(converter=cv2.COLOR_BGR2GRAY)
@cv2Decorator.ConvertCOLOR(converter=cv2.COLOR_RGB2BGR, frameName="bgr_frame")
def all_actions(**kwargs):
mainFrameCopy = kwargs["frame"].copy()
processed = kwargs["pose"].detect(kwargs["bgr_frame"])
face_coordinate = kwargs["pose"].getDetectionBox(
processed, kwargs["frame"], draw=True
)
kwargs["pose"].getLandmarks(processed, kwargs["frame"],draw=True)

kwargs["detected"] = [clipImage(mainFrameCopy, i) for i in face_coordinate]
show_all_frames(kwargs, keysToShow=["frame", "detected"])
return kwargs

all_actions()

image

Example 4 : Reading video and saving each frame in a folder

from opencv_wrap import cv2Decorator
from opencv_wrap.utils.helper import saveFrame

@cv2Decorator.AccessCamOrVideo(show=True, videoPath="./opencv_wrap/testMedia/test.mp4", )
def all_actions(**kwargs):
saveFrame(kwargs['frame'],kwargs['frame_count'],destination='./output')
return kwargs

all_actions()

image

Example 5 : Reading video and show converted frame in smart view

@cv2Decorator.TotalTimeTaken(show=True)
@cv2Decorator.AccessCamOrVideo(show=False, videoPath="./opencv_wrap/testMedia/test.mp4", fps=12)
@cv2Decorator.CalculateFps(draw=True)
@cv2Decorator.MirrorFrame()
@cv2Decorator.ConvertCOLOR(converter=cv2.COLOR_BGR2GRAY)
def all_actions(**kwargs):
show_all_frames(kwargs,keysToShow=['frame','greyScale','mirror_frame'])
return kwargs

all_actions()

Future Updates

☐ Face recognition
☐ Eye detection
☐ Object detection
☐ Image classification
☐ segmentation (decorator)
☐ making whole program faster by atleast 10x using cython