Metadata-Version: 2.1
Name: visionlib
Version: 1.2.0
Summary: A simple, easy to use and customizeble cv library 
Home-page: https://github.com/ashwinvin/Visionlib
Author: Ashwin Vinod
Author-email: ashwinvinodsa@gmail.com
License: UNKNOWN
Download-URL: https://github.com/ashwinvin/Visionlib/archive/v1.2.0.tar.gz
Description: # Visionlib
        
        A simple high level api made for assisting in cv-related projects.
        
        ## Features
        
        - Track faces using
          - MTCNN module
          - Dlib hog Based detector
          - Opencv Haar casscades
          - Dnn based model
        - Predict Gender
        - Detect Objects
          - Yolo v3
          - tiny-yolo
        
        ### Installation
        
        #### Dependencies
        
        `sudo apt-get install build-essential cmake pkg-config`
        
        `sudo apt-get install libx11-dev libatlas-base-dev`
        
        `sudo apt-get install libgtk-3-dev libboost-python-dev`
        
        This should install Dependencies required by dlib.
        
        `pip install visionlib`
        
        This will install visionlib.
        
        ##### Optional
        
        If You want to install from source
        `git clone https://github.com/ashwinvin/Visionlib.git`
        `cd visionlib`
        `pip install .`
        
        ### Face Detection
        
        Detecting face in an image is easy . This will return the image with bounding box and box coordinates
        `from visionlib.face.detection import FDetector`
        `detector = FDetector()`
        `detector.detect_face(img, show=True)`
        
        This would detect face and display it automatically.
        
        `detector.set_detector("mtcnn")`
        Dont like the default detector?, change it like this.
        
        #### Examples
        
        ![Detection](docs/images/face_detected.jpg)
        
        ![Detection](docs/images/face_detected_group.jpg)
        
        ### Gender Detection
        
        Once face is detected, it can be passed on to detect_gender() function to recognize gender. It will return the labels (man, woman) and associated probabilities.Like this
        `from visionlib.gender.detection import GDetector`
        `Gdetector = GDetector()`
        `pred, confidence = Gdetector.detect_gender(c_img)`
        
        ##### Example
        
        ![Gender Detection](docs/images/gender_detected_single.jpg)
        
        ### Object Detection
        
        Detecting common objects in the scene is enabled through a single function call detect_objects(). It will return the labeled image for the detected objects in the image. By default it uses yolov3-tiny model.
        `from visionlib.object.detection import Detection`
        `import cv2`
        `detector = Detection()`
        `d_img = detector.detect_objects(img)`
        
        You can enable gpu usage by 
        `detector.detect_objects(img, enable_gpu=True)`
        
        #### Example
        ![object Detection](docs/images/object_detected_objects.jpg)
        
        
        For more examples look in examples directory
        
Keywords: Deep learning,Vision,cv
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
