Metadata-Version: 2.1
Name: cv2utils
Version: 0.1.0
Summary: Implementation of some object detection
Home-page: https://github.com/luizcarloscf/cv2utils.git
Author: Luiz Carlos Cosmi Filho
Author-email: luizcarloscosmifilho@gmail.com
License: MIT
Description: # cv2utils
        
        [![PyPI](https://img.shields.io/pypi/v/cv2utils.svg?label=cv2utils)](https://pypi.org/project/cv2utils)
        [![Travis](http://img.shields.io/travis/luizcarloscf/cv2utils/master.svg?label=linux)](https://travis-ci.com/luizcarloscf/cv2utils)
        [![PyPI - Downloads](https://img.shields.io/pypi/dm/cv2utils)](https://pypi.org/project/cv2utils)
        [![License](https://img.shields.io/badge/license-MIT%20-blue.svg)](https://opensource.org/licenses/mit)
        Implementation of some object detection in Python3.5+.
        
        ## Installation
        
        It can be installed through pip:
        ```bash
        pip3 install --user cv2utils
        ```
        This implementation requires OpenCV and Numpy.
        
        ## Usage
        
        #### OpenCV Face DNN
        
        The following example illustrates the ease of use of this package:
        ```python
        >>> import cv2
        >>> from cv2utils import FaceDnn
        >>> image = cv2.imread("face.jpg")
        >>> detector = FaceDnn()
        >>> detector.detect_faces(image)
        [{'label': 'face', 'confidence': 0.9966524243354797, 'box': [210, 64, 522, 465]}]
        ```
        
        The detector returns a list of DICTIONARY objects. Each DICTIONARY object contains three main keys: 'box', 'confidence' and 'label':
        
        * The bounding **box** is formatted as [x_initial, y_initial, x_final, y_final] under the key 'box'.
        * The **confidence** is the probability estimate for a bounding box to be matching the label.
        * The **label** identifies which object is detecting.
        
        Look the file [result_dnn.py](https://github.com/luizcarloscf/cv2utils/blob/master/examples/result_dnn.py) to see how the image below was generated.
        
        <p align="center"><img src="https://raw.githubusercontent.com/luizcarloscf/cv2utils/master/examples/images/result_dnn.jpg" align=middle width=700pt height=250pt/></p>
        
        
        #### OpenCV Face Cascade and Eye Cascade
        
        The following example illustrates the ease of use of this package:
        
        ```python
        >>> import cv2
        >>> from cv2utils import FaceCascade, EyeCascade
        >>> image = imread("face.jpg")
        >>> face_detector = FaceCascade()
        >>> faces = face_detector.detect_faces(image)
        >>> faces
        [{'label': 'face', 'box': [199, 65, 591, 457]}]
        >>>
        >>> [x,y,x_final,y_final] = faces[0]['box']
        >>> eye_detector = EyeCascade()
        >>> eye_detector.detect_eyes(image[y:y_final, x:x_final])
        [{'label': 'eye', 'box': [83, 132, 166, 215]}, {'label': 'eye', 'box': [218, 119, 298, 199]}]
        ```
        
        The detector returns a list of DICTIONARY objects. Each DICTIONARY object contains two main keys: 'box', 'label':
        
        * The bounding **box** is formatted as [x_initial, y_initial, x_final, y_final] under the key 'box'.
        * The **label** identifies which object is detecting.
        
        Look the file [result_cascade.py](https://github.com/luizcarloscf/cv2utils/blob/master/examples/result_cascade.py) to see how the image below was generated.
        
        <p align="center"><img src="https://raw.githubusercontent.com/luizcarloscf/cv2utils/master/examples/images/result_cascade.jpg" align=middle width=700pt height=250pt/></p>
        
        
        ## References
        
        * [OpenCV HaarCascades](https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classifier.html)
        
        * [OpenCV Dnn](https://docs.opencv.org/master/d2/d58/tutorial_table_of_content_dnn.html)
        
        ## License
        
        [MIT](https://github.com/luizcarloscf/cv2utils/blob/master/LICENSE).
        
Keywords: dnn haar cascade face eye detection opencv numpy pip package
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
