cv2utils

Implementation of some object detection in Python3.5+.

Installation

It can be installed through pip:

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:

>>> 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 to see how the image below was generated.

OpenCV Face Cascade and Eye Cascade

The following example illustrates the ease of use of this package:

>>> 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 three 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 to see how the image below was generated.

References

OpenCV HaarCascades OpenCV Dnn

License

MIT.