yoloface 0.0.4
Simplest and efficient way to detect face
License: MIT
Keywords: face detection

# YOLO FACE
```bash
You only look once (YOLO) is a state-of-the-art, real-time object detection system. It is based on Deep Learning.
Face detection is one of the important tasks of object detection. We apply a single neural network to the full image.
This project focuses on improving the accuracy of detecting the face using the model of deep learning network (YOLO).
This network divides the image into regions and predicts bounding boxes and probabilities for each region.
These bounding boxes are weighted by the predicted probabilities.

For this project, I am going to use YOLOv3, one of the most frequently used versions of the YOLO family, which comprises the 
state-of-the-art object detection system for the real-time scenario and it is amazingly accurate and fast.
you can give  the weights file created by training with YOLOv3 and our results on the custom dataset.
Also it has been added configuration files for use of weights file properly. 
You want to test our face detection system you can use the following sample code sample.

```

## User Installation :
If you already have a working installation of numpy and pandas, opencv the easiest way to install yoloface is using pip
```bash
pip install yoloface
```



## This Package Depend On Other Packages:
```bash
# Prerequisites
  1.numpy
  2.cv2
  3.os 
  4.PIL
  5.gdown
  6.time
  7.IPython
```

# Usage

## Face Detection In Image
```python
# import libraries
from yoloface import face_analysis
import numpy
import cv2
face=face_analysis()        #  Auto Download a large weight files from Google Drive.
                            #  only first time.
                            #  Automatically  create folder .yoloface on cwd.
# example 1
%%time
img,box,conf=face.face_detection(image_path='path/to/jpg/or/png/filename.jpg',model='tiny')
print(box)                  # box[i]=[x,y,w,h]
print(conf)                 #  value between(0 - 1)  or probability
face.show_output(img,box)
```

```python
# example 2
%%time
img,box,conf=face.face_detection(image_path='path/to/jpg/or/png/filename.jpg',model='full')
print(box)
print(conf)
face.show_output(img,box)

```

## Real-Time Detection on a Webcam

```python
from yoloface import face_analysis
import numpy
import cv2

# example 3
cap = cv2.VideoCapture(0)
while True: 
    _, frame = cap.read()
    _,box,conf=face.face_detection(frame_arr=frame,frame_status=True,model='tiny')
    output_frame=face.show_output(frame,box,frame_status=True)
    cv2.imshow('frame',output_frame)
    key=cv2.waitKey(1)
    if key ==ord('v'): 
        break 
cap.release()
cv2.destroyAllWindows()
#press v (exits)
```



```python
# example 4
cap = cv2.VideoCapture(r'video file path.mp4')
while True: 
    _, frame = cap.read()
    __,box,conf=face.face_detection(frame_arr=frame,frame_status=True,model='full')
    output_frame=face.show_output(img=frame,face_box=box,frame_status=True)
    print(box)
    cv2.imshow('frame',output_frame)
    key=cv2.waitKey(0)
    if key ==ord('v'): 
        break 
cap.release()
cv2.destroyAllWindows()
#press v (exits)
```

## Sample images
## Output Image 1
![input](result1.png)

## Output Image 2
![input2](https://github.com/vishalbpatil1/yoloface/blob/main/ff4.png)

## Output Image 3
![input3](result2.png)

The YOLOv3 (You Only Look Once) is a state-of-the-art, real-time object detection algorithm. The published model recognizes 80 different objects in images and videos. For more details, you can refer to this paper.
[Reference link ](https://pjreddie.com/darknet/yolo/)


[Github file source fist](https://github.com/vishalbpatil1/Supper-face-detection-or-crowd-detection)
```bash

```
[Github file source second](https://github.com/vishalbpatil1/yoloface)


Change Log
==========
0.0.3 (28/04/2021)
-------------------
- First Release
-------------------
0.0.4 (29/04/2021)
-------------------
- First Release
-------------------