Face detection Open cv

Image-Processing/Face_detection/Face_detection.py

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+import cv2    ##Import OpenCv
+
+face = cv2.CascadeClassifier('C:/Users/91976/Desktop/haarcascade_frontalface_default.xml') ##'haarcascade_frontalface_default.xml' an opencv classifier for face detection 
+cap = cv2.VideoCapture(0) ##capturevideo by webcam or your laptop default cam for webcame  => 1 and for defalut laptop camera => 2
+
+while True:
+    sucess , img = cap.read()
+    gray = cv2.cvtColor( img , cv2.COLOR_BGR2GRAY ) ##before detecting  face you should convert img or video into gray image
+    faces = face.detectMultiScale( gray ,1.1 ,5 )
+
+    for ( x , y , w , h) in faces:
+        cv2.rectangle(img,( x , y ),( x*w , y*h ),(255,0,0),2)  ##will create the rectangle where face is detected
+
+    cv2.imshow('img',img) 
+    k = cv2.waitKey(30) & 0xff
+    if k == 27:
+        break
+
+cap.release()

Image-Processing/Face_detection/README.md

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+# Image Processing
+
+Image Processing is most commonly termed as 'Digital Image Processing' and the domain in which it is frequently used is 'Computer Vision'. 
+Don't be confused - we are going to talk about both of these terms and how they connect. 
+Both Image Processing algorithms and Computer Vision (CV) algorithms take an image as input; however, in image processing,
+the output is also an image, whereas in computer vision the output can be some features/information about the image.
+
+## OpenCV
+
+![](https://logodix.com/logo/1989939.png)
+
+## Installation
+
+### Windows
+
+ $ pip install opencv-python
+### MacOS
+   $ brew install opencv3 --with-contrib --with-python3
+### Linux
+   $ sudo apt-get install libopencv-dev python-opencv