Added python code for face detection

face_detection.py

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+import cv2
+import numpy as np
+
+# Video pocessing using Opencv
+
+
+
+# to run a stored media file - try to keep in same dir as code
+
+# cap = cv2.VideoCapture("sample.mp4")
+cap = cv2.VideoCapture(0) # index = 0 for webcam 
+
+
+
+if cap.isOpened()==False :
+    print("error displaying video from webcam")
+
+
+# to read entire video file , read it frame-by-frame until its completed
+
+# cap.read()   where cap = video -> reads entire vidso file frame-by-frame
+# returns ret(bool) and frame(image - numpy 2D / 3D array)
+# ret = bool shows wehter curr. frame was succesfully read or not
+# frame = the 2D /3D np array =- image / curr frame to be displayed / analysed
+
+
+fourcc = cv2.VideoWriter_fourcc(*'XVID') # saved file  =.avi format
+
+out = cv2.VideoWriter("output.avi" , fourcc , 21 , (800 , 800)) # its just a temporary memory loc top store processed video frames .
+
+while cap.isOpened()==True :
+
+    ret , frame = cap.read()
+
+    if ret==True :
+        # process the video files and save its frame colectively
+        # straigthen the image by median blur
+        frame1 = cv2.medianBlur(frame , 5)
+        img = cv2.cvtColor(frame1 , cv2.COLOR_BGR2GRAY)
+
+        edges = cv2.Canny(img , 30 , 200) # Canny edge detection 
+
+        contours , heirarchy = cv2.findContours(edges , cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_NONE)
+
+        cv2.drawContours(frame, contours , -1 , (0, 255, 0) , 1)
+
+        # for face detection , use Haar Cascade Classifier
+        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+        # detect faces in grayscale
+        faces = face_cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))
+
+        for (x, y, w, h) in faces:
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
+            cv2.putText(frame , "Face Detected!!" , (x , y) ,cv2.FONT_HERSHEY_SIMPLEX  ,1 , (0 , 0 , 255) , 2)
+
+        print(faces) # coordinates of detected face 
+
+        cv2.imshow("current frame " , frame)
+
+        # save each frame in output video file declared at start - by - out.write(frame)
+        out.write(img)
+
+ # while showing frame (image) , wait for 25 millisecond and if pressed key is - 'q' - ASCII(ord) code of presed key(0xFF) , then break  the while loop an dend the video
+        if cv2.waitKey(25) & 0xFF == ord('q'):
+            break
+
+    else : # if ret = false , it means , file not read succesfuly , so break the loop
+        break
+     #if frame succesfuly read , display the frame
+
+# to release system memory from ref var where video was stored
+cap.release()
+
+
+# release space from temporary out file as the actual procese fiel has been saved on sidk as output.avi
+out.release()
+cv2.destroyAllWindows()
+
+'''' 
+Frames - particular instances of video = images / 2D/3D matrix
+multiple frames can pass in 1 sec in video
+
+every operation that can be performed on img , can also be performed on frames
+'''
+
+
+