updating readme. updating car detection file.

README.md

@@ -1,6 +1,15 @@
 # ml
 ml
 
+# Repo Components
+## Computer Vision
+TRAIN YOUR OWN OPENCV HAAR CLASSIFIER - https://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html
+
+## Natural Language Processing
+
+## Time Series Analysis
+
+## General Data Analysis
 
 # Learning Resources
 https://machinelearningmastery.com/start-here/#getstarted

cv/car_detection.py

@@ -7,29 +7,31 @@
 
 # capture frames from a video 
 cap = cv2.VideoCapture(os.path.join( FILE_DIR, 'video.avi'))
-
+#cap = cv2.VideoCapture(os.path.join( FILE_DIR, "Dash-Cam Videos That'll Make You Say - WTF!-0SXAkpxF6_k.mp4"))
+
+
 # Trained XML classifiers describes some features of some object we want to detect 
 car_cascade = cv2.CascadeClassifier(os.path.join( FILE_DIR, 'cars.xml'))
 
 # loop runs if capturing has been initialized. 
 while True: 
     # reads frames from a video 
     ret, frames = cap.read() 
-      
+
     # convert to gray scale of each frames 
     gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY) 
-      
+
 
     # Detects cars of different sizes in the input image 
     cars = car_cascade.detectMultiScale(gray, 1.1, 1) 
-      
+
     # To draw a rectangle in each cars 
     for (x,y,w,h) in cars: 
         cv2.rectangle(frames,(x,y),(x+w,y+h),(0,0,255),2) 
-
+    
     # Display frames in a window  
     cv2.imshow('video2', frames) 
-      
+
     # Wait for Esc key to stop 
     if cv2.waitKey(33) == 27: 
         break

cv/python_background_subtraction.py

@@ -1,8 +1,8 @@
 # Python code for Background subtraction using OpenCV 
 import numpy as np 
 import cv2 
-  
-cap = cv2.VideoCapture('/home/username/Downloads/people-walking.mp4') 
+
+cap = cv2.VideoCapture('Two people walk up to home, walk away after seeing security camera-bFrwLUjXcLs.mp4')
 fgbg = cv2.createBackgroundSubtractorMOG2() 
 
 while(1):