Car & Pedestrian Detection.py

#!/usr/bin/env python
# coding: utf-8

# ## Car & Pedestrian Detection
# 
# 

# ### Pedistrian Detection

# In[1]:


import cv2
import numpy as np

# Create our body classifier
body_classifier = cv2.CascadeClassifier('Haarcascades\haarcascade_fullbody.xml')

# Initiate video capture for video file
cap = cv2.VideoCapture('image_examples/walking.avi')

# Loop once video is successfully loaded
while cap.isOpened():
    
    # Read first frame
    ret, frame = cap.read()
    #frame = cv2.resize(frame, None,fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # Pass frame to our body classifier
    bodies = body_classifier.detectMultiScale(gray, 1.2, 3)
    
    # Extract bounding boxes for any bodies identified
    for (x,y,w,h) in bodies:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 255), 2)
        cv2.imshow('Pedestrians', frame)

    if cv2.waitKey(1) == 13: #13 is the Enter Key
        break

cap.release()
cv2.destroyAllWindows()


# ### Car Detection
# 

# In[ ]:


import cv2
import time
import numpy as np

# Create our body classifier
car_classifier = cv2.CascadeClassifier('Haarcascades\haarcascade_car.xml')

# Initiate video capture for video file
cap = cv2.VideoCapture('image_examples/cars.avi')


# Loop once video is successfully loaded
while cap.isOpened():
    
    time.sleep(.05)
    # Read first frame
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
   
    # Pass frame to our car classifier
    cars = car_classifier.detectMultiScale(gray, 1.4, 2)
    
    # Extract bounding boxes for any bodies identified
    for (x,y,w,h) in cars:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 255), 2)
        cv2.imshow('Cars', frame)

    if cv2.waitKey(1) == 13: #13 is the Enter Key
        break

cap.release()
cv2.destroyAllWindows()


# In[ ]: