Project 8

import cv2
# OpenCV-Python is a library of Python bindings designed to solve computer vision problems.
import numpy as np # np is an alias for numpy
# general-purpose array-processing package.
# It provides a high-performance multidimensional array object,
# and tools for working with these arrays

#tracking features, Identifying corners through
#shi-tomasi corner detector method

def find_features(gray_image):
# Extracting the features from the gray scaled image using cv2.goodFeaturesToTrack method
# No masking
# Here Maximum Corners detection is 100
# Quality Level is 0.1
# Minimum distance between two features is 10 to avoid features that are closely spaced
# Window size is 5
points = cv2.goodFeaturesToTrack(gray_image,mask=None,maxCorners=100,qualityLevel=0.1,minDistance=10,blockSize=5)
return points

# Lucas Kanade sparse optical flow model

def LK_model(gray_t_1,gray_t,prev_points):
# Window size = (10,10)
# Max level means Level of the Pyramid
# Criteria is for setup to perform the operation
points,status,error = cv2.calcOpticalFlowPyrLK(gray_t_1,gray_t,prev_points,None,winSize=(10,10),maxLevel=2,criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
return (points,status,error)

# Reading MP4 files using cv2.VideoCapture
capture = cv2.VideoCapture("traffic.mp4")
# Initiate Color
color = np.random.randint(0,255,(100,3))
# Reading the image/frame i.e., (T-1)th frame from the video
ret,prev_frame = capture.read()
# Converting (T-1)th frame into GRAY Scaled image
prev_gray = cv2.cvtColor(prev_frame,cv2.COLOR_BGR2GRAY)
# Finding the features such as corner using shi-tomasi corner detector method
prev_points = find_features(prev_gray)
# Creating mask image
mask_image = np.zeros_like(prev_frame)

img_array = []

while(1):
# Read the next (T)th frame
ret,frame = capture.read()

# Check whether the video is finished or not, If frame is None then its End of video
# Then Destroy all the windows
if frame is None:
cv2.destroyAllWindows()
break

# Converting current (T)th frame into GRAY Scaled image
current_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)

# Calculating the current points, status and error using Lucas Kanade model
current_points,status,error = LK_model(prev_gray,current_gray,prev_points)

# Check Current points
if current_points is not None:
current_points = current_points[status==1]
old_points = prev_points[status==1]

# Mapping
for i,(current,prev) in enumerate(zip(current_points,old_points)):
a,b = current.ravel()
c,d = prev.ravel()
# Masking the screen with black
mask_image = cv2.line(mask_image,(int(a),int(b)),(int(c),int(d)),color[i].tolist(),2)
# Plotting the features
frame = cv2.circle(frame,(int(a),int(b)),5,color[i].tolist(),-1)

img = cv2.add(frame,mask_image)

height,width,layers = img.shape
size = (width,height)
img_array.append(img)

#display image
cv2.imshow('frame',img)

k = cv2.waitKey(5)
if k == 27:
cv2.destroyAllWindows()
break

#set the current frame as the (T-2)th frame and update the current points as the previous points(T-1)th
prev_gray = current_gray.copy()
prev_points = current_points.reshape(-1,1,2)

# Collecting the frames and writes into the Video MP4 file
out = cv2.VideoWriter('ProcessedVideo.mp4',cv2.VideoWriter_fourcc(*'DIVX'),15,size)
for i in range(len(img_array)):
out.write(img_array[i])
out.release()