#
# Author: Yijun Zhang
#
import cv2
import math
import numpy as np
import skvideo.io as sv

cap = sv.VideoCapture('czy1.mp4')
out = sv.VideoWriter('czy_other.mp4', 'H264', 30.0, (640, 480), True)
print out.open()
ret, frame2 = cap.read()
current_frame = frame2


## extract background
fgbg = cv2.createBackgroundSubtractorKNN()

while True:
    ret, frame = cap.read()
    # The rgb structure is different in cv and scikit-video, thus reconstructing
    b, g, r = cv2.split(frame)
    ori_frame = frame
    ori_frame = cv2.merge([r, g, b])
    cv2.imshow('frame', ori_frame) ## the frame we get from the camera 
    # Edge Enhancement
    b, g, r = cv2.split(frame)
    b_gray = cv2.GaussianBlur(b, (5, 5), 0)
    b_edged = cv2.Canny(b_gray, 35, 125)
 
    g_gray = cv2.GaussianBlur(g, (5, 5), 0)
    g_edged = cv2.Canny(g_gray, 35, 125)
 
    r_gray = cv2.GaussianBlur(r, (5, 5), 0)
    r_edged = cv2.Canny(r_gray, 35, 125)
    
    edge_frame = cv2.merge([b + 0.1*b_edged, g + 0.1*g_edged, r + 0.1*r_edged])

    
    b, g, r = cv2.split(frame)
    ## using equalizehist to adjust the constrast of the image 
    b = cv2.equalizeHist(b)
    g = cv2.equalizeHist(g)
    r = cv2.equalizeHist(r)

    frame = cv2.merge([b, g, r])

    fmask = fgbg.apply(frame)

    #  denosing the area of motion  
    
    th = cv2.threshold(fmask.copy(), 244, 255, cv2.THRESH_BINARY)[1]
    th = cv2.erode(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), iterations=2)
    dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 3)), iterations=2)


    # if it is a stationary video, define the number 
    nonzeroNum = np.count_nonzero(dilated)
    if nonzeroNum == 0:
        min_x1 = 1
        max_x1 = 1
        min_y1 = 2
        max_y1 = 2
    else: 
        x1, y1 = np.nonzero(dilated)
        min_x1 = x1.min()+1
        max_x1 = x1.max()+1
        min_y1 = y1.min()+2
        max_y1 = y1.max()+2 

    # compute the minimal rect of motion area
    c = np.array([[max_y1,min_x1],[max_y1,max_x1],[min_y1,min_x1],[min_y1,max_x1]]) 
    rect = cv2.minAreaRect(c)
    box = np.int0(cv2.boxPoints(rect))
    

    ##  fill the motion area 
    cv2.drawContours(dilated, [box], -1, (0, 255, 0), 3)
    image1 = cv2.fillPoly(dilated, [box], (255, 255, 255));  

    ##  extract the motion area
    b, g, r = cv2.split(frame)

    res_b = b*(image1/255)
    res_g = g*(image1/255)
    res_r = r*(image1/255)
    ## merge the image 

    result = cv2.merge([res_r, res_g, res_b])
    realresult = cv2.merge([res_b, res_g, res_r])
    frame = cv2.merge([r, g, b])
    cv2.imshow('result', result) 
    out.write(realresult)


    k = cv2.waitKey(30) & 0xFF
    if k == 27:
        break

cv2.destroyAllWindows()
cap.release()