# -*- coding: utf-8 -*-
"""
Created on Fri Mar  8 17:29:18 2024
https://www.bogotobogo.com/python/OpenCV_Python/python_opencv3_mean_shift_tracking_segmentation.php
"""
import numpy as np
import cv2 as cv
import time
import matplotlib.pyplot as plt

roi = cv.imread('Image1.png'); roi_rgb=cv.cvtColor(roi, cv.COLOR_BGR2RGB);
target = cv.imread('Image2.png'); target_rgb=cv.cvtColor(target, cv.COLOR_BGR2RGB);


roi_hsv = cv.cvtColor(roi,cv.COLOR_BGR2HSV)
x=y=200; h=w=100; roi_hsv = roi_hsv[y:y+h,x:x+w,:]
target_hsv  = cv.cvtColor(target,cv.COLOR_BGR2HSV)

roihist = cv.calcHist([roi_hsv],[0], None, [180], [0, 180] )
cv.normalize(roihist,roihist,0,255,cv.NORM_MINMAX)
dst = cv.calcBackProject([target_hsv],[0],roihist,[0,180],1)

# # calculating object histogram
# roihist = cv.calcHist([roi_hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] )

# # normalize histogram and apply backprojection
# cv.normalize(roihist,roihist,0,255,cv.NORM_MINMAX)

# # Back Projection is a way of recording how well the pixels of a given image fit the distribution of pixels in a histogram model.
# # calculate the histogram model of a feature and then use it to find this feature in an image.
# dst = cv.calcBackProject([target_hsv],[0,1],roihist,[0,180,0,256],1)

# Now convolute with circular disc
# disc = cv.getStructuringElement(cv.MORPH_ELLIPSE,(5,5))
# cv.filter2D(dst,-1,disc,dst)

rec = cv.rectangle(roi_rgb, (x,y), (x+w,y+h), 255,2)

plt.figure(1)
plt.subplot(221); plt.imshow(roi_rgb); plt.axis('off')
plt.subplot(222); plt.imshow(target_rgb); plt.axis('off')
plt.subplot(223); plt.stem(roihist); 
plt.subplot(224); plt.imshow(dst,cmap='gray'); plt.axis('off')


"Tracking using meanshift"

# cap = cv.VideoCapture('traffic.mp4')

# # take first frame of the video
# ret,frame = cap.read()

# # setup initial location of window
# x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
# track_window = (x, y, w, h)

# # set up the ROI for tracking
# roi = frame[y:y+h, x:x+w]
# hsv_roi =  cv.cvtColor(roi, cv.COLOR_BGR2HSV)
# mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
# roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
# cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)

# # Setup the termination criteria, either 10 iteration or move by at least 1 pt
# term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )

# while(1):
#     ret, frame = cap.read()

#     if ret == True:
#         hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
#         dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)

#         # apply meanshift to get the new location
#         ret, track_window = cv.meanShift(dst, track_window, term_crit)

#         # Draw it on image
#         x,y,w,h = track_window
#         img2 = cv.rectangle(frame, (x,y), (x+w,y+h), 255,2)
#         time.sleep(1/4)   
#         cv.imshow('Tracking using meanshift',img2)

#         if cv.waitKey(1) == ord('q'):    break

#     else:
#         break

# "Tracking using camshift"

# cap = cv.VideoCapture('traffic.mp4')
# # take first frame of the video
# ret,frame = cap.read()

# # setup initial location of window
# x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
# track_window = (x, y, w, h)

# # set up the ROI for tracking
# roi = frame[y:y+h, x:x+w]
# hsv_roi =  cv.cvtColor(roi, cv.COLOR_BGR2HSV)
# mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
# roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
# cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)

# # Setup the termination criteria, either 10 iteration or move by at least 1 pt
# term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )

# while(1):
#     ret, frame = cap.read()

#     if ret == True:
#         hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
#         dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)

#         # apply camshift to get the new location
#         ret, track_window = cv.CamShift(dst, track_window, term_crit)

#         # Draw it on image
#         pts = cv.boxPoints(ret)
#         pts = np.int0(pts)
#         img2 = cv.polylines(frame,[pts],True, 255,2)
#         time.sleep(1/4)  
#         cv.imshow('Tracking using camshift',img2)
#         if cv.waitKey(1) == ord('q'):    break

#     else:
#         break

''' https://docs.opencv.org/3.4.15/da/d7f/tutorial_back_projection.html
In terms of statistics, the values stored in BackProjection represent the probability
 that a pixel in Test Image belongs to the same area, based on the model histogram that we use.
 For instance in our Test image, the brighter areas are more probable to be our tracked area 
 (as they actually are), whereas the darker areas have less probability.'''

roihist = cv.calcHist([roi_hsv],[0], None, [180], [0, 180] )
cv.normalize(roihist,roihist,0,255,cv.NORM_MINMAX)
dst = cv.calcBackProject([target_hsv],[0],roihist,[0,180],1)