Vison_exam_01-06-2018

import cv2
import numpy as np
import matplotlib.pyplot as plt



def Isolate_Objects(img):
 
	# Converting it into grayscale
	gray =  cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
	
	getGrayHisto(gray)

	
	#Slow way
	'''
	# goes through every pixel evaluates if it has  a grayscale level higher than 190, if it does it turns it black
	for i in range(gray.shape[0]): # travels across the hight of the image
		for j in range (gray.shape[1]): # travels across the width of the image
			if (gray[i][j] > 190): # and gray[i][j] < 220):
				gray[i][j] = 0
	'''
	
	
	#https://docs.opencv.org/3.4.0/d7/d4d/tutorial_py_thresholding.html
	# if pixel value is greater than a threshold value, it is assigned one value (may be white), else it is assigned another value (may be black)
	
	#Otsu thresholding
	ret, gray = cv2.threshold(gray, 120 , 130, cv2.THRESH_BINARY_INV)
	
	#multi_colour
	#ret, gray = cv2.threshold(gray, 200 , 255, cv2.THRESH_BINARY_INV)


	showImage(gray)
	
	img_closed = Morphological_Close(gray)
	showImage(img_closed)
	
	img_flooded = floodfill(img_closed)
	showImage(img_flooded)
	
	#Count_Objcets = count(img_flooded)
	#showImage(Count_Objcets)
	
 
def Morphological_Close(gray):

	kernel = np.ones((5,5),np.uint8)
	bw_close = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel)
	gray = bw_close
	return (gray)
	
def floodfill(Imgflood):
	# Mask used to flood filling.
	# Notice the size needs to be 2 pixels than the image.
	h, w = Imgflood.shape[:2]
	filled_flood = Imgflood
	mask = np.zeros((h + 2, w + 2), np.uint8)
	
	# Floodfill from point (0, 0)
	cv2.floodFill(filled_flood, mask, (0, 0), 255);
	
	# Invert floodfilled image
	FloodFill_Inverse = cv2.bitwise_not(filled_flood)
	
	flood = Imgflood | FloodFill_Inverse

	
	return(flood)
	
def showImage(ima):
	cv2.namedWindow('lego_cars_issolatated', cv2.WINDOW_KEEPRATIO)

	# Command will show the image 
	cv2.imshow('lego_cars_issolatated', ima)

	# will wait to execute the rest of the script (close the image), till the user have pressed a key
	cv2.waitKey()
	cv2.destroyAllWindows()
	
	
	## Finds the area where there is the highest concentration of white points.
def meanshift(frame, mask, x, y, width, height, term_criteria, prior_state):
		
	ret, track_window = cv2.meanShift(mask, (x, y, width, height), term_criteria)
	
	x,y,w,h = track_window
	cv2.rectangle(frame, (x,y), (x + w, y+h), (0, 255, 0), 2)
	
	cv2.imshow('mask', mask)
	cv2.imshow('frame', frame)	
	
	return (x,y)
	
def camshift(frame, mask, x, y, width, height, term_criteria, prior_state):
	
	
	prior_state.append([x,y])
	print (prior_state)
	ret, track_window = cv2.CamShift(mask, (x, y, width, height), term_criteria)
	
	
	if ret[0][0] != 0:
	
		frame = box_it(ret, frame)

	else:
		x,y = prediction()
		ret, track_window = cv2.CamShift(mask, (x, y, width, height), term_criteria)
		frame = box_it(ret, frame)

		
	x,y,w,h = track_window
	
	cv2.imshow('mask', mask)
	cv2.imshow('frame', frame)	
	
	return (x,y)
	
	
def box_it(ret, frame):
	# discontinued using the rectangle function, as but used polylines instead
	points = cv2.boxPoints(ret)
	points = np.int0(points)
	cv2.polylines(frame, [points], True, (0, 255, 0), 2)

	return (frame)
	
def eclipse_it(ret, frame):
	points = cv2.fitEllipse(ret)
	cv2.ellipse(f,ellipse,(0,255,0),2)
	cv2.polylines(frame, [points], True, (0, 255, 0), 2)
	
	return (frame)
	
def getGrayHisto(gray):
	
# shows the histogram of the grayscale picture
	hist = cv2.calcHist ([gray], [0], None, [256], [0,256])

	plt.subplot(211)

	plt.plot(hist)

	plt.show()


def getROI(frame, x, y, width, height):



		roi = frame[y: y+ height, x: x+ width]
		return (roi)
		
def prediction():
	
	x = 580
	y = 285
	width = 300
	height = 200

	return(x, y)

	

	#video
video = cv2.VideoCapture("/Users/xr081/Desktop/Vision_exam/Chosen_video.mp4")

ret, frame = video.read()
	
x = 5
y = 385 
width = 30
height = 45

prior_state = []

'''
#delete
x = 580
y = 285
width = 300
height = 200

ROI = getROI(frame, x, y, width, height)

cv2.imshow('test',ROI)
cv2.waitKey()
# to here
'''


ROI = getROI(frame, x, y, width, height)

hsv_roi = cv2.cvtColor(ROI, cv2.COLOR_BGR2HSV)

# cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]])
roi_hist = cv2.calcHist([hsv_roi], [0], None, [180], [0,180])

# as can be seen, we need to normalise the histogram, so the maximum becomes 255
roi_hist = cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
#print (roi_hist)

term_criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)


while True:
	_, frame = video.read()

	hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

	# we find the back projection : https://docs.opencv.org/3.3.1/dc/df6/tutorial_py_histogram_backprojection.html
	# calcBackProject didn't perform as expected
	#mask = cv2.calcBackProject([hsv], [0], roi_hist, [0,180], 1)

	# define range of yellow color in HSV
	lower_yellow = np.array([20, 100, 100])
	upper_yellow = np.array([30, 255, 255])
	
	# Threshold the HSV image to get only yellow colors
	mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
	
	mask = Morphological_Close(mask)
	#getGrayHisto(mask)
	

	#x,y = meanshift(frame, mask, x, y, width, height, term_criteria)
	
	# had to move away from meanShift as it lost the object
	x, y = camshift(frame, mask, x, y, width, height, term_criteria, prior_state)
	
	cv2.waitKey()
	
	key = cv2.waitKey(32)
	if key == 27:
		break
video.release()
cv2.destroyAllWindows()