import cv2

# Constant variables definition.
DESIRED_HEIGHT = 480		# The input image will be resized to this height, preserving its aspect ratio.
BLUE_THRESHOLD = 150		# If the blue channel is bigger than this, it is considered background and removed.
BINARY_THRESHOLD = 30		# If the pixel is not brighter than this, it is removed before detection.
CANNY_LOW_THRES = 150		# Low threshold for the canny edge detector.
CANNY_HIGH_THRES = 350		# High threshold for the canny edge detector.
LINE_THICKNESS = 2			# Thickness of the drawn lines.
MIN_CONTOUR_AREA = 100		# Min area of a contour to be considered valid.
MAX_CONTOUR_AREA = 2100		# Max area of a contour to be considered valid.
BLUR_KERNEL_SIZE = 3		# The size of the Gaussian blur kernel.
DILATION_KERNEL_SIZE = 5	# The size of the dilation kernel.
DILATION_ITERATIONS = 5		# The number of dilation iterations.
MIN_DISTANCE_FOR_MOVE = 10	# Min distance of the drone from the center for the servos to move.

# Colors (assuming the default BGR order).
RED = (0, 0, 255)
GREEN = (0, 255, 0)
BLUE = (255, 0, 0)
YELLOW = (0, 255, 255)

# -------------- Function definitions -----------------------------
def resizeImage(img):
	"Resize the input image based on the DESIRED_HEIGHT variable."
	p = img.shape
	aspectRatio = p[0]/p[1]
	width = DESIRED_HEIGHT*aspectRatio
	img = cv2.resize(img, ( DESIRED_HEIGHT, int(width) ))
	return img

def removeColors(img):
	out = None
	dim = img.shape
	blue = img.copy()
	for i in range(dim[0]):
		for j in range(dim[1]):
			pixel = img[i,j]
			if pixel[0] > 150:
				blue[i,j,:] = 255
			else:
				blue[i,j,:] = 0
	
	gray = cv2.cvtColor(blue, cv2.COLOR_BGR2GRAY)
	_, contours, hierarchy = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
	if len(contours) > 0:
		maxContour = max(contours, key = cv2.contourArea)
		x,y,w,h = cv2.boundingRect(maxContour)
		out = img[y:y+h,x:x+w,:]

	return out

def findMatchingContour(img, objX, objY):

	dilated = img.copy()
	#dilated = cv2.dilate(img, (5,5), iterations=1)
	canny = cv2.Canny(dilated, CANNY_LOW_THRES, CANNY_HIGH_THRES)
	_, contours, hierarchy = cv2.findContours(canny, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
	
	#print('len:' + str(len(contours)))
	contours.sort(key = cv2.contourArea, reverse = True)
	
	cv2.imshow('hey', canny)
	for i in range(len(contours)):
		contour = contours[i]
		x,y,w,h = cv2.boundingRect(contour)

		area = w*h
		dist = cv2.pointPolygonTest(contour, (objX,objY), False)
		#print('dist: ' + str(dist))
		if area >= MIN_CONTOUR_AREA and area <= MAX_CONTOUR_AREA and dist >= 0:
			return (True, contour)

	return (False, None)

def processImage(img):

	# Resize image to the desired height.
	resized = resizeImage(img)
	#return removeColors(resized)
	dim = resized.shape

	# Convert to grayscale.
	gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
	
	# Blur the image.
	blur = cv2.GaussianBlur(gray, (BLUR_KERNEL_SIZE, BLUR_KERNEL_SIZE), 0)

	# Threshold the image and find its contours.
	_, imgThres = cv2.threshold(blur, BINARY_THRESHOLD, 255, cv2.THRESH_BINARY_INV)
	
	# Dilate the image.
	dilated = cv2.dilate(imgThres, (DILATION_KERNEL_SIZE,DILATION_KERNEL_SIZE), iterations=DILATION_ITERATIONS)
	
	# Find the largest image contour.
	_, contours, hierarchy = cv2.findContours(dilated, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
	if len(contours) > 0:
		maxContour = max(contours, key = cv2.contourArea)
	else:
		print('No contours found.')
		return (resized, 0, 0)

	# Get the bounding rectangle of the contour.
	x,y,w,h = cv2.boundingRect(maxContour)

	# Get the centroid of the rectangle.
	objCenterX = int( (x + x + w) / 2)
	objCenterY = int( (y + y + h) / 2)
	imgCenterX = int(dim[1]/2)
	imgCenterY = int(dim[0]/2)
	#cv2.circle(resized, (objCenterX, objCenterY), 5, BLUE, LINE_THICKNESS)
	ret, finalContour = findMatchingContour(blur, objCenterX, objCenterY)
	if (ret == False):
		return (resized, 0, 0)
	
	#cv2.fillPoly(resized, finalContour, BLUE, cv2.LINE_4)
	x,y,w,h = cv2.boundingRect(finalContour)
	objCenterX = int( (x + x + w) / 2)
	objCenterY = int( (y + y + h) / 2)

	# Draw the bounding rectangle  and its centroid to the image.
	#cv2.circle(resized, (objCenterX, objCenterY), 5, YELLOW, LINE_THICKNESS)
	cv2.rectangle(resized, (x,y), (x+w,y+h), RED, LINE_THICKNESS)

	# Determinate the direction of the object relative to the center of the camera.
	xDir, yDir = determinateDir(imgCenterX, imgCenterY, objCenterX, objCenterY)

	return (resized, xDir, yDir)

def determinateDir(cenX, cenY, objX, objY):
	xDir = 0
	yDir = 0

	if abs(cenX - objX) >= MIN_DISTANCE_FOR_MOVE:
		if objX > cenX:
			xDir = 1
		else:
			xDir = -1
	if abs(cenY - objY) >= MIN_DISTANCE_FOR_MOVE:
		if objY > cenY:
			yDir = -1
		else:
			yDir = 1
	return (xDir, yDir)

#####################################################################################


cap = cv2.VideoCapture('/home/stelios/Desktop/drone_flight_test (cut).mp4')
if (cap.isOpened() == False):
	print('Error opening stream.')
	quit()
	
#cap.set(1, 30*6)

while(cap.isOpened()):
	ret, frame = cap.read()
	if (ret == True):
		img, xDir, yDir = processImage(frame)
		cv2.imshow('Frame', img)

		print('Got ' + str(xDir) + ' ' + str(yDir))

		k = cv2.waitKey(25) & 0xFF
		if k == 27:
			break
		if k == ord('p') or k == ord('P'):
			cv2.waitKey(0)

	else:
		break
cap.release()
cv2.destroyAllWindows()