#!/usr/bin/python3
# https://pyimagesearch.com/2018/07/19/opencv-tutorial-a-guide-to-learn-opencv/
# import the necessary packages
import cv2
import imutils
import numpy as np
import argparse

def rescale_frame(frame, percent): # rescale image considering aspect ratio
    width = int(frame.shape[1] * percent/ 100)
    height = int(frame.shape[0] * percent/ 100)
    dim = (width, height)
    return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)
    # alternative: resized = imutils.resize(image, width=300) => give goal height or width

def extract_channels(image,color_space):
    (ch1,ch2,ch3) = cv2.split(image)
    cv2.imshow("Channel "+color_space[0],ch1)
    cv2.imshow("Channel "+color_space[1],ch2)
    cv2.imshow("Channel "+color_space[2],ch3)

def rotate_bound(image, angle):
    (h, w) = image.shape[:2] # image dimesnsions
    (cX, cY) = (w // 2, h // 2) # center of image 
    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0) # neg angle to rotate clockwise
    cos = np.abs(M[0, 0]) # cos & sin value from rotation matrix
    sin = np.abs(M[0, 1])
    # compute the new bounding dimensions of the image
    nW = int((h * sin) + (w * cos))
    nH = int((h * cos) + (w * sin))
    # adjust the rotation matrix to take into account translation
    M[0, 2] += (nW / 2) - cX
    M[1, 2] += (nH / 2) - cY
    # perform the actual rotation and return the image
    return cv2.warpAffine(image, M, (nW, nH))


# load the input image and show its dimensions, keeping in mind that
# images are represented as a multi-dimensional NumPy array with
# shape no. rows (height) x no. columns (width) x no. channels (depth)
image = cv2.imread('/home/caroline/Pictures/first.jpg') #numpy array

# resize image ignoring aspect ratio
resized_image = cv2.resize(image, (300, 300))
cv2.imshow("Fixed Resizing", resized_image)
cv2.waitKey(0)

image = rescale_frame(image,20)


(h, w, d) = image.shape #height=rows, width=columns, depth=channels => 3 for RGB
print("width={}, height={}, depth={}".format(w, h, d))
# display the image to our screen -- we will need to click the window
# open by OpenCV and press a key on our keyboard to continue execution
cv2.imshow("Image", image)
cv2.waitKey(0)

# grayscale value 0-255
# (B, G, R) with range [0, 255]

# access the RGB pixel located at x=50, y=100, keepind in mind that
# OpenCV stores images in BGR order rather than RGB
(B, G, R) = image[100, 50]
print("R={}, G={}, B={}".format(R, G, B))

# array slicing and cropping
# extract a 100x100 pixel square ROI (Region of Interest) from the
# input image starting at x=320,y=60 at ending at x=420,y=160
""" roi = image[60:160, 320:420]
cv2.imshow("ROI", roi)
cv2.waitKey(0) """

# transform image into different color spaces and split channels 
""" image_lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
extract_channels(image_lab,"LAB") # lightness, red/green value, blue/yellow value => very vast color space 
cv2.waitKey(0)
image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # hue:color 0°-360°, saturation: amount of grey 0-1, value: brightness/intensity 0-1
extract_channels(image_hsv,"HSV")
cv2.waitKey(0) """

# rotate image 
# loop over rotation angles without cutting image
""" for angle in np.arange(0, 360, 30): # range [0, 360] in 15° increments
	rotated = rotate_bound(image, angle)
	cv2.imshow("Rotated", rotated)
	cv2.waitKey(0) """

# blurring: reduce high-frequency noise => gaussianBlur function
blurred = cv2.GaussianBlur(image, (11, 11), 0) # 11x11 kernel: larger kernel create more blurry images 
cv2.imshow("Blurred", blurred)
cv2.waitKey(0)