#!/usr/bin/env python

'''
Welcome to the Image Masking Program!

This program allows users to highlight a specific 
object within an image by masking it.

Usage:
  image_masking.py [<image>]

Keys:
  r     - mask the image
  SPACE - reset the inpainting mask
  ESC   - exit
'''

# Python 2/3 compatibility
from __future__ import print_function

import cv2 # Import the OpenCV library
import numpy as np # Import Numpy library
import matplotlib.pyplot as plt # Import matplotlib functionality
import sys # Enables the passing of arguments
from common import Sketcher

# Project: Image Masking Using OpenCV
# Author: Addison Sears-Collins
# Date created: 9/18/2019
# Python version: 3.7
# Description: This program allows users to highlight a specific 
# object within an image by masking it.

# Define the file name of the image
INPUT_IMAGE = "fruits.jpg"
IMAGE_NAME = INPUT_IMAGE[:INPUT_IMAGE.index(".")]
OUTPUT_IMAGE = IMAGE_NAME + "_output.jpg"
TABLE_IMAGE = IMAGE_NAME + "_table.jpg"

def main():
    """
    Main method of the program.
    """
    # Pull system arguments
    try:
        fn = sys.argv[1]
    except:
        fn = INPUT_IMAGE

    # Load the image and store into a variable
    image = cv2.imread(cv2.samples.findFile(fn))

    if image is None:
        print('Failed to load image file:', fn)
        sys.exit(1)

    # Create an image for sketching the mask
    image_mark = image.copy()
    sketch = Sketcher('Image', [image_mark], lambda : ((255, 255, 255), 255))

    # Sketch a mask
    while True:
        ch = cv2.waitKey()
        if ch == 27: # ESC - exit
            break
        if ch == ord('r'): # r - mask the image
            break
        if ch == ord(' '): # SPACE - reset the inpainting mask
            image_mark[:] = image
            sketch.show()

    # define range of white color in HSV
    lower_white = np.array([0,0,255])
    upper_white = np.array([255,255,255])

    # Create the mask
    mask = cv2.inRange(image_mark, lower_white, upper_white)

    # Create the inverted mask
    mask_inv = cv2.bitwise_not(mask)

    # Convert to grayscale image
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Extract the dimensions of the original image
    rows, cols, channels = image.shape
    image = image[0:rows, 0:cols]

    # Bitwise-OR mask and original image
    colored_portion = cv2.bitwise_or(image, image, mask = mask)
    colored_portion = colored_portion[0:rows, 0:cols]

    # Bitwise-OR inverse mask and grayscale image
    gray_portion = cv2.bitwise_or(gray, gray, mask = mask_inv)
    gray_portion = np.stack((gray_portion,)*3, axis=-1)

    # Combine the two images
    output = colored_portion + gray_portion

    # Save the image
    cv2.imwrite(OUTPUT_IMAGE, output)

    # Create a table showing input image, mask, and output
    mask = np.stack((mask,)*3, axis=-1)
    table_of_images = np.concatenate((image, mask, output), axis=1)
    cv2.imwrite(TABLE_IMAGE, table_of_images)

    # Display images, used for debugging
    #cv2.imshow('Original Image', image)
    #cv2.imshow('Sketched Mask', image_mark)
    #cv2.imshow('Mask', mask)
    #cv2.imshow('Output Image', output)
    cv2.imshow('Table of Images', table_of_images)
    cv2.waitKey(0) # Wait for a keyboard event

if __name__ == '__main__':
    print(__doc__)
    main()
    cv2.destroyAllWindows()

#!/usr/bin/env python

'''
This module contains some common routines used by other samples.
'''

# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3

if PY3:
    from functools import reduce

import numpy as np
import cv2 as cv

# built-in modules
import os
import itertools as it
from contextlib import contextmanager

image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']

class Bunch(object):
    def __init__(self, **kw):
        self.__dict__.update(kw)
    def __str__(self):
        return str(self.__dict__)

def splitfn(fn):
    path, fn = os.path.split(fn)
    name, ext = os.path.splitext(fn)
    return path, name, ext

def anorm2(a):
    return (a*a).sum(-1)
def anorm(a):
    return np.sqrt( anorm2(a) )

def homotrans(H, x, y):
    xs = H[0, 0]*x + H[0, 1]*y + H[0, 2]
    ys = H[1, 0]*x + H[1, 1]*y + H[1, 2]
    s  = H[2, 0]*x + H[2, 1]*y + H[2, 2]
    return xs/s, ys/s

def to_rect(a):
    a = np.ravel(a)
    if len(a) == 2:
        a = (0, 0, a[0], a[1])
    return np.array(a, np.float64).reshape(2, 2)

def rect2rect_mtx(src, dst):
    src, dst = to_rect(src), to_rect(dst)
    cx, cy = (dst[1] - dst[0]) / (src[1] - src[0])
    tx, ty = dst[0] - src[0] * (cx, cy)
    M = np.float64([[ cx,  0, tx],
                    [  0, cy, ty],
                    [  0,  0,  1]])
    return M


def lookat(eye, target, up = (0, 0, 1)):
    fwd = np.asarray(target, np.float64) - eye
    fwd /= anorm(fwd)
    right = np.cross(fwd, up)
    right /= anorm(right)
    down = np.cross(fwd, right)
    R = np.float64([right, down, fwd])
    tvec = -np.dot(R, eye)
    return R, tvec

def mtx2rvec(R):
    w, u, vt = cv.SVDecomp(R - np.eye(3))
    p = vt[0] + u[:,0]*w[0]    # same as np.dot(R, vt[0])
    c = np.dot(vt[0], p)
    s = np.dot(vt[1], p)
    axis = np.cross(vt[0], vt[1])
    return axis * np.arctan2(s, c)

def draw_str(dst, target, s):
    x, y = target
    cv.putText(dst, s, (x+1, y+1), cv.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv.LINE_AA)
    cv.putText(dst, s, (x, y), cv.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv.LINE_AA)

class Sketcher:
    def __init__(self, windowname, dests, colors_func):
        self.prev_pt = None
        self.windowname = windowname
        self.dests = dests
        self.colors_func = colors_func
        self.dirty = False
        self.show()
        cv.setMouseCallback(self.windowname, self.on_mouse)

    def show(self):
        cv.imshow(self.windowname, self.dests[0])

    def on_mouse(self, event, x, y, flags, param):
        pt = (x, y)
        if event == cv.EVENT_LBUTTONDOWN:
            self.prev_pt = pt
        elif event == cv.EVENT_LBUTTONUP:
            self.prev_pt = None

        if self.prev_pt and flags & cv.EVENT_FLAG_LBUTTON:
            for dst, color in zip(self.dests, self.colors_func()):
                cv.line(dst, self.prev_pt, pt, color, 5)
            self.dirty = True
            self.prev_pt = pt
            self.show()


# palette data from matplotlib/_cm.py
_jet_data =   {'red':   ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1),
                         (1, 0.5, 0.5)),
               'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1),
                         (0.91,0,0), (1, 0, 0)),
               'blue':  ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0),
                         (1, 0, 0))}

cmap_data = { 'jet' : _jet_data }

def make_cmap(name, n=256):
    data = cmap_data[name]
    xs = np.linspace(0.0, 1.0, n)
    channels = []
    eps = 1e-6
    for ch_name in ['blue', 'green', 'red']:
        ch_data = data[ch_name]
        xp, yp = [], []
        for x, y1, y2 in ch_data:
            xp += [x, x+eps]
            yp += [y1, y2]
        ch = np.interp(xs, xp, yp)
        channels.append(ch)
    return np.uint8(np.array(channels).T*255)

def nothing(*arg, **kw):
    pass

def clock():
    return cv.getTickCount() / cv.getTickFrequency()

@contextmanager
def Timer(msg):
    print(msg, '...',)
    start = clock()
    try:
        yield
    finally:
        print("%.2f ms" % ((clock()-start)*1000))

class StatValue:
    def __init__(self, smooth_coef = 0.5):
        self.value = None
        self.smooth_coef = smooth_coef
    def update(self, v):
        if self.value is None:
            self.value = v
        else:
            c = self.smooth_coef
            self.value = c * self.value + (1.0-c) * v

class RectSelector:
    def __init__(self, win, callback):
        self.win = win
        self.callback = callback
        cv.setMouseCallback(win, self.onmouse)
        self.drag_start = None
        self.drag_rect = None
    def onmouse(self, event, x, y, flags, param):
        x, y = np.int16([x, y]) # BUG
        if event == cv.EVENT_LBUTTONDOWN:
            self.drag_start = (x, y)
            return
        if self.drag_start:
            if flags & cv.EVENT_FLAG_LBUTTON:
                xo, yo = self.drag_start
                x0, y0 = np.minimum([xo, yo], [x, y])
                x1, y1 = np.maximum([xo, yo], [x, y])
                self.drag_rect = None
                if x1-x0 > 0 and y1-y0 > 0:
                    self.drag_rect = (x0, y0, x1, y1)
            else:
                rect = self.drag_rect
                self.drag_start = None
                self.drag_rect = None
                if rect:
                    self.callback(rect)
    def draw(self, vis):
        if not self.drag_rect:
            return False
        x0, y0, x1, y1 = self.drag_rect
        cv.rectangle(vis, (x0, y0), (x1, y1), (0, 255, 0), 2)
        return True
    @property
    def dragging(self):
        return self.drag_rect is not None


def grouper(n, iterable, fillvalue=None):
    '''grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx'''
    args = [iter(iterable)] * n
    if PY3:
        output = it.zip_longest(fillvalue=fillvalue, *args)
    else:
        output = it.izip_longest(fillvalue=fillvalue, *args)
    return output

def mosaic(w, imgs):
    '''Make a grid from images.

    w    -- number of grid columns
    imgs -- images (must have same size and format)
    '''
    imgs = iter(imgs)
    if PY3:
        img0 = next(imgs)
    else:
        img0 = imgs.next()
    pad = np.zeros_like(img0)
    imgs = it.chain([img0], imgs)
    rows = grouper(w, imgs, pad)
    return np.vstack(map(np.hstack, rows))

def getsize(img):
    h, w = img.shape[:2]
    return w, h

def mdot(*args):
    return reduce(np.dot, args)

def draw_keypoints(vis, keypoints, color = (0, 255, 255)):
    for kp in keypoints:
        x, y = kp.pt
        cv.circle(vis, (int(x), int(y)), 2, color)

# Python program for blending multiple images using OpenCV

import glob
import numpy as np
import cv2

# Import all image files with the .jpg extension
files = glob.glob ("*.jpg")
image_data = []
for my_file in files:
    this_image = cv2.imread(my_file, 1)
    image_data.append(this_image)

# Calculate blended image
dst = image_data[0]
for i in range(len(image_data)):
	if i == 0:
		pass
	else:
		alpha = 1.0/(i + 1)
		beta = 1.0 - alpha
		dst = cv2.addWeighted(image_data[i], alpha, dst, beta, 0.0)

# Save blended image
cv2.imwrite('weather_forecast.png', dst)