horizon.py

import sys
import cv2
import os
import numpy as np
from matplotlib import pyplot as plt


def sigm(x):
	return 1 / (1 + np.exp(-x))

def is_sky(img):
        syn0 = np.array([[0.6106635051820115, -1.2018987127529588, -10.344605820189082, 1.1911213385074928, -6.818421664371254, 0.7888012143578024, 0.1930026599192343, 2.3468732267729644, -0.8629627172245428, -4.855127665505846, -8.782456796605247, -6.495787542595586, -1.42453153150294, -0.91145196348796, -0.34523737705411006], [-1.3963274415314406, -1.4612339780784143, -2.9000212540397685, -3.9905541370795463, -3.4490261869089287, -4.30542395055999, -2.6069427860345145, 7.201038210239841, -2.205826668689026, -2.493364425571145, -1.9813891706545306, -2.235792731073901, -7.475941696773453, -2.68683663270719, 4.173252030927632], [-0.5585916670209942, 0.3126863684210608, 2.142283443670229, 0.6422582372446218, 0.8699959804142926, 1.2677877625877656, 0.697665181045127, -4.116900256696914, 0.8735456225659666, -0.842712533453469, 1.1200739327640843, -0.703797233889045, 3.3491098693459187, 1.1383933429060538, -1.1608021413621255], [-0.0272945986039962, 1.3810803094898392, -0.3000751044667501, 0.530598483693932, -0.25230337237162953, 1.227322205409595, 0.7475404385595492, -4.708759516668004, 1.5170799948290143, -1.309427991379729, 0.13045771401578515, -1.2421270434590852, 5.141812566546993, 1.7478932634716013, -1.230678486397662], [-1.5471106279095554, -2.524731157065115, 1.0015792402542971, -3.649008251507766, -0.43193380458921354, -3.64779032623984, -1.2585955585366164, 7.075627752142407, -2.3434697661076553, -0.17324616725164094, 0.012324380796953634, 0.1201495802730507, -6.468182569926108, -1.0450745719122267, 3.1541002784637886], [0.5316498085997584, 1.8187154828158774, 0.6800840386512677, 3.154341773471645, -0.633596948312113, 2.770528037922082, 0.22043514814321089, -7.246507554283216, 1.3361606503168058, -1.8011391721619912, -0.7156002807301286, -0.37783520885870486, 6.373115811402003, 0.22971478266471973, -2.857966397739584]])
        syn1 = np.array([[5.177044095570317], 
[6.5898220063556], 
[-20.881638524287233], 
[8.880383432994854], 
[-14.676726398416983], 
[9.192745916291782], 
[5.80497325212264], 
[-16.424434027307676], 
[6.820380663953862], 
[-9.664844259044122], 
[-17.73177812938899], 
[-11.809681114121691], 
[14.747050641950713], 
[6.009983025197835], 
[-9.571035518824162]])
        mask = np.zeros(img.shape[:2], np.uint8)
        mask[0:(img.shape[0] / 2), 0:img.shape[1]] = 255
        masked_img = cv2.bitwise_and(img, img, mask = mask)

        # Create histograms with 16 bins in range 0-255
        color = ('b', 'g', 'r')
        b, g, r = cv2.split(img)
        dimy, dimx = img.shape[:2]

        largest = [0, 0]
        it = dimy / 200 #iterations = total number of rows(pixels) / 200
        for i in range(dimy / 6, (dimy / 6) * 5, it):   #only looking at the middle half of the image
            ravg = (sum(r[i]) / float(len(r[i])))
            gavg = (sum(g[i]) / float(len(g[i])))
            bavg = (sum(b[i]) / float(len(b[i])))
            avg = (ravg + gavg + bavg) / 3
            pravg = (sum(r[i - it]) / float(len(r[i - it])))
            pgavg = (sum(g[i - it]) / float(len(g[i - it])))
            pbavg = (sum(b[i - it]) / float(len(b[i - it])))
            pavg = (pravg + pgavg + pbavg) / 3
            diff = pavg - avg
            if diff > largest[0]:   #only getting the largest intensity drop.
                largest = [diff,i-(it/2)]
        sky = img[0:largest[1], 0:dimx]#cropping out landscape
        h1 = sky[0:(sky.shape[0] / 2), 0:dimx]#top half of sky
        h2 = sky[(sky.shape[0] / 2):(sky.shape[0]), 0:dimx]#bottom half
        mask1 = np.zeros(h1.shape[:2], np.uint8)
        mask1[0:(h1.shape[0] / 2), 0:h1.shape[1]] = 255
        hist1 = [0,0,0]
        hist2 = [0,0,0]
        max1 = [0,0,0]
        max2 = [0,0,0]
        for i,col in enumerate(color):
            hist1[i] = cv2.calcHist([h1], [i], mask1, [255], [0, 255])
            max1[i] = np.argmax(hist1[i][6:250])
    
        mask2 = np.zeros(h2.shape[:2], np.uint8)
        mask2[0:(h2.shape[0] / 2), 0:h2.shape[1]] = 255
        for j,col in enumerate(color):
            hist2[j] = cv2.calcHist([h2], [j], mask2, [255], [0, 255])
            max2[j] = np.argmax(hist2[j][6:250])
        X = np.array([float(max1[0])/255., float(max1[1])/255., float(max1[2])/255., float(max2[0])/255., float(max2[1])/255., float(max2[2])/255.])
        l1dup = sigm(np.dot(X,syn0))
        l2dup = sigm(np.dot(l1dup,syn1))
        if float(l2dup) >= 0.5:
            return True
        return False