#import numpy as np
import cv2

# read an image with shadow...
# and it converts to BGR color space automatically
or_img = cv2.imread('shadow4.jpg')

# covert the BGR image to an YCbCr image
y_cb_cr_img = cv2.cvtColor(or_img, cv2.COLOR_BGR2YCrCb)

# copy the image to create a binary mask later
binary_mask = np.copy(y_cb_cr_img)

# get mean value of the pixels in Y plane
y_mean = np.mean(cv2.split(y_cb_cr_img)[0])

# get standard deviation of channel in Y plane
y_std = np.std(cv2.split(y_cb_cr_img)[0])

# classify pixels as shadow and non-shadow pixels
for i in range(y_cb_cr_img.shape[0]):
    for j in range(y_cb_cr_img.shape[1]):

        if y_cb_cr_img[i, j, 0] < y_mean - (y_std / 3):
            # paint it white (shadow)
            binary_mask[i, j] = [255, 255, 255]
        else:
            # paint it black (non-shadow)
            binary_mask[i, j] = [0, 0, 0]

# Using morphological operation
# The misclassified pixels are
# removed using dilation followed by erosion.
kernel = np.ones((3, 3), np.uint8)
erosion = cv2.erode(binary_mask, kernel, iterations=1)

# sum of pixel intensities in the lit areas
spi_la = 0

# sum of pixel intensities in the shadow
spi_s = 0

# number of pixels in the lit areas
n_la = 0

# number of pixels in the shadow
n_s = 0

# get sum of pixel intensities in the lit areas
# and sum of pixel intensities in the shadow
for i in range(y_cb_cr_img.shape[0]):
    for j in range(y_cb_cr_img.shape[1]):
        if erosion[i, j, 0] == 0 and erosion[i, j, 1] == 0 and erosion[i, j, 2] == 0:
            spi_la = spi_la + y_cb_cr_img[i, j, 0]
            n_la += 1
        else:
            spi_s = spi_s + y_cb_cr_img[i, j, 0]
            n_s += 1

# get the average pixel intensities in the lit areas
average_ld = spi_la / n_la

# get the average pixel intensities in the shadow
average_le = spi_s / n_s

# difference of the pixel intensities in the shadow and lit areas
i_diff = average_ld - average_le

# get the ratio between average shadow pixels and average lit pixels
ratio_as_al = average_ld / average_le

# added these difference
for i in range(y_cb_cr_img.shape[0]):
    for j in range(y_cb_cr_img.shape[1]):
        if erosion[i, j, 0] == 255 and erosion[i, j, 1] == 255 and erosion[i, j, 2] == 255:

            y_cb_cr_img[i, j] = [y_cb_cr_img[i, j, 0] + i_diff, y_cb_cr_img[i, j, 1] + ratio_as_al,
                                 y_cb_cr_img[i, j, 2] + ratio_as_al]

# covert the YCbCr image to the BGR image
final_image = cv2.cvtColor(y_cb_cr_img, cv2.COLOR_YCR_CB2BGR)

cv2.imshow("im1", or_img)
cv2.imshow("im2", final_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

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About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset{"iPhone","Pixel","Samsung"}
print{myset}

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)