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import cv2
import numpy as np
import streamlit as st
# Refer to the application notebook implement the following filters
def bw_filter(img):
# Write your code here to convert img to a gray image
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img_gray
def vignette(img, level=2):
# Write your code here to create the vignette effect
height, width = img.shape[:2]
# Generate vignette mask using Gaussian kernels.
X_resultant_kernel = cv2.getGaussianKernel(width, width/level)
Y_resultant_kernel = cv2.getGaussianKernel(height, height/level)
# Generating resultant_kernel matrix.
# H x 1 * 1 x W
kernel = Y_resultant_kernel * X_resultant_kernel.T
mask = kernel / kernel.max()
img_vignette = np.copy(img)
# Applying the mask to each channel in the input image.
for i in range(3):
img_vignette[:,:,i] = img_vignette[:,:,i] * mask
return img_vignette
def sepia(img):
# Write your code here to create the sepia effect
img_sepia = img.copy()
# Converting to RGB as sepia matrix below is for RGB.
img_sepia = cv2.cvtColor(img_sepia, cv2.COLOR_BGR2RGB)
img_sepia = np.array(img_sepia, dtype = np.float64)
img_sepia = cv2.transform(img_sepia, np.matrix([[0.393, 0.769, 0.189],
[0.349, 0.686, 0.168],
[0.272, 0.534, 0.131]]))
# Clip values to the range [0, 255].
img_sepia = np.clip(img_sepia, 0, 255)
img_sepia = np.array(img_sepia, dtype = np.uint8)
img_sepia = cv2.cvtColor(img_sepia, cv2.COLOR_RGB2BGR)
return img_sepia
def pencil_sketch(img, ksize=5):
# Write your code here to create the pencil sketch effect
img_blur = cv2.GaussianBlur(img, (ksize, ksize), 0, 0)
img_sketch, _ = cv2.pencilSketch(img_blur)
return img_sketch
# Don't be constrained, add your own filters here |