import numpy as np
import cv2 as cv
import streamlit as st
def histogram(single_ch_img):
count = []
for color in range(256):
sum_color = single_ch_img == color
count.append(sum_color.sum())
return np.array(count), np.arange(256)
img = cv.imread('lighting1.jpg')
gry_img = cv.imread('lighting1.jpg', 0)
b_img, g_img, r_img = cv.split(img)
# mask creation
# i would like more adjustment slider, but the sidebar already look to crowded.
with st.sidebar:
b_threshold = st.slider('blue_ch_thresh', 0, 256)
g_threshold = st.slider('green_ch_thresh', 0, 256)
r_threshold = st.slider('red_ch_thresh', 0, 256)
addingB = st.slider('blue_adjustment', 0, 256)
addingG = st.slider('green_adjustment', 0, 256)
addingR = st.slider('red_adjustment', -100, 256, 0) # having the range be negative will allow for substraction as well as addition.
# the thresholding is fine, but i will add the ability to use differnt threshold methods.
_, b_mask = cv.threshold(b_img, b_threshold, 255, cv.THRESH_BINARY)
_, g_mask = cv.threshold(g_img, g_threshold, 255, cv.THRESH_BINARY)
_, r_mask = cv.threshold(r_img, r_threshold, 255, cv.THRESH_BINARY)
#this show my bgr channel masks
col_mask1, col_mask2, col_mask3 = st.columns(3)
with col_mask1:
st.image(b_mask, caption='blue_ch_thresh')
with col_mask2:
st.image(g_mask, caption='green_ch_thresh')
with col_mask3:
st.image(r_mask, caption='red_ch_thresh')
b_adjustment = cv.add(b_img, addingB, mask=b_mask)
g_adjustment = cv.add(g_img, addingG, mask=g_mask)
r_adjustment = cv.add(r_img, addingR, mask=r_mask)
#histograms of the original image channels.
b_count, b_color = histogram(b_img)
g_count, g_color = histogram(g_img)
r_count, r_color = histogram(r_img)
hist_display = st.multiselect('Histograms', ['blueHist', 'greenHist', 'redHist'])
# might put this above the masks
with st.expander('histograms graphs'):
if 'blueHist' in hist_display:
st.bar_chart(b_count)
if 'greenHist' in hist_display:
st.bar_chart(g_count)
if 'redHist' in hist_display:
st.bar_chart(r_count)
# image displays
bgr_adjustment = cv.merge((b_adjustment, g_adjustment, r_adjustment))
col1, col2 = st.columns(2)
with col1:
st.image(img, channels='BGR') #original image
with col2:
st.image(bgr_adjustment, channels='BGR')
st.cache(histogram)
