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import cv2
import numpy as np
import os
import matplotlib.pyplot as plt
import io
from PIL import Image
import numpy as np
def process_bgr_image(img, lb, ub, channel):
# Define the lower and upperbounds based on the image channel.
if channel == 'Blue':
lb = np.array([lb, 0, 0], np.uint8)
ub = np.array([ub, 255, 255], np.uint8)
elif channel == 'Green':
lb = np.array([0, lb, 0], np.uint8)
ub = np.array([255, ub, 255], np.uint8)
elif channel == 'Red':
lb = np.array([0, 0, lb], np.uint8)
ub = np.array([255, 255, ub], np.uint8)
# Calculate the histograms for the BGR color space and the Hue channel in the HSV image.
hist_b, hist_g, hist_r = calculateHistogram(img, mode='BGR')
# Plot the histograms
hist_bgr = plotHistogram((hist_b, hist_g, hist_r), mode='BGR')
# Calculate the mask for the given bounds from hsv image
op = calculateMask(img, lb, ub, mode='BGR')
return hist_bgr, op
def process_hsv_image(img, lb, ub):
# Conver the image to the HSV color space
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# Define the lower and upperbounds for Hue
lb = np.array([lb, 0, 0], np.uint8)
ub = np.array([ub, 255, 255], np.uint8)
# Calculate the histograms for the BGR color space and the Hue channel in the HSV image.
hist_hue = calculateHistogram(img_hsv, mode='HSV')
# Plot the histogram.
hist_hsv = plotHistogram(hist_hue, mode='HSV')
# Calculate the mask for the given bounds from hsv image
op = calculateMask((img_hsv, img), lb, ub, mode='HSV')
return hist_hsv, op
def calculateHistogram(img, mode):
if mode == 'BGR':
hist_b = cv2.calcHist([img], [0], None, [256], [0,255])
hist_g = cv2.calcHist([img], [1], None, [256], [0,255])
hist_r = cv2.calcHist([img], [2], None, [256], [0,255])
return hist_b, hist_g, hist_r
elif mode == 'HSV':
hist_hue = cv2.calcHist([img], [0], None, [180], [0,180])
return hist_hue
def plotHistogram(hists, mode):
if mode == 'BGR':
hist_b, hist_g, hist_r = hists
plt.figure(figsize=(10,2))
plt.plot(hist_b, color='b', label='Blue')
plt.plot(hist_g, color='g', label='Green')
plt.plot(hist_r, color='r', label='Red')
plt.xlim([0, 255])
plt.legend()
buf_bgr= io.BytesIO()
plt.savefig(buf_bgr, format='png')
buf_bgr.seek(0)
hist_bgr = Image.open(buf_bgr)
plt.close()
return hist_bgr
if mode == 'HSV':
hist_hue = hists
plt.figure(figsize=(10,2))
plt.plot(hist_hue, color='black', label='Black')
plt.xlim([0, 180])
buf_hsv = io.BytesIO()
plt.savefig(buf_hsv, format='png')
buf_hsv.seek(0)
hist_hsv = Image.open(buf_hsv)
plt.close()
return hist_hsv
def calculateMask(imgs, lb, ub, mode):
op = None
if mode == 'BGR':
img = imgs
mask_1d = cv2.inRange(img, lb, ub)
mask_3d = cv2.merge([mask_1d, mask_1d, mask_1d])
op = cv2.bitwise_and(img, mask_3d)
if mode == 'HSV':
img_hsv, img = imgs
mask_1d = cv2.inRange(img_hsv, lb, ub)
mask_3d = cv2.merge([mask_1d, mask_1d, mask_1d])
op = cv2.bitwise_and(img, mask_3d)
return op |