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Pipeline_TemplateMatching_looping.py
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Pipeline_TemplateMatching_looping.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 22 21:22:46 2021
@author: genevieve.hayes
Template Matching Pipeline
"""
# In[0]: IMPORT
# import the necessary packages
from __future__ import print_function
from PIL import Image
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy import ndimage
import argparse
import imutils
import cv2
import numpy as np
import random as rng
import MTM
import pandas as pd
from MTM import matchTemplates, drawBoxesOnRGB
# In[00]: FUNCTIONS
def load_BGR_img(filepath):
#Loads BGR image at filepath (str type).
rng.seed(12345)
parser = argparse.ArgumentParser(description='Code for Image Segmentation with Distance Transform and Watershed Algorithm.\
Sample code showing how to segment overlapping objects using Laplacian filtering, \
in addition to Watershed and Distance Transformation')
parser.add_argument('--input', help='Path to input image.', default=filepath)
args = parser.parse_args()
BGR_img = cv2.imread(cv2.samples.findFile(args.input))
if BGR_img is None:
print('Could not open or find the image:', args.input)
exit(0)
return BGR_img
def create_red_mask(img):
#Convert image to HSV
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
hsv_color_min = np.array([160, 100, 100],np.uint8) #identifies red dye well
hsv_color_max = np.array([179, 255, 255],np.uint8)
#Define threshold color range to filter
mask_red = cv2.inRange(hsv_img, hsv_color_min, hsv_color_max)
#Get inverted mask (background)
mask_nored = cv2.bitwise_not(mask_red)
return mask_red, mask_nored
def create_light_red_mask(img):
#Convert image to HSV
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
#hsv_color_min = np.array([-1,70,20]) #identifies brown cells well in red region
#hsv_color_max = np.array([30,255,255])
hsv_color_min = np.array([100, 50, 100],np.uint8) #identifies red dye well
hsv_color_max = np.array([179, 255, 255],np.uint8)
#Define threshold color range to filter
mask_red = cv2.inRange(hsv_img, hsv_color_min, hsv_color_max)
#Get inverted mask (background)
mask_nored = cv2.bitwise_not(mask_red)
return mask_red, mask_nored
def removeRedRegionFromImage(img, mask_red):
#Convert image to HSV
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
#Bitwise-AND mask and original image
channel_red_hsv_img = cv2.bitwise_and(hsv_img, hsv_img, mask=mask_red)
ratio = cv2.countNonZero(mask_red)/(hsv_img.size/3)
percentage_redstain = np.round(ratio*100, 2)
#Get second masked value (background) mask must be inverted
mask_nored = cv2.bitwise_not(mask_red)
channel_nored_hsv_img = cv2.bitwise_and(hsv_img, hsv_img, mask=mask_nored)
channel_nored_img = cv2.cvtColor(channel_nored_hsv_img, cv2.COLOR_HSV2BGR)
#Set masked region to white
whiteimg = np.full(channel_nored_img.shape, 255, dtype=np.uint8) #make white image
img_redremoved = np.where(channel_nored_img[:,:] == [0,0,0], whiteimg, channel_nored_img) #here we replace black pixels.
return img_redremoved
def fill_mask_holes(mask, kernel):
''' Fill holes in the mask with a defined kernel.
Kernel can be rectangular (kernel = np.ones((10,10),np.uint8)),
cross (kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(20,25))),
or elliptical (kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(20,25))).
'''
closed_mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
return closed_mask
#WATERSHED PREPROCESSING FUNCTION
def preprocessing_for_watershed(image_nored):
# Invert input image
image = 255-image_nored
#Apply pyramid mean shift filtering
spatial_window_radius = 19
color_window_radius = 20
shifted = cv2.pyrMeanShiftFiltering(image, sp = spatial_window_radius, sr = color_window_radius)
# Convert the mean shift image to grayscale, then apply Otsu thresholding
gray = cv2.cvtColor(shifted, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# Compute the exact Euclidean distance from every binary pixel to the nearest zero pixel, then find peaks in this distance map
minimum_distance_map_distance = 11 #3 #10
# Perform the distance transform algorithm
dist = cv2.distanceTransform(thresh, distanceType=cv2.DIST_L2, maskSize=3)
# Normalize the distance image for range = {0.0, 1.0} so we can visualize and threshold it
cv2.normalize(dist, dist, 0, 1.0, cv2.NORM_MINMAX)
localMax = peak_local_max(dist, indices=False, min_distance=minimum_distance_map_distance, labels=thresh)
# perform a connected component analysis on the local peaks, using 8-connectivity, then apply the Watershed algorithm
markers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]
markersimg = np.array(markers*1000000, dtype="uint8")
return markers, thresh, dist, gray, markersimg
def BGRinBrownRange_whiteRegion(mean_colour_in_contourHSV):
hsv_color_min = np.array([-1,70,20]) #identifies brown cells well
hsv_color_max = np.array([30,255,255])
inHRange = (mean_colour_in_contourHSV[0]>hsv_color_min[0])*(mean_colour_in_contourHSV[0]<hsv_color_max[0])
inSRange = (mean_colour_in_contourHSV[1]>hsv_color_min[1])*(mean_colour_in_contourHSV[1]<hsv_color_max[1])
inVRange = (mean_colour_in_contourHSV[2]>hsv_color_min[2])*(mean_colour_in_contourHSV[2]<hsv_color_max[2])
inBrownRange = inHRange*inSRange*inVRange
return inBrownRange
def BGRinBlueRange_whiteRegion(mean_colour_in_contourHSV):
hsv_color_min = np.array([38, 0, 18],np.uint8) #identifies blue cells well
hsv_color_max = np.array([160, 255, 255],np.uint8)
inHRange = (mean_colour_in_contourHSV[0]>hsv_color_min[0])*(mean_colour_in_contourHSV[0]<hsv_color_max[0])
inSRange = (mean_colour_in_contourHSV[1]>hsv_color_min[1])*(mean_colour_in_contourHSV[1]<hsv_color_max[1])
inVRange = (mean_colour_in_contourHSV[2]>hsv_color_min[2])*(mean_colour_in_contourHSV[2]<hsv_color_max[2])
inBlueRange = inHRange*inSRange*inVRange
return inBlueRange
def BGRinBrownRange_redRegion(mean_colour_in_contourHSV):
hsv_color_min = np.array([-1,70,20]) #identifies brown cells well in red region
hsv_color_max = np.array([30,255,255])
inHRange = (mean_colour_in_contourHSV[0]>hsv_color_min[0])*(mean_colour_in_contourHSV[0]<hsv_color_max[0])
inSRange = (mean_colour_in_contourHSV[1]>hsv_color_min[1])*(mean_colour_in_contourHSV[1]<hsv_color_max[1])
inVRange = (mean_colour_in_contourHSV[2]>hsv_color_min[2])*(mean_colour_in_contourHSV[2]<hsv_color_max[2])
inBrownRange = inHRange*inSRange*inVRange
return inBrownRange
def BGRinBlueRange_redRegion(mean_colour_in_contourHSV):
hsv_color_min = np.array([38, 0, 18],np.uint8) #identifies blue cells well
hsv_color_max = np.array([160, 255, 255],np.uint8)
inHRange = (mean_colour_in_contourHSV[0]>hsv_color_min[0])*(mean_colour_in_contourHSV[0]<hsv_color_max[0])
inSRange = (mean_colour_in_contourHSV[1]>hsv_color_min[1])*(mean_colour_in_contourHSV[1]<hsv_color_max[1])
inVRange = (mean_colour_in_contourHSV[2]>hsv_color_min[2])*(mean_colour_in_contourHSV[2]<hsv_color_max[2])
inBlueRange = inHRange*inSRange*inVRange
return inBlueRange
# In[1]: LOAD AND CROP IMAGE AND FEATURES
SAVEIMG = 0
#filepath = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Sample 6B/Bladder 1 TMA - QATA3_sample6B.tiff"
filepath = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Sample6B.tiff"
#filepath = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Sample6G.tiff"
#filepath = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Sample6D.tiff"
filepath_featurekngp = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5neg_gata3pos.tiff"
filepath_featurekngp_2 = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5neg_gata3pos_2.tiff"
filepath_featurekngp_3 = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5neg_gata3pos_7.tiff"
filepath_featurekngp_4 = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5neg_gata3pos_4.tiff"
filepath_featurekngp_5 = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5neg_gata3pos_5.tiff"
filepath_featurekpgp = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5pos_gata3pos.tiff"
filepath_featurekpgp_2 = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5pos_gata3pos_2.tiff"
filepath_featurekpgp_3 = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5pos_gata3pos_3.tiff"
filepath_featurekpgp_4 = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5pos_gata3pos_4.tiff"
filepath_featurekpgp_5 = "/Users/genevieve.hayes/Desktop/ENPH 455 Thesis/Features/krt5pos_gata3pos_5.tiff"
BGR_fullimg = load_BGR_img(filepath)
BGR_featureimg_kngp = load_BGR_img(filepath_featurekngp)
BGR_featureimg_kngp_2 = load_BGR_img(filepath_featurekngp_2)
BGR_featureimg_kngp_3 = load_BGR_img(filepath_featurekngp_3)
BGR_featureimg_kngp_4 = load_BGR_img(filepath_featurekngp_4)
BGR_featureimg_kngp_5 = load_BGR_img(filepath_featurekngp_5)
BGR_featureimg_kpgp = load_BGR_img(filepath_featurekpgp)
BGR_featureimg_kpgp_2 = load_BGR_img(filepath_featurekpgp_2)
BGR_featureimg_kpgp_3 = load_BGR_img(filepath_featurekpgp_3)
BGR_featureimg_kpgp_4 = load_BGR_img(filepath_featurekpgp_4)
BGR_featureimg_kpgp_5 = load_BGR_img(filepath_featurekpgp_5)
cv2.imshow('Full image', BGR_fullimg)
cv2.waitKey(1)
Nx, Ny, w = np.shape(BGR_fullimg)
x_iterations = np.floor(Nx/500)
y_iterations = np.floor(Ny/500)
TOTAL_kngn = np.array([])
TOTAL_kngp = np.array([])
TOTAL_kpgn = np.array([])
TOTAL_kpgp = np.array([])
#In[2]: CROP IMAGE
for ind_x in range(0, int(x_iterations)):
for ind_y in range(0, int(y_iterations)):
BGR_cropimg = BGR_fullimg[ind_x*500:(ind_x*500+500),ind_y*500:(ind_y*500+500)]
BGR_cropimg_duplicate = BGR_cropimg
cv2.imshow('Original Image', BGR_cropimg)
cv2.waitKey(1)
print("------------------------------------------------- ")
print("\nImage Coordinates: ",ind_x*500,ind_y*500)
#In[3]: IDENTIFY RED REGION AND FILL IN HOLES
mask_red, mask_nored = create_red_mask(BGR_cropimg)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(20,25))
mask_closedred = fill_mask_holes(mask_red, kernel)
#In[4]: REMOVE FILLED RED REGION FROM IMAGE
image_nored = removeRedRegionFromImage(BGR_cropimg, mask_closedred)
cv2.imshow('No red white region',image_nored)
cv2.waitKey(1)
#In[4]: APPLY TEMPLATE MATCHING WITHOUT RED REGION
listTemplate_inwhite = [('A', BGR_featureimg_kngp),('B', BGR_featureimg_kngp_2),('G', BGR_featureimg_kngp_3),('D', BGR_featureimg_kngp_4)]#,('E', BGR_featureimg_kngp_5)]
Hits = matchTemplates(listTemplate_inwhite, image_nored, score_threshold=0.55, method=cv2.TM_CCOEFF_NORMED, maxOverlap=0)
Overlay_inwhite = drawBoxesOnRGB(image_nored, Hits, showLabel=True)
total_inwhite = len(Hits)
print('\n KRT5 -\n')
print('Number of segments identified:', total_inwhite)
cv2.imshow('KRT5- Region', Overlay_inwhite)
cv2.waitKey(1)
#In[6]: INVERT MASK TO GET ONLY RED REGION
mask_closed_nored = cv2.bitwise_not(mask_closedred)
image_justred = removeRedRegionFromImage(BGR_cropimg, mask_closed_nored)
cv2.imshow('Red region', image_justred)
cv2.waitKey(1)
#In[6]: APPLY TEMPLATE MATCHING IN RED REGION
listTemplate_inred = [('A+', BGR_featureimg_kpgp),('B+', BGR_featureimg_kpgp_2),('C+', BGR_featureimg_kpgp_3),('D+', BGR_featureimg_kpgp_4),('F+', BGR_featureimg_kpgp_5)]
Hits_inred = matchTemplates(listTemplate_inred, image_justred, score_threshold=0.4, method=cv2.TM_CCOEFF_NORMED, maxOverlap=0)
Overlay_inred = drawBoxesOnRGB(image_justred, Hits_inred, showLabel=True)
total_inred = len(Hits_inred)
print('\n KRT5 +\n')
print('Number of segments identified:', total_inred)
cv2.imshow('KRT5+ Region', Overlay_inred)
cv2.waitKey(1)
Total_overlay = drawBoxesOnRGB(BGR_cropimg, pd.concat([Hits,Hits_inred]), showLabel=True)
cv2.imshow('KRT5+ Region', Total_overlay)
cv2.waitKey(1)
#TOTAL_kngn = np.append(TOTAL_kngn, blue)
TOTAL_kngp = np.append(TOTAL_kngp, total_inwhite)
TOTAL_kpgn = np.append(TOTAL_kpgn, total_inred)
#TOTAL_kpgp = np.append(TOTAL_kpgp, blue_inred)
if SAVEIMG == 1:
filename_output = "Outputs/"+str(ind_x)+str(ind_y)+ "processed_img_section.tiff"
cv2.imwrite(filename_output, Total_overlay)
print('- Image Saved -')
print('\nIMAGE PROCESSING COMPLETE')
print('---- ---- ---- ----')
print('\nTOTAL KRT5-/GATA3+:', np.sum(TOTAL_kngp))
print('TOTAL KRT5+/GATA3+:', np.sum(TOTAL_kpgp))
print('TOTAL KRT5+/GATA3-:', np.sum(TOTAL_kpgn))
print('TOTAL KRT5-/GATA3-:', np.sum(TOTAL_kngn))