Spaces:
Build error
Build error
File size: 3,157 Bytes
e6770c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
import math
import cv2
import numpy as np
from matplotlib import pyplot as plt
from scipy import ndimage
from skimage import measure, color, io
from tensorflow.keras.preprocessing import image
from scipy import ndimage
#Function that predicts on only 1 sample
def predict_sample(image):
prediction = model.predict(image[tf.newaxis, ...])
prediction[prediction > 0.5 ] = 1
prediction[prediction !=1] = 0
result = prediction[0]*255
return result
def create_input_image(data, visualize=False):
#Initialize input matrix
input = np.ones((256,256))
#Fill matrix with data point values
for i in range(0,len(data)):
if math.floor(data[i][0]) < 256 and math.floor(data[i][1]) < 256:
input[math.floor(data[i][0])][math.floor(data[i][1])] = 0
elif math.floor(data[i][0]) >= 256:
input[255][math.floor(data[i][1])] = 0
elif math.floor(data[i][1]) >= 256:
input[math.floor(data[i][0])][255] = 0
#Visualize
if visualize == True:
plt.imshow(input.T, cmap='gray')
plt.gca().invert_yaxis()
return input
def get_instances(prediction, data, max_filter_size=1):
#Adjust format (clusters to be 255 and rest is 0)
prediction[prediction == 255] = 3
prediction[prediction == 0] = 4
prediction[prediction == 3] = 0
prediction[prediction == 4] = 255
#Convert to 8-bit image
prediction = image.img_to_array(prediction, dtype='uint8')
#Get 1 color channel
cells=prediction[:,:,0]
#Threshold
ret1, thresh = cv2.threshold(cells, 0, 255, cv2.THRESH_BINARY)
#Filter to remove noise
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
#Get the background
background = cv2.dilate(opening,kernel,iterations=5)
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret2, foreground = cv2.threshold(dist_transform,0.04*dist_transform.max(),255,0)
foreground = np.uint8(foreground)
unknown = cv2.subtract(background,foreground)
#Connected Component Analysis
ret3, markers = cv2.connectedComponents(foreground)
markers = markers+10
markers[unknown==255] = 0
#Watershed
img = cv2.merge((prediction,prediction,prediction))
markers = cv2.watershed(img,markers)
img[markers == -1] = [0,255,255]
#Maximum filtering
markers = ndimage.maximum_filter(markers, size=max_filter_size)
# plt.imshow(markers.T, cmap='gray')
# plt.gca().invert_yaxis()
#Get an RGB colored image
img2 = color.label2rgb(markers, bg_label=1)
# plt.imshow(img2)
# plt.gca().invert_yaxis()
#Get regions
regions = measure.regionprops(markers, intensity_image=cells)
#Get Cluster IDs
cluster_ids = np.zeros(len(data))
for i in range(0,len(cluster_ids)):
row = math.floor(data[i][0])
column = math.floor(data[i][1])
if row < 256 and column < 256:
cluster_ids[i] = markers[row][column] - 10
elif row >= 256:
# cluster_ids[i] = markers[255][column]
cluster_ids[i] = 0
elif column >= 256:
# cluster_ids[i] = markers[row][255]
cluster_ids[i] = 0
cluster_ids = cluster_ids.astype('int8')
cluster_ids[cluster_ids == -11] = 0
return cluster_ids |