Update app.py

app.py

@@ -0,0 +1,110 @@
+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