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TissueClustersFromThumbnails.py
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TissueClustersFromThumbnails.py
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## creating direct clusters of tissues using pretrained models
import glob
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from PIL import Image
from sklearn.cluster import KMeans
from tensorflow.keras.applications import VGG19
from tensorflow.keras.preprocessing import image as img_prep
## Select GPU
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
files = glob.glob("/Path/To/Thumbnails/*.png")
for f in files:
ff, _ = os.path.splitext(f)
basename = os.path.basename(ff)
print(basename)
# Load the image
image_path = f
original_image = Image.open(image_path)
original_image = np.array(original_image)
original_image = cv2.resize(
original_image,
(int(original_image.shape[1] / 2), int(original_image.shape[0] / 2)),
)
# Define the size of the small squares
square_size = 32
# Calculate the number of small squares along width and height
width, height, _ = original_image.shape
num_squares_x = width // square_size
num_squares_y = height // square_size
# Load pre-trained model + higher level layers
model = VGG19(weights="imagenet", include_top=False)
# Initialize an array to store feature vectors
feature_vectors = []
# Divide the original image into small squares and extract feature vectors
for i in range(num_squares_x):
for j in range(num_squares_y):
# Extract small square from original image
square = original_image[
i * square_size : (i + 1) * square_size,
j * square_size : (j + 1) * square_size,
]
# Preprocess the square
square = img_prep.img_to_array(square)
square = np.expand_dims(square, axis=0)
square = tf.keras.applications.mobilenet.preprocess_input(square)
# Extract feature vector using MobileNet
feature_vector = model.predict(square, verbose=-1)
feature_vectors.append(feature_vector.flatten())
feature_vectors = np.array(feature_vectors)
# Apply k-means clustering on the feature vectors
num_clusters = 7 # Define the number of clusters
kmeans = KMeans(n_clusters=num_clusters, random_state=0).fit(feature_vectors)
labels = kmeans.labels_
# Create an image with clustering result
clustered_image = np.zeros_like(original_image)
for i in range(num_squares_x):
for j in range(num_squares_y):
label = labels[i * num_squares_y + j]
color = np.array(plt.cm.rainbow(label / num_clusters)[:3]) * 255
clustered_image[
i * square_size : (i + 1) * square_size,
j * square_size : (j + 1) * square_size,
] = color
cv2.imwrite("/Path/To/Outputs/" + basename + ".png", clustered_image)