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import os
import numpy as np
import cv2
import pandas as pd
from glob import glob
from tqdm import tqdm
import tensorflow as tf
from tensorflow.keras.utils import CustomObjectScope
from sklearn.metrics import f1_score, jaccard_score, precision_score, recall_score
from sklearn.model_selection import train_test_split
from metrics import dice_loss, dice_coef
from train import load_dataset
from unet import build_unet
""" Global parameters """
H = 256
W = 256
""" Creating a directory """
def create_dir(path):
if not os.path.exists(path):
os.makedirs(path)
def save_results(image, mask, y_pred, save_image_path):
mask = np.expand_dims(mask, axis=-1)
mask = np.concatenate([mask, mask, mask], axis=-1)
y_pred = np.expand_dims(y_pred, axis=-1)
y_pred = np.concatenate([y_pred, y_pred, y_pred], axis=-1)
y_pred = y_pred * 255
line = np.ones((H, 10, 3)) * 255
cat_images = np.concatenate([image, line, mask, line, y_pred], axis=1)
cv2.imwrite(save_image_path, cat_images)
if __name__ == "__main__":
""" Seeding """
np.random.seed(42)
tf.random.set_seed(42)
""" Directory for storing files """
create_dir("results")
""" Load the model """
with CustomObjectScope({"dice_coef": dice_coef, "dice_loss": dice_loss}):
model = tf.keras.models.load_model(os.path.join("files", "model.h5"))
""" Dataset """
dataset_path = "/media/nikhil/Seagate Backup Plus Drive/ML_DATASET/brain_tumor_dataset/data"
(train_x, train_y), (valid_x, valid_y), (test_x, test_y) = load_dataset(dataset_path)
""" Prediction and Evaluation """
SCORE = []
for x, y in tqdm(zip(test_x, test_y), total=len(test_y)):
""" Extracting the name """
name = x.split("/")[-1]
""" Reading the image """
image = cv2.imread(x, cv2.IMREAD_COLOR) ## [H, w, 3]
image = cv2.resize(image, (W, H)) ## [H, w, 3]
x = image/255.0 ## [H, w, 3]
x = np.expand_dims(x, axis=0) ## [1, H, w, 3]
""" Reading the mask """
mask = cv2.imread(y, cv2.IMREAD_GRAYSCALE)
mask = cv2.resize(mask, (W, H))
""" Prediction """
y_pred = model.predict(x, verbose=0)[0]
y_pred = np.squeeze(y_pred, axis=-1)
y_pred = y_pred >= 0.5
y_pred = y_pred.astype(np.int32)
""" Saving the prediction """
save_image_path = os.path.join("results", name)
save_results(image, mask, y_pred, save_image_path)
""" Flatten the array """
mask = mask/255.0
mask = (mask > 0.5).astype(np.int32).flatten()
y_pred = y_pred.flatten()
""" Calculating the metrics values """
f1_value = f1_score(mask, y_pred, labels=[0, 1], average="binary")
jac_value = jaccard_score(mask, y_pred, labels=[0, 1], average="binary")
recall_value = recall_score(mask, y_pred, labels=[0, 1], average="binary", zero_division=0)
precision_value = precision_score(mask, y_pred, labels=[0, 1], average="binary", zero_division=0)
SCORE.append([name, f1_value, jac_value, recall_value, precision_value])
""" Metrics values """
score = [s[1:]for s in SCORE]
score = np.mean(score, axis=0)
print(f"F1: {score[0]:0.5f}")
print(f"Jaccard: {score[1]:0.5f}")
print(f"Recall: {score[2]:0.5f}")
print(f"Precision: {score[3]:0.5f}")
df = pd.DataFrame(SCORE, columns=["Image", "F1", "Jaccard", "Recall", "Precision"])
df.to_csv("files/score.csv") |