Convert data set to CSV with custom resize

GetCovidToCSV.py

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+'''
+This code creates a CSV format of data set with resized images of your defined size (Height, Width)
+We got labels for every image from metadata.csv and in new creted dataset, first attribute defines the label
+label == 1 defines COVID-19 and Label == 0 defines all others
+Since Excel contains XFD as last column and XFD equals 16384. Thus we have to look for the resized height and width
+In this example we took 40 x 40 with 3 channels making total row size (40x40x3)+1 +1 is added because label is stored as first attribute of image
+
+'''
+import numpy as np
+import pandas as pd
+import csv
+from keras.preprocessing.image import ImageDataGenerator
+import matplotlib.pyplot as plt
+
+
+#Defining image size
+Height = 40
+Width = 40
+
+
+#defining dataset dir
+DATASET_DIR = "./dataset/Images/" #Define paths as per your environment
+metadata = "./dataset/metadata/metadata.csv" # Meta info - path to metadata file
+
+
+# Resizing Training data images to 40 x 40
+train_datagen = ImageDataGenerator()
+train_generator = train_datagen.flow_from_directory(
+    DATASET_DIR,
+    target_size=(Height, Width),
+    batch_size=1,
+    class_mode='binary',
+    subset='training')
+
+
+#Getting Labels
+metadata_csv = pd.read_csv(metadata)
+labels = []
+for file in train_generator.filenames:
+    for (x, row) in metadata_csv.iterrows():
+        file_name = 'images\\'+ row["filename"] #file contains the directory path 'images\' as well. Thus prefixing images\ to row['filename'] for comparison
+        if file_name == file:
+            label = row['finding']
+            if label == "COVID-19":
+                label = 1 #label == 1 defines COVID-19
+            else:
+                label = 0
+            break
+    labels.append(label)
+
+print('Total Images: ',len(labels))
+
+
+#Writting pixel values to CSV file
+with open('mydata.csv', 'w', newline='') as file:
+    writer = csv.writer(file)
+    for i in range(len(train_generator)):
+        image, label = train_generator[i]
+        image = image.reshape(Height*Width*3) # Reshaping into single list
+        label = labels[i]     
+        row = [label] #Writting label as first element of the row 
+        for i in range(len(image)): #Iterating over Height
+            row.append(image[i]) #Adding all pixels of an image in a single row after the label
+        writer.writerow(row)
+
+print("CSV data genereated sucessfully !");
+
+
+# Plot the first image from the input dataset to be sure that data is correctly transformed
+#============================================================
+# Take a single image, and remove the color dimension by reshaping
+imgSize = Height*Width*3 + 1 #We used first element as label
+image = row[1:imgSize] #Taking Image pixels from element 2
+image = np.array(image)
+image = image.reshape(Height,Width,3)
+
+
+#Plotting here
+plt.figure()
+plt.imshow(image.astype(int), cmap=plt.cm.binary)
+plt.colorbar()
+plt.grid(False)
+plt.title('First Image')
+plt.show()
+