Delete Notebook and Py File/inceptionv3_image_classification.py

Notebook and Py File/inceptionv3_image_classification.py

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-# -*- coding: utf-8 -*-
-"""InceptionV3_Image_Classification.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1qSA_Rg-2gDZ0lKcAuLivNsCgrBmJOcfz
-
-# **Import Dependencies**
-"""
-
-import warnings
-warnings.filterwarnings('ignore')
-
-import zipfile
-import hashlib
-import matplotlib.pyplot as plt
-import pandas as pd
-import os
-import uuid
-import re
-import random
-import cv2
-import numpy as np
-import tensorflow as tf
-import seaborn as sns
-from google.colab import drive
-from google.colab import files
-from pathlib import Path
-from PIL import Image, ImageStat, UnidentifiedImageError, ImageEnhance
-from matplotlib import patches
-from tqdm import tqdm
-from collections import defaultdict
-from sklearn.preprocessing import LabelEncoder, label_binarize
-from sklearn.model_selection import train_test_split
-from sklearn.utils import resample
-from tensorflow import keras
-from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
-from tensorflow.keras.utils import to_categorical
-from tensorflow.keras import layers, models, optimizers, callbacks, regularizers
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Conv2D, BatchNormalization, MaxPooling2D,Dropout, Flatten, Dense, GlobalAveragePooling2D
-from tensorflow.keras.regularizers import l2
-from tensorflow.keras.optimizers import Adam, AdamW
-from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
-from tensorflow.keras import Input, Model
-from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img, array_to_img
-from tensorflow.keras.preprocessing import image
-from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve, precision_score, recall_score, f1_score, precision_recall_fscore_support, auc
-
-print(tf.__version__)
-
-drive.mount('/content/drive')
-zip_path = '/content/drive/MyDrive/Animals.zip'
-extract_to = '/content/my_data'
-with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-    zip_ref.extractall(extract_to)
-
-"""# **Convert Dataset to a Data Frame**"""
-
-image_extensions = {'.jpg', '.jpeg', '.png'}
-paths = [(path.parts[-2], path.name, str(path)) for path in Path(extract_to).rglob('*.*') if path.suffix.lower() in image_extensions]
-
-df = pd.DataFrame(paths, columns = ['class', 'image', 'full_path'])
-df = df.sort_values('class', ascending = True)
-df.reset_index(drop = True, inplace = True)
-df
-
-"""# **EDA Process**"""
-
-class_count = df['class'].value_counts()
-for cls, count in class_count.items():
-    print(f'Class: {cls}, Count: {count} images')
-
-print(f"\nTotal dataset size is: {len(df)} images")
-print(f"Number of classes: {df['class'].nunique()} classes")
-
-plt.figure(figsize = (32, 16))
-class_count.plot(kind = 'bar', color = 'skyblue', edgecolor = 'black')
-plt.title('Number of Images per Class')
-plt.xlabel('Class')
-plt.ylabel('Count')
-plt.xticks(rotation = 45)
-plt.show()
-
-plt.figure(figsize = (32, 16))
-class_count.plot(kind = 'pie', autopct = '%1.1f%%', colors = plt.cm.Paired.colors)
-plt.title('Percentage of Images per Class')
-plt.ylabel('')
-plt.show()
-
-percentages = (class_count / len(df)) * 100
-imbalance_df = pd.DataFrame({'Count': class_count, 'Percentage %': percentages.round(2)})
-print(imbalance_df)
-
-plt.figure(figsize = (32, 16))
-class_count.plot(kind = 'bar', color = 'lightgreen', edgecolor = 'black')
-plt.title('Class Distribution Check')
-plt.xlabel('Class')
-plt.ylabel('Count')
-plt.xticks(rotation = 45)
-plt.axhline(y = class_count.mean(), color = 'red', linestyle = '--', label = 'Average Count')
-plt.legend()
-plt.show()
-
-image_sizes = []
-
-for file_path in df['full_path']:
-    with Image.open(file_path) as img:
-        image_sizes.append(img.size)
-
-sizes_df = pd.DataFrame(image_sizes, columns=['Width', 'Height'])
-
-#Width
-plt.figure(figsize=(8,5))
-plt.scatter(x = range(len(sizes_df)), y = sizes_df['Width'], color='skyblue', s=10)
-plt.title('Image Width Distribution')
-plt.xlabel('Width (pixels)')
-plt.ylabel('Frequency')
-plt.show()
-
-#Height
-plt.figure(figsize=(8,5))
-plt.scatter(x = sizes_df['Height'], y = range(len(sizes_df)), color='lightgreen', s=10)
-plt.title('Image Height Distribution')
-plt.xlabel('Height (pixels)')
-plt.ylabel('Frequency')
-plt.show()
-
-#For best sure the size of the whole images
-unique_sizes = sizes_df.value_counts().reset_index(name='Count')
-print(unique_sizes)
-
-image_data = []
-
-for file_path in df['full_path']:
-    with Image.open(file_path) as img:
-        width, height = img.size
-        mode = img.mode  # e.g., 'RGB', 'L', 'RGBA', etc.
-        channels = len(img.getbands())  # Number of channels
-        image_data.append((width, height, mode, channels))
-
-# Create DataFrame
-image_df = pd.DataFrame(image_data, columns=['Width', 'Height', 'Mode', 'Channels'])
-
-print("Image Mode Distribution:")
-print(image_df['Mode'].value_counts())
-
-print("\nNumber of Channels Distribution:")
-print(image_df['Channels'].value_counts())
-
-plt.figure(figsize=(6,4))
-image_df['Mode'].value_counts().plot(kind='bar', color='coral')
-plt.title("Image Mode Distribution")
-plt.xlabel("Mode")
-plt.ylabel("Count")
-plt.xticks(rotation=45)
-plt.tight_layout()
-plt.show()
-
-plt.figure(figsize=(6,4))
-image_df['Channels'].value_counts().sort_index().plot(kind='bar', color='slateblue')
-plt.title("Number of Channels per Image")
-plt.xlabel("Channels")
-plt.ylabel("Count")
-plt.xticks(rotation=0)
-plt.tight_layout()
-plt.show()
-
-sample_df = df.sample(n = 10, random_state = 42)
-
-plt.figure(figsize=(32, 16))
-
-for i, (cls, img_name, full_path) in enumerate(sample_df.values):
-    with Image.open(full_path) as img:
-      stat = ImageStat.Stat(img.convert("RGB")) #Convert images to RGB images
-      brightness = stat.mean[0]
-      contrast = stat.stddev[0]
-
-      width, height = img.size
-      # Print size to console
-      print(f"Image: {img_name} | Class: {cls} | Size: {width}x{height} | Brightness: {brightness:.1f} | Contrast: {contrast:.1f}")
-
-      plt.subplot(2, 5, i + 1)
-      plt.imshow(img)
-      plt.axis('off')
-      plt.title(f"Class: {cls}\nImage: {img_name}\nBrightness: {brightness:.2f}\nContrast: {contrast:.2f} \nSize: {width}x{height}")
-
-plt.tight_layout
-plt.show()
-
-# Sample 20 random images
-num_samples = 20
-sample_df = df.sample(num_samples, random_state=42)
-
-# Get sorted class list and color map
-classes = sorted(df['class'].unique())
-colors = plt.cm.tab10.colors
-
-# Grid setup
-cols = 4
-rows = num_samples // cols + int(num_samples % cols > 0)
-
-# Figure setup
-plt.figure(figsize=(15, 5 * rows))
-
-for idx, (cls, img_name, full_path) in enumerate(sample_df.values):
-    with Image.open(full_path) as img:
-        ax = plt.subplot(rows, cols, idx + 1)
-        ax.imshow(img)
-        ax.axis('off')
-
-        # Title with class info
-        ax.set_title(
-            f"Class: {cls} \nImage: {img_name} \nSize: {img.width} x {img.height}",
-            fontsize=10
-        )
-
-        # Rectangle in axes coords: full width, small height at top
-        label_height = 0.1  # 10% of image height
-        label_width = 1.0   # full width of the image
-
-        rect = patches.Rectangle(
-        (0, 1 - label_height), label_width, label_height,
-        transform=ax.transAxes,
-        linewidth=0,
-        edgecolor=None,
-        facecolor=colors[classes.index(cls) % len(colors)],
-        alpha=0.7
-      )
-        ax.add_patch(rect)
-
-        # Add class name text centered horizontally
-        ax.text(
-        0.5, 1 - label_height / 2,
-        cls,
-        transform=ax.transAxes,
-        fontsize=12,
-        color="white",
-        fontweight="bold",
-        va="center",
-        ha="center"
-      )
-
-# Figure title and layout
-plt.suptitle("Random Dataset Samples - Sanity Check", fontsize=18, fontweight="bold")
-plt.tight_layout(rect=[0, 0, 1, 0.96])
-plt.show()
-
-#Check missing files
-print("Missing values per column: ")
-print(df.isnull().sum())
-
-#Check duplicate files
-duplicate_names = df.duplicated().sum()
-print(f"\nNumber of duplicate files: {duplicate_names}")
-
-duplicate_names = df[df.duplicated(subset = ['image'], keep = False)]
-print(f"Duplicate file names: {len(duplicate_names)}")
-
-#Check if two images or more are the same even if they are having different file names
-def get_hash(file_path):
-    with open(file_path, 'rb') as f:
-        return hashlib.md5(f.read()).hexdigest()
-
-df['file_hash'] = df['full_path'].apply(get_hash)
-duplicate_hashes = df[df.duplicated(subset = ['file_hash'], keep = False)]
-print(f"Duplicate image files: {len(duplicate_hashes)}")
-
-#This code below just removing the duplicate files, which means will not be feeded to the model, but will be still in the actual directory
-#Important note: duplicates are removed from the dataframe only, not from the actual directory.
-#Drop duplicates based on file_hash, keeping the first one
-
-# df_unique = df.drop_duplicates(subset='file_hash', keep='first')
-# print(f"After removing duplicates, unique images: {len(df_unique)}")
-
-#Check for images extentions
-df['extenstion'] = df['image'].apply(lambda x: Path(x).suffix.lower())
-print("File type counts: ")
-#print(df['extenstion'].value_counts)
-print(df['extenstion'].value_counts())
-
-#Check for resolution relationships
-df['Width'] = sizes_df['Width']
-df['Height'] = sizes_df['Height']
-#print(df.groupby(['Width', 'Height']).size())
-print(df.groupby('class')[['Width', 'Height']].agg(['min', 'max', 'mean']))
-
-#Check for class balance (relationship between label and count)
-class_summary = df['class'].value_counts(normalize = False).to_frame('Count')
-#class_summary['Percentage'] = class_summary['Count'] / class_summary['Count'].sum() * 100
-#class_summary
-class_summary['Percentage %'] = round((class_summary['Count'] / len(df)) * 100, 2)
-print(class_summary)
-
-"""# **Data Cleaning Process**"""
-
-corrupted_files = []
-
-for file_path in df['full_path']:
-    try:
-        with Image.open(file_path) as img:
-            img.verify()
-    except (UnidentifiedImageError, OSError):
-        corrupted_files.append(file_path)
-
-print(f"Found {len(corrupted_files)} corrupted images.")
-    #except (IOError, SyntaxError) as e:
-        #corrupted_files.append(file_path)
-
-#print(f"Number of corrupted files: {len(corrupted_files)}")
-
-if corrupted_files:
-  df = df[~df['full_path'].isin(corrupted_files)].reset_index(drop = True)
-  print("Corrupted files removed.")
-
-#Outliers detection
-#Resolution-based outlier detection
-#width_mean = width_std = sizes_df['Width'].mean(), sizes_df['Width'].std()
-#height_mean = height_std = sizes_df['Height'].mean(), sizes_df['Height'].std()
-
-width_mean = sizes_df['Width'].mean()
-width_std = sizes_df['Width'].std()
-height_mean = sizes_df['Height'].mean()
-height_std = sizes_df['Height'].std()
-
-outliers = df[(df['Width'] > width_mean + 3 * width_std) | (df['Width'] < width_mean - 3 * width_std) | (df['Height'] > height_mean + 3 * height_std) | (df['Height'] < height_mean - 3 * height_std)]
-#print(f"Number of outliers: {len(outliers)}")
-print(f"Found {len(outliers)} resolution outliers.")
-
-df["image"] = df["full_path"].apply(lambda p: Image.open(p).convert('RGB')) #Convert it to RGB for flexibility
-
-too_dark = []
-too_bright = []
-blank_or_gray = []
-
-# Thresholds
-dark_threshold = 30    # Below this is too dark
-bright_threshold = 225 # Above this is too bright
-low_contrast_threshold = 5  # Low contrast ~ blank/gray
-
-for idx, img in enumerate(df["image"]):
-    gray = img.convert('L')
-    stat = ImageStat.Stat(gray) # Convert to grayscale for brightness/contrast analysis
-    brightness = stat.mean[0]
-    contrast = stat.stddev[0]
-
-    if brightness < dark_threshold:
-        too_dark.append(idx)
-    elif brightness > bright_threshold:
-        too_bright.append(idx)
-    elif contrast < low_contrast_threshold:
-        blank_or_gray.append(idx)
-
-print(f"Too dark images: {len(too_dark)}")
-print(f"Too bright images: {len(too_bright)}")
-print(f"Blank/gray images: {len(blank_or_gray)}")
-
-# df = df.drop(index=too_bright + blank_or_gray).reset_index(drop=True) --> DROPS too_bright + blank_or_gray TOGETHER!
-
-for idx, row in tqdm(df.iterrows(), total=len(df), desc="Enhancing images"):
-    img = row["image"]
-
-    # Enhance too dark images
-    if row["full_path"] in df.loc[too_dark, "full_path"].values:
-        img = ImageEnhance.Brightness(img).enhance(1.5)  # Increase brightness
-        img = ImageEnhance.Contrast(img).enhance(1.5)    # Increase contrast
-
-    # Decrease brightness for too bright images
-    if row["full_path"] in df.loc[too_bright, "full_path"].values:
-        img = ImageEnhance.Brightness(img).enhance(0.7)  # Decrease brightness (less than 1)
-        img = ImageEnhance.Contrast(img).enhance(1.2)    # Optionally, you can also enhance contrast
-
-    # Overwrite the image back into the DataFrame
-    df.at[idx, "image"] = img
-
-print(f"Enhanced images in memory: {len(df)}")
-
-# Lists to store paths of still too dark and too bright images
-still_dark = []
-still_bright = []
-
-# Threshold for "too bright" (already defined as bright_threshold)
-for idx, img in enumerate(df["image"]):
-    gray = img.convert('L')  # Convert to grayscale for brightness analysis
-    stat = ImageStat.Stat(gray)
-    brightness = stat.mean[0]
-
-    # Check if the image is still too dark
-    if brightness < dark_threshold:
-        still_dark.append(df.loc[idx, 'full_path'])
-
-    # Check if the image is too bright
-    if brightness > bright_threshold:
-        still_bright.append(df.loc[idx, 'full_path'])
-
-print(f"Still too dark after enhancement: {len(still_dark)} images")
-print(f"Still too bright after enhancement: {len(still_bright)} images")
-
-# Point to the extracted dataset, not the zip file location
-dataset_root = "/content/my_data/Animals"
-
-# Check mislabeled images
-mismatches = []
-for i, row in df.iterrows():
-    folder_name = os.path.basename(os.path.dirname(row["full_path"]))
-    if row["class"] != folder_name:
-        mismatches.append((row["full_path"], row["class"], folder_name))
-
-print(f"Found {len(mismatches)} mislabeled images (class vs folder mismatch).")
-
-# Compare classes vs folders
-classes_in_df = set(df["class"].unique())
-folders_in_fs = {f for f in os.listdir(dataset_root) if os.path.isdir(os.path.join(dataset_root, f))}
-
-print("Classes in DF but not in folders:", classes_in_df - folders_in_fs)
-print("Folders in FS but not in DF:", folders_in_fs - classes_in_df)
-
-def check_file_naming_issues(df):
-    issues = {"invalid_chars": [], "spaces": [], "long_paths": [], "case_conflicts": [], "duplicate_names_across_classes": []}
-
-    seen_names = {}
-
-    for _, row in df.iterrows():
-        fpath = row["full_path"]              # full path
-        fname = os.path.basename(fpath)       # just filename
-        cls = row["class"]
-
-        if re.search(r'[<>:"/\\|?*]', fname):  # Windows restricted chars
-            issues["invalid_chars"].append(fpath)
-
-        if "  " in fname or fname.startswith(" ") or fname.endswith(" "):
-            issues["spaces"].append(fpath)
-
-        if len(fpath) > 255:
-            issues["long_paths"].append(fpath)
-
-        lower_name = fname.lower()
-        if lower_name in seen_names and seen_names[lower_name] != cls:
-            issues["case_conflicts"].append((fpath, seen_names[lower_name]))
-        else:
-            seen_names[lower_name] = cls
-
-    duplicates = df.groupby(df["full_path"].apply(os.path.basename))["class"].nunique()
-    duplicates = duplicates[duplicates > 1].index.tolist()
-    for dup in duplicates:
-        dup_paths = df[df["full_path"].str.endswith(dup)]["full_path"].tolist()
-        issues["duplicate_names_across_classes"].extend(dup_paths)
-
-    return issues
-
-# Run the check
-naming_issues = check_file_naming_issues(df)
-
-for issue_type, files in naming_issues.items():
-    print(f"\n{issue_type.upper()} ({len(files)})")
-    for f in files[:10]:  # preview first 10
-        print(f)
-
-"""# **Data Preprocessing Process**"""
-
-def preprocess_image(path, target_size=(256, 256), augment=True):
-    img = tf.io.read_file(path)
-    img = tf.image.decode_image(img, channels=3, expand_animations=False)
-    img = tf.image.resize(img, target_size)
-    img = tf.cast(img, tf.float32) / 255.0
-
-    if augment and tf.random.uniform(()) < 0.1:  # Only 10% chance
-        img = tf.image.random_flip_left_right(img)
-        img = tf.image.random_flip_up_down(img)
-        img = tf.image.random_brightness(img, max_delta=0.1)
-        img = tf.image.random_contrast(img, lower=0.9, upper=1.1)
-
-    return img
-
-le = LabelEncoder()
-df['label'] = le.fit_transform(df['class'])
-
-# Prepare paths and labels
-paths = df['full_path'].values
-labels = df['label'].values
-
-AUTOTUNE = tf.data.AUTOTUNE
-batch_size = 32
-
-# Split data into train+val and test (10% test)
-train_val_paths, test_paths, train_val_labels, test_labels = train_test_split(
-    paths, labels, test_size=0.1, random_state=42, stratify=labels
-)
-
-# Split train+val into train and val (10% of train_val as val)
-train_paths, val_paths, train_labels, val_labels = train_test_split(
-    train_val_paths, train_val_labels, test_size=0.1, random_state=42, stratify=train_val_labels
-)
-
-# Create datasets
-def load_and_preprocess(path, label):
-    return preprocess_image(path), label
-
-train_ds = tf.data.Dataset.from_tensor_slices((train_paths, train_labels))
-train_ds = train_ds.map(lambda x, y: (preprocess_image(x, augment=True), y), num_parallel_calls=AUTOTUNE)
-train_ds = train_ds.shuffle(1024).batch(batch_size).prefetch(AUTOTUNE)
-
-val_ds = tf.data.Dataset.from_tensor_slices((val_paths, val_labels))
-val_ds = val_ds.map(load_and_preprocess, num_parallel_calls=AUTOTUNE)
-val_ds = val_ds.batch(batch_size).prefetch(AUTOTUNE)
-
-test_ds = tf.data.Dataset.from_tensor_slices((test_paths, test_labels))
-test_ds = test_ds.map(load_and_preprocess, num_parallel_calls=AUTOTUNE)
-test_ds = test_ds.batch(batch_size).prefetch(AUTOTUNE)
-
-print("Dataset sizes:")
-print(f"Train: {len(train_paths)} images")
-print(f"Validation: {len(val_paths)} images")
-print(f"Test: {len(test_paths)} images")
-print("--------------------------------------------------")
-print("Train labels sample:", train_labels[:10])
-print("Validation labels sample:", val_labels[:10])
-print("Test labels sample:", test_labels[:10])
-
-# Preview normalized image stats and visualization
-for image_batch, label_batch in train_ds.take(1):
-    # Print pixel value stats for first image in the batch
-    image = image_batch[0]
-    label = label_batch[0]
-    print("Image dtype:", image.dtype)
-    print("Min pixel value:", tf.reduce_min(image).numpy())
-    print("Max pixel value:", tf.reduce_max(image).numpy())
-    print("Label:", label.numpy())
-
-    # Show the image
-    plt.imshow(image.numpy())
-    plt.title(f"Label: {label.numpy()}")
-    plt.axis('off')
-    plt.show()
-print("---------------------------------------------------")
-print("Number of Classes: ", len(le.classes_))
-
-# After train_ds is defined
-for image_batch, label_batch in train_ds.take(1):
-    print("Image batch shape:", image_batch.shape)  # full batch shape
-    print("Label batch shape:", label_batch.shape)  # labels shape
-
-    input_shape = image_batch.shape[1:]  # shape of a single image
-    print("Single image shape:", input_shape)
-    break
-
-"""# **Model Loading**"""
-
-inception = InceptionV3(input_shape=input_shape, weights='imagenet', include_top=False)
-
-# don't train existing weights
-for layer in inception.layers:
-    layer.trainable = False
-
-# Number of classes
-print("Number of Classes: ", len(le.classes_))
-
-x = GlobalAveragePooling2D()(inception.output)
-x = Dense(512, activation='relu')(x)
-x = Dropout(0.5)(x)
-prediction = Dense(len(le.classes_), activation='softmax')(x)
-
-# create a model object
-model = Model(inputs=inception.input, outputs=prediction)
-
-# view the structure of the model
-model.summary()
-
-# tell the model what cost and optimization method to use
-model.compile(
-  loss='sparse_categorical_crossentropy',
-  optimizer='adam',
-  metrics=['accuracy']
-)
-
-"""# **Model Training**"""
-
-callbacks = [
-    EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True,  verbose = 1),
-    ModelCheckpoint("best_model.h5", save_best_only=True, monitor='val_loss',  verbose = 1),
-    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, min_lr=1e-5, verbose=1)
-]
-
-history = model.fit(train_ds, validation_data=val_ds, epochs=10, callbacks=callbacks, verbose = 1)
-
-"""# **Model Evaluation**"""
-
-model.evaluate(test_ds)
-
-"""# **Saving the Model**"""
-
-model.save("Simple_CNN_Classification.h5")
-files.download("Simple_CNN_Classification.h5")