diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..93526df6c --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +venv/ +__pycache__/ diff --git a/app.py b/app.py new file mode 100644 index 000000000..918b58724 --- /dev/null +++ b/app.py @@ -0,0 +1,233 @@ +from moviepy.editor import ImageSequenceClip +from collections import Counter +import tempfile +import streamlit as st +import cv2 +import numpy as np +from keras.models import load_model +import time +import os +import base64 + +# Load the pre-trained model once + + +@st.cache_resource +def load_emotion_model(): + return load_model('best_model.h5') + + +# Load the model +model = load_emotion_model() + +# Load the face detection classifier +faceDetect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') + +# Define the labels for facial expressions +labels_dict = {0: 'Angry', 1: 'Disgust', 2: 'Fear', + 3: 'Happy', 4: 'Neutral', 5: 'Sad', 6: 'Surprise'} + +# Function to perform facial expression recognition on an image + + +def process_image(image): + # Resize image to reduce computation time + resized_image = cv2.resize(image, (0, 0), fx=0.5, fy=0.5) + gray = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY) + faces = faceDetect.detectMultiScale(gray, 1.1, 5) + + for x, y, w, h in faces: + sub_face_img = gray[y:y + h, x:x + w] + resized = cv2.resize(sub_face_img, (48, 48)) + normalize = resized / 255.0 + reshaped = np.reshape(normalize, (1, 48, 48, 1)) + result = model.predict(reshaped) + label = np.argmax(result, axis=1)[0] + + cv2.rectangle(resized_image, (x, y), (x + w, y + h), (0, 0, 255), 2) + cv2.rectangle(resized_image, (x, y - 20), + (x + w, y), (50, 50, 255), -1) + cv2.putText(resized_image, labels_dict[label], (x, y - 5), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + + return resized_image + + +def process_video(video_file): + cap = cv2.VideoCapture(video_file) + processed_frames = [] + + frame_window = st.empty() + processing_placeholder = st.empty() + + uploaded_fps = cap.get(cv2.CAP_PROP_FPS) + if uploaded_fps == 0: + st.error("Failed to retrieve valid frame rate for the video.") + return + + emotions_list = [] # List to store emotions of all frames + frame_number = 0 + while cap.isOpened(): + ret, frame = cap.read() + if not ret: + break + + if frame_number % 13 == 0: # Skip 5 frames + processed_frame = process_image(frame) + processed_frame_rgb = cv2.cvtColor( + processed_frame, cv2.COLOR_BGR2RGB) # Convert to RGB + processed_frames.append(processed_frame_rgb) + + # Display the processed frame + frame_window.image(processed_frame_rgb, + channels="RGB", use_column_width=True) + + # Display processing text and GIF + with open("load.svg", "rb") as f: + html_content = f.read() + processing_placeholder.html( + f'Processing... ') + + # Get emotion of the frame and add it to the list + emotion = get_dominant_emotion(processed_frame) + emotions_list.append(emotion) + + frame_number += 1 # Increment by 1 to process each frame, not skipping them + + cap.release() + + # Create video clip + clip = ImageSequenceClip(processed_frames, fps=4) + + # Write the processed video to a temporary file + with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_file: + output_video_path = temp_file.name + clip.write_videofile(output_video_path, codec='libx264') + + # Hide the video being processed + frame_window.empty() + processing_placeholder.empty() + + # Display the processed video + st.video(output_video_path) + + # Find the most dominant emotion + dominant_emotion = Counter(emotions_list).most_common(1)[0][0] + + # Display the frame with the most dominant emotion + most_dominant_frame = get_frame_with_emotion( + processed_frames, dominant_emotion) + st.image(most_dominant_frame, + caption=f"Most dominant emotion: {labels_dict[dominant_emotion]}", use_column_width=True) + + # Remove the temporary file + os.remove(output_video_path) + + +def get_dominant_emotion(frame): + gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) + faces = faceDetect.detectMultiScale(gray, 1.1, 5) + + emotions = [] + for x, y, w, h in faces: + sub_face_img = gray[y:y + h, x:x + w] + resized = cv2.resize(sub_face_img, (48, 48)) + normalize = resized / 255.0 + reshaped = np.reshape(normalize, (1, 48, 48, 1)) + result = model.predict(reshaped) + label = np.argmax(result, axis=1)[0] + emotions.append(label) + + if emotions: + return max(set(emotions), key=emotions.count) + else: + return None + + +def get_frame_with_emotion(frames, emotion): + for frame in frames: + gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) + faces = faceDetect.detectMultiScale(gray, 1.1, 5) + + for x, y, w, h in faces: + sub_face_img = gray[y:y + h, x:x + w] + resized = cv2.resize(sub_face_img, (48, 48)) + normalize = resized / 255.0 + reshaped = np.reshape(normalize, (1, 48, 48, 1)) + result = model.predict(reshaped) + label = np.argmax(result, axis=1)[0] + + if label == emotion: + cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2) + cv2.rectangle(frame, (x, y - 20), + (x + w, y), (50, 50, 255), -1) + cv2.putText(frame, labels_dict[label], (x, y - 5), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + return frame + + return None + +# Function to start the webcam and perform facial expression recognition + + +def webcam_stream(): + video = cv2.VideoCapture(0) + frame_window = st.image([]) + + while st.session_state['run']: + ret, frame = video.read() + if not ret: + st.write("Failed to grab frame") + break + + frame = process_image(frame) + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame_window.image(frame_rgb) + + time.sleep(0.03) + + video.release() + frame_window.image([]) + + +# Streamlit UI +st.title("Facial Expression Recognition") + +# Upload a file +uploaded_file = st.file_uploader("Please upload a video", type=["mp4", "avi"]) + +if uploaded_file is not None: + file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) + if uploaded_file.type.startswith('image'): + image = cv2.imdecode(file_bytes, 1) + st.image(image, channels="BGR") + processed_image = process_image(image) + st.image(processed_image, channels="BGR") + else: + video_path = f"temp_video.{uploaded_file.type.split('/')[-1]}" + with open(video_path, 'wb') as out_file: + out_file.write(file_bytes) + process_video(video_path) + +if 'run' not in st.session_state: + st.session_state['run'] = False + +st.title("Real Time Facial Expression Recognition") + +# Display the start and stop buttons side by side +col1, col2 = st.columns(2) +with col1: + if st.button("Start Webcam"): + st.session_state['run'] = True + st.experimental_rerun() + +with col2: + if st.button("Stop Webcam"): + st.session_state['run'] = False + st.experimental_rerun() + +if st.session_state['run']: + st.write("") + webcam_stream() 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file mode 100644 index 000000000..db3531c83 --- /dev/null +++ b/model.ipynb @@ -0,0 +1,410 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, BatchNormalization, LeakyReLU, SpatialDropout2D\n", + "from tensorflow.keras.regularizers import l2\n", + "import tensorflow as tf\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "train_data_dir = 'data/train/'\n", + "validation_data_dir = 'data/test/'" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 28709 images belonging to 7 classes.\n", + "Found 7178 images belonging to 7 classes.\n", + "Train Generator: \n", + "Validation Generator: \n" + ] + } + ], + "source": [ + "# Data generators\n", + "train_datagen = ImageDataGenerator(\n", + " rescale=1./255,\n", + " rotation_range=30,\n", + " shear_range=0.3,\n", + " zoom_range=0.3,\n", + " horizontal_flip=True,\n", + " fill_mode='nearest'\n", + ")\n", + "\n", + "validation_datagen = ImageDataGenerator(rescale=1./255)\n", + "\n", + "train_generator = train_datagen.flow_from_directory(\n", + " train_data_dir,\n", + " color_mode='grayscale',\n", + " target_size=(48, 48),\n", + " batch_size=32,\n", + " class_mode='categorical',\n", + " shuffle=True\n", + ")\n", + "\n", + "validation_generator = validation_datagen.flow_from_directory(\n", + " validation_data_dir,\n", + " color_mode='grayscale',\n", + " target_size=(48, 48),\n", + " batch_size=32,\n", + " class_mode='categorical',\n", + " shuffle=True\n", + ")\n", + "\n", + "# Print generator information\n", + "print(\"Train Generator:\", train_generator)\n", + "print(\"Validation Generator:\", validation_generator)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\HomePC\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_3\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_3\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d_12 (Conv2D)              │ (None, 46, 46, 64)     │           640 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_12          │ (None, 46, 46, 64)     │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ leaky_re_lu_12 (LeakyReLU)      │ (None, 46, 46, 64)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_13 (Conv2D)              │ (None, 44, 44, 128)    │        73,856 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_13          │ (None, 44, 44, 128)    │           512 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ leaky_re_lu_13 (LeakyReLU)      │ (None, 44, 44, 128)    │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_9 (MaxPooling2D)  │ (None, 22, 22, 128)    │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ spatial_dropout2d_9             │ (None, 22, 22, 128)    │             0 │\n",
+       "│ (SpatialDropout2D)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_14 (Conv2D)              │ (None, 20, 20, 256)    │       295,168 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_14          │ (None, 20, 20, 256)    │         1,024 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ leaky_re_lu_14 (LeakyReLU)      │ (None, 20, 20, 256)    │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_10 (MaxPooling2D) │ (None, 10, 10, 256)    │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ spatial_dropout2d_10            │ (None, 10, 10, 256)    │             0 │\n",
+       "│ (SpatialDropout2D)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_15 (Conv2D)              │ (None, 8, 8, 512)      │     1,180,160 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_15          │ (None, 8, 8, 512)      │         2,048 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ leaky_re_lu_15 (LeakyReLU)      │ (None, 8, 8, 512)      │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_11 (MaxPooling2D) │ (None, 4, 4, 512)      │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ spatial_dropout2d_11            │ (None, 4, 4, 512)      │             0 │\n",
+       "│ (SpatialDropout2D)              │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_3 (Flatten)             │ (None, 8192)           │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (Dense)                 │ (None, 512)            │     4,194,816 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (Dropout)             │ (None, 512)            │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (Dense)                 │ (None, 7)              │         3,591 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ conv2d_12 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m640\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ leaky_re_lu_12 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_13 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m44\u001b[0m, \u001b[38;5;34m44\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m44\u001b[0m, \u001b[38;5;34m44\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ leaky_re_lu_13 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m44\u001b[0m, \u001b[38;5;34m44\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ spatial_dropout2d_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mSpatialDropout2D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ leaky_re_lu_14 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_10 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ spatial_dropout2d_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mSpatialDropout2D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m1,180,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m2,048\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ leaky_re_lu_15 (\u001b[38;5;33mLeakyReLU\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_11 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ spatial_dropout2d_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mSpatialDropout2D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ flatten_3 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8192\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m4,194,816\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m) │ \u001b[38;5;34m3,591\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 5,752,071 (21.94 MB)\n",
+       "
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 Trainable params: 5,750,151 (21.94 MB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m5,750,151\u001b[0m (21.94 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 1,920 (7.50 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,920\u001b[0m (7.50 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + } + ], + "source": [ + "# Model definition\n", + "model = Sequential()\n", + "model.add(Conv2D(64, kernel_size=(3, 3), kernel_regularizer=l2(0.01), input_shape=(48, 48, 1)))\n", + "model.add(BatchNormalization())\n", + "model.add(LeakyReLU())\n", + "model.add(Conv2D(128, kernel_size=(3, 3), kernel_regularizer=l2(0.01)))\n", + "model.add(BatchNormalization())\n", + "model.add(LeakyReLU())\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(SpatialDropout2D(0.3))\n", + "\n", + "model.add(Conv2D(256, kernel_size=(3, 3), kernel_regularizer=l2(0.01)))\n", + "model.add(BatchNormalization())\n", + "model.add(LeakyReLU())\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(SpatialDropout2D(0.4))\n", + "\n", + "model.add(Conv2D(512, kernel_size=(3, 3), kernel_regularizer=l2(0.01)))\n", + "model.add(BatchNormalization())\n", + "model.add(LeakyReLU())\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(SpatialDropout2D(0.5))\n", + "\n", + "model.add(Flatten())\n", + "model.add(Dense(512, activation='relu', kernel_regularizer=l2(0.01)))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(7, activation='softmax'))\n", + "\n", + "nadam = tf.keras.optimizers.Nadam(learning_rate=0.001)\n", + "model.compile(optimizer=nadam, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Print model summary\n", + "print(model.summary())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of training images: 28709\n", + "Number of testing images: 7178\n" + ] + } + ], + "source": [ + "\n", + "# Count number of images in train and test directories\n", + "def count_images(directory):\n", + " count = 0\n", + " for root, dirs, files in os.walk(directory):\n", + " count += len(files)\n", + " return count\n", + "\n", + "num_train_imgs = count_images(train_data_dir)\n", + "num_test_imgs = count_images(validation_data_dir)\n", + "\n", + "print(\"Number of training images:\", num_train_imgs)\n", + "print(\"Number of testing images:\", num_test_imgs)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Adjust steps per epoch\n", + "steps_per_epoch_train = num_train_imgs // 32 \n", + "steps_per_epoch_test = num_test_imgs // 32 \n", + "\n", + "# Learning rate scheduling\n", + "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=1e-6, verbose=1)\n", + "\n", + "# Model checkpoint to save the best weights only\n", + "checkpoint = tf.keras.callbacks.ModelCheckpoint('best.weights.h5', monitor='val_accuracy', save_best_only=True, mode='max', verbose=1, save_weights_only=True)\n", + "\n", + "\n", + "epochs = 25" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Training the model\n", + "try:\n", + " history = model.fit(train_generator,\n", + " steps_per_epoch=steps_per_epoch_train,\n", + " epochs=epochs,\n", + " validation_data=validation_generator,\n", + " validation_steps=steps_per_epoch_test,\n", + " callbacks=[reduce_lr,checkpoint])\n", + "except Exception as e:\n", + " print(\"An error occurred during training:\", e)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the best weights\n", + "model.load_weights('best.weights.h5')\n", + "\n", + "# Save the entire model to HDF5 format\n", + "model.save('best_model.h5')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..cfd0cb10d Binary files /dev/null and b/requirements.txt differ