# Model recognition
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## Training source code:
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```python
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import cv2
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import numpy as np
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense, Dropout
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# Load dataset of images with desired output (facial expressions)
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data_dir = 'path/to/dataset'
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image_paths = [f for f in os.listdir(data_dir) if f.endswith('.jpg')]
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# Create a list to hold our image data and labels
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X_train = []
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y_train = []
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for i, img_path in enumerate(image_paths):
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# Load image from disk using OpenCV
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img = cv2.imread(os.path.join(data_dir, img_path))
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# Resize image to 224x224 pixels (standard for VGG16)
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img = cv2.resize(img, (224, 224))
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# Convert image to grayscale and normalize pixel values
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img_norm = img_gray / 255.0
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# Add image data to list
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X_train.append(np.array(img_norm))
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# Assign label for current facial expression (e.g., happy, sad, angry)
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if i % 3 == 0: # Label for 'happy' expression
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y_train.append(1)
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elif i % 3 == 1: # Label for 'sad' expression
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y_train.append(2)
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else: # Label for 'angry' expression
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y_train.append(3)
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else: # Label for 'angry' expression
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y_train.append(3)
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# Create a TensorFlow model using the VGG16 architecture as base
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model = Sequential()
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model.add(Dense(64, activation='relu', input_shape=(224*224,)))
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model.add(Dropout(0.2))
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model.add(Dense(32, activation='relu'))
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model.add(Dropout(0.2))
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model.add(Dense(3)) # Output layer for 3 facial expressions
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# Compile the model with suitable loss function and optimizer
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model.compile(loss='sparse_categorical_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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# Train the model using our dataset
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model.fit(np.array(X_train), np.array(y_train), epochs=10, batch_size=32)
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print("Model trained!")
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```
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## Using the recognition model
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```python
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import cv2
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from tensorflow.keras.models import load_model
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# Load trained model from disk
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model_path = 'path/to/trained/model.h5'
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model = load_model(model_path)
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# Capture video from webcam or load pre-existing image
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cap = cv2.VideoCapture(0)
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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# Convert frame to grayscale and resize to 224x224 pixels
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gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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resized_frame = cv2.resize(gray_frame, (224, 224))
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# Add batch dimension for prediction
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input_data = np.array([resized_frame])
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# Get output probabilities for each facial expression
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output_probabilities = model.predict(input_data)
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# Print the top 3 predicted expressions with their probabilities
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print(np.argsort(-output_probabilities[0])[:3], output_probabilities[0][:3])
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# Release^[[A^[[A^[[A^[[A^[[A^[[A^[[A^[[A resources^[[A^[[A^[[A^[[A^[[A^[[A^[[A^[[A^[[A and^[[A^[[A^[[A exit
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cap.release()
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cv2.destroyAllWindows()
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```
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