Row 6735

Row ID: 6735 | Dataset Entry | Axioma AXP Content Repository

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import streamlit as st import os import random import numpy as np import tensorflow as tf from PIL import Image # Path to the directory containing the celebrity images IMAGE_DIR = "path to dataset" # List of classes (directory names) CLASSES = os.listdir(IMAGE_DIR) # Load the pre-trained face recognition model MODEL_PATH = "VGG16.h5" u/st.cache(allow_output_mutation=True) def load_model(): model = tf.keras.models.load_model(MODEL_PATH) return model with st.spinner('Model is being loaded..'): model=load_model() # Function to preprocess the input image def preprocess_image(image_path): img = Image.open(image_path).convert('RGB') img = img.resize((224, 224)) # Assuming the model input size is 224x224 img = np.array(img) / 255.0 img = np.expand_dims(img, axis=0) return img # Function to get the name of the celebrity def get_celebrity_name(image_path): # Preprocess the image img = preprocess_image(image_path) # Predict using the loaded model predictions = model.predict(img) # Get the predicted class index predicted_index = np.argmax(predictions) # Get the celebrity name from the class index celebrity_name = CLASSES[predicted_index] return celebrity_name # Function to get a random image def get_random_image(): # Choose a random class random_class = random.choice(CLASSES) # Get list of images in the class directory images = os.listdir(os.path.join(IMAGE_DIR, random_class)) # Choose a random image from the class random_image = random.choice(images) # Return the path to the random image return os.path.join(IMAGE_DIR, random_class, random_image) def main(): st.title("Celebrity Face Recognition") st.write("Click below to recognize a celebrity") # Get a random image random_image_path = get_random_image() # Display the image st.image(random_image_path, caption='Random Celebrity Image', use_column_width=True) if st.button("Recognize Celebrity"): # Get the name of the celebrity celebrity_name = get_celebrity_name(random_image_path) st.write(f"Predicted Celebrity:{celebrity_name}") if __name__ == "__main__": main()

FieldValue
text import streamlit as st import os import random import numpy as np import tensorflow as tf from PIL import Image # Path to the directory containing the celebrity images IMAGE_DIR = "path to dataset" # List of classes (directory names) CLASSES = os.listdir(IMAGE_DIR) # Load the pre-trained face recognition model MODEL_PATH = "VGG16.h5" u/st.cache(allow_output_mutation=True) def load_model(): model = tf.keras.…
label r/tensorflow
dataType comment
communityName r/tensorflow
datetime 2024-05-13
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Raw Record

{
  "text": "    import streamlit as st\n    import os\n    import random\n    import numpy as np\n    import tensorflow as tf\n    from PIL import Image\n    \n    # Path to the directory containing the celebrity images\n    IMAGE_DIR = \"path to dataset\"\n    \n    # List of classes (directory names)\n    CLASSES = os.listdir(IMAGE_DIR)\n    \n    \n    # Load the pre-trained face recognition model\n    \n    MODEL_PATH = \"VGG16.h5\"\n    \n    u/st.cache(allow_output_mutation=True)\n    def load_model():\n        model = tf.keras.models.load_model(MODEL_PATH)\n        return model\n    with st.spinner('Model is being loaded..'):\n        model=load_model()\n    \n    \n    \n    # Function to preprocess the input image\n    def preprocess_image(image_path):\n        img = Image.open(image_path).convert('RGB')\n        img = img.resize((224, 224))  # Assuming the model input size is 224x224\n        img = np.array(img) / 255.0\n        img = np.expand_dims(img, axis=0)\n        return img\n    \n    # Function to get the name of the celebrity\n    def get_celebrity_name(image_path):\n        # Preprocess the image\n        img = preprocess_image(image_path)\n        # Predict using the loaded model\n        predictions = model.predict(img)\n        # Get the predicted class index\n        predicted_index = np.argmax(predictions)\n        # Get the celebrity name from the class index\n        celebrity_name = CLASSES[predicted_index]\n        return celebrity_name\n    \n    # Function to get a random image\n    def get_random_image():\n        # Choose a random class\n        random_class = random.choice(CLASSES)\n        # Get list of images in the class directory\n        images = os.listdir(os.path.join(IMAGE_DIR, random_class))\n        # Choose a random image from the class\n        random_image = random.choice(images)\n        # Return the path to the random image\n        return os.path.join(IMAGE_DIR, random_class, random_image)\n    \n    def main():\n        st.title(\"Celebrity Face Recognition\")\n        st.write(\"Click below to recognize a celebrity\")\n    \n        # Get a random image\n        random_image_path = get_random_image()\n    \n        # Display the image\n        st.image(random_image_path, caption='Random Celebrity Image', use_column_width=True)\n    \n        if st.button(\"Recognize Celebrity\"):\n            # Get the name of the celebrity\n            celebrity_name = get_celebrity_name(random_image_path)\n            st.write(f\"Predicted Celebrity:{celebrity_name}\")\n    \n    if __name__ == \"__main__\":\n        main()",
  "label": "r/tensorflow",
  "dataType": "comment",
  "communityName": "r/tensorflow",
  "datetime": "2024-05-13",
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}
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