Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+from deepface import DeepFace
+import cv2
+import numpy as np
+
+
+# Set background color to black
+st.markdown(
+    """
+    <style>
+    body {
+        background-color: #000000;
+        color: white;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Center-align the heading
+st.markdown(
+    """
+    <h1 style='text-align: center;'>DeepFace - Age, Gender, Race, Expression Detection Program</h1>
+    """,
+    unsafe_allow_html=True
+)
+
+# Add the description
+st.markdown(
+    """
+    <p style='text-align: center;'>Application Created by:<br>
+     Shahzad Ahmed (2K20/IT/114)<br>
+     Aziz Dil Khan (2K20/IT/31)<br>
+     Muhammad Ahsan Qureshi (2K20/IT/68)</p>
+    """,
+    unsafe_allow_html=True
+)
+
+def drawFace(image, results):
+    for result in results:
+        x = result['region']['x']
+        y = result['region']['y']
+        h = result['region']['h']
+        w = result['region']['w']
+        age = result['age']
+        gender = 'M' if result['gender']['Man'] > result['gender']['Woman'] else 'F'
+        dominant_emotion = result['dominant_emotion']
+        dominant_race = result['dominant_race']
+        description = f'{age}{gender} - {dominant_emotion}'
+
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.putText(image, description, (x, y - 10), cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 3)
+        cv2.putText(image, dominant_race, (x, y + h + 30), cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 3)
+
+    return image
+st.title("DeepFace Analysis")
+
+# Add file upload option
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Read the uploaded image
+    image = Image.open(uploaded_file)
+    img_path = "temp_image.jpg"  # Save the image temporarily
+
+    # Save the image temporarily for OpenCV processing
+    image.save(img_path)
+
+    # Analyze the image using DeepFace
+    results = DeepFace.analyze(img_path=img_path, actions=['age', 'gender', 'race', 'emotion'])
+
+
+    # Read the image using OpenCV
+    cv_image = cv2.imread(img_path)
+    # Convert BGR to RGB color channel ordering
+    cv_image_rgb = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
+
+    # Adjust color balance
+    cv_image_rgb_adjusted = cv2.cvtColor(cv_image_rgb, cv2.COLOR_RGB2LAB)
+    avg_a = np.average(cv_image_rgb_adjusted[:, :, 1])
+    avg_b = np.average(cv_image_rgb_adjusted[:, :, 2])
+    cv_image_rgb_adjusted[:, :, 1] = cv_image_rgb_adjusted[:, :, 1] - ((np.average(cv_image_rgb_adjusted[:, :, 1]) - 128) * (cv_image_rgb_adjusted[:, :, 0] / 255.0) * 1.1)
+    cv_image_rgb_adjusted[:, :, 2] = cv_image_rgb_adjusted[:, :, 2] - ((np.average(cv_image_rgb_adjusted[:, :, 2]) - 128) * (cv_image_rgb_adjusted[:, :, 0] / 255.0) * 1.1)
+    cv_image_rgb_adjusted = cv2.cvtColor(cv_image_rgb_adjusted, cv2.COLOR_LAB2RGB)
+
+    annotated_image = drawFace(cv_image_rgb_adjusted, results)
+
+
+    # Display image and text side by side
+    col1, col2 = st.columns(2)
+    col1.image(annotated_image, caption='Annotated Image', use_column_width=True)
+    col2.write("Results:")
+    col2.write(results)