Upload 5 files

AGNew.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fb9b96c55e446e4eb163d0b6823bbe71b425499dee55193bfda18d99d93f480
+size 291801472

main.py

@@ -0,0 +1,89 @@
+import streamlit as st
+import cv2
+from PIL import Image
+import numpy as np
+from keras.models import load_model
+
+# Load models
+mymodel = load_model('./models/AGNew.h5')
+Emotion_model = load_model("./models/mymodel.h5")
+face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+# Define emotion labels
+emotion_labels = ["Anger", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]
+
+# Define a function to process the uploaded image
+def process_image(img):
+    # Convert image to grayscale
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # Detect faces in the image
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+    predictions = []
+    for (x, y, w, h) in faces:
+        # Extract face region
+        face_img = img[y:y+h, x:x+w]
+        # Resize face image for age and gender model
+        face_resized = cv2.resize(face_img, (200, 200))
+        normalized_face = face_resized / 255.0
+        normalized_face = np.expand_dims(normalized_face, axis=0)
+        # Resize face image for emotion model
+        face_resized1 = cv2.resize(face_img, (48, 48))
+        normalized_face1 = face_resized1 / 255.0
+        normalized_face1 = np.expand_dims(normalized_face1, axis=0)
+        # Predict age and gender
+        pred_age = int(mymodel.predict(normalized_face)[0][0][0])
+        pred_gender = mymodel.predict(normalized_face)[1][0][0]
+        # Predict emotion
+        pred_emotion = Emotion_model.predict(normalized_face1)[0]
+        # Append predictions
+        predictions.append({
+            "x": x,
+            "y": y,
+            "w": w,
+            "h": h,
+            "age": pred_age,
+            "gender": pred_gender,
+            "emotion": emotion_labels[np.argmax(pred_emotion)]
+        })
+    return predictions
+
+# Streamlit app
+
+def main():
+
+    # st.set_page_config(page_title="Age , Emotion and Gender Recognition", page_icon=":)", layout="wide", initial_sidebar_state="expanded")
+
+    st.title("Age , Emotion and Gender Recognition")
+
+    uploaded_file = st.file_uploader("Upload Image", type=['png', 'jpg', 'jpeg'])
+
+    if uploaded_file is not None:
+        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+        img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        predictions = process_image(img)
+
+        # Draw bounding boxes and predictions on the image
+        for i, prediction in enumerate(predictions, start=1):
+            x, y, w, h = prediction['x'], prediction['y'], prediction['w'], prediction['h']
+            cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 5)
+            age = int(prediction['age'])
+            gender = "Female" if prediction['gender'] > 0.5 else "Male"
+            label = f"{i}"
+            cv2.putText(img, label, (x, y + h + 35), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 10)
+
+        # Convert image to PNG format
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img_pil = Image.fromarray(img)
+
+        # Display image with predictions
+        st.image(img_pil, caption="Uploaded Image", use_column_width=True)
+
+        # Display predictions
+        for i, prediction in enumerate(predictions, start=1):
+            st.subheader(f"Prediction {i}:")
+            st.write(f"Age: {prediction['age']}")
+            st.write(f"Gender: {'Female' if prediction['gender'] > 0.5 else 'Male'}")
+            st.write(f"Emotion: {prediction['emotion']}")
+
+if __name__ == "__main__":
+    main()

mymodel.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cfd209147bc566b0cb737aa613c507aaf6e23db08c7426b70b085b991d48054
+size 343974136

requirements.txt

@@ -0,0 +1,6 @@
+tensorflow==2.14.0
+numpy>=1.18.5
+pandas>=1.0.0
+opencv-python
+flask==3.0.2
+uvicorn