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realtime.py

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+import os
+import cv2
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import model_from_json
+import streamlit as st
+from PIL import Image
+
+# Load model
+with open("jsn_model.json", "r") as json_file:
+    loaded_model_json = json_file.read()
+model = model_from_json(loaded_model_json)
+model.load_weights('weights_model1.h5')
+
+# Loading the classifier from the file.
+face_haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+UPLOAD_FOLDER = 'static'
+ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
+
+def allowed_file(filename):
+    """Checks the file format when file is uploaded"""
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def Emotion_Analysis(image):
+    """It does prediction of Emotions found in the Image provided, saves as Images and returns them"""
+    gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_haar_cascade.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
+
+    if len(faces) == 0:
+        return None
+
+    for (x, y, w, h) in faces:
+        roi = gray_frame[y:y + h, x:x + w]
+        roi = cv2.resize(roi, (48, 48))
+        roi = roi.astype("float") / 255.0
+        roi = tf.expand_dims(roi, axis=-1)  # Adding channel dimension
+        roi = np.expand_dims(roi, axis=0)  # Adding batch dimension
+
+        prediction = model.predict(roi)
+        EMOTIONS_LIST = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]
+        rec_col = {"Happy": (0, 255, 0), "Sad": (255, 0, 0), "Surprise": (255, 204, 55),
+                   "Angry": (0, 0, 255), "Disgust": (230, 159, 0), "Neutral": (0, 255, 255), "Fear": (128, 0, 128)}
+
+        pred_emotion = EMOTIONS_LIST[np.argmax(prediction)]
+        Text = str(pred_emotion)
+
+        cv2.rectangle(image, (x, y), (x + w, y + h), rec_col[str(pred_emotion)], 2)
+        cv2.rectangle(image, (x, y - 40), (x + w, y), rec_col[str(pred_emotion)], -1)
+        cv2.putText(image, Text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
+
+    return image, pred_emotion
+
+def video_frame_callback(frame):
+    """Callback function to process each frame of video"""
+    image = np.array(frame)
+    result = Emotion_Analysis(image)
+    if result is not None:
+        processed_image, _ = result
+        return processed_image
+    return frame
+
+st.title('Emotion Detection App')
+
+st.sidebar.title("Options")
+
+# Options for manual upload or webcam capture
+upload_option = st.sidebar.selectbox("Choose Upload Option", ["Image Upload", "Webcam"])
+
+if upload_option == "Image Upload":
+    uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["png", "jpg", "jpeg", "gif"])
+
+    if uploaded_file is not None and allowed_file(uploaded_file.name):
+        image = Image.open(uploaded_file)
+        image = np.array(image.convert('RGB'))  # Ensure image is in RGB format
+        result = Emotion_Analysis(image)
+
+        if result is None:
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+            st.error("No face detected")
+        else:
+            processed_image, pred_emotion = result
+            st.image(processed_image, caption=f"Predicted Emotion: {pred_emotion}", use_column_width=True)
+
+elif upload_option == "Webcam":
+    st.sidebar.write("Webcam Capture")
+    run = st.checkbox('Run Webcam')
+    FRAME_WINDOW = st.image([])
+
+    camera = cv2.VideoCapture(0)
+
+    while run:
+        success, frame = camera.read()
+        if not success:
+            st.error("Unable to read from webcam. Please check your camera settings.")
+            break
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        processed_frame = video_frame_callback(frame)
+        FRAME_WINDOW.image(processed_frame)
+
+    camera.release()
+else:
+    st.write("Please select an option to start.")
+
+
+# import os
+# import cv2
+# import numpy as np
+# import tensorflow as tf
+# from tensorflow.keras.models import model_from_json
+# import streamlit as st
+# from PIL import Image
+
+# # Load model
+# with open("jsn_model.json", "r") as json_file:
+#     loaded_model_json = json_file.read()
+# model = model_from_json(loaded_model_json)
+# model.load_weights('weights_model1.h5')
+
+# # Loading the classifier from the file.
+# face_haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# UPLOAD_FOLDER = 'static'
+# ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
+
+# def allowed_file(filename):
+#     """Checks the file format when file is uploaded"""
+#     return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+# def Emotion_Analysis(image):
+#     """It does prediction of Emotions found in the Image provided, saves as Images and returns them"""
+#     gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+#     faces = face_haar_cascade.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
+
+#     if len(faces) == 0:
+#         return None
+
+#     for (x, y, w, h) in faces:
+#         roi = gray_frame[y:y + h, x:x + w]
+#         roi = cv2.resize(roi, (48, 48))
+#         roi = roi.astype("float") / 255.0
+#         roi = tf.expand_dims(roi, axis=-1)  # Adding channel dimension
+#         roi = np.expand_dims(roi, axis=0)  # Adding batch dimension
+
+#         prediction = model.predict(roi)
+#         EMOTIONS_LIST = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]
+#         rec_col = {"Happy": (0, 255, 0), "Sad": (255, 0, 0), "Surprise": (255, 204, 55),
+#                    "Angry": (0, 0, 255), "Disgust": (230, 159, 0), "Neutral": (0, 255, 255), "Fear": (128, 0, 128)}
+
+#         pred_emotion = EMOTIONS_LIST[np.argmax(prediction)]
+#         Text = str(pred_emotion)
+
+#         cv2.rectangle(image, (x, y), (x + w, y + h), rec_col[str(pred_emotion)], 2)
+#         cv2.rectangle(image, (x, y - 40), (x + w, y), rec_col[str(pred_emotion)], -1)
+#         cv2.putText(image, Text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
+
+#     return image, pred_emotion
+
+# def video_frame_callback(frame):
+#     """Callback function to process each frame of video"""
+#     image = np.array(frame)
+#     result = Emotion_Analysis(image)
+#     if result is not None:
+#         processed_image, _ = result
+#         return processed_image
+#     return frame
+
+# st.title('Emotion Detection App')
+
+# st.sidebar.title("Options")
+
+# # Options for manual upload or webcam capture
+# upload_option = st.sidebar.selectbox("Choose Upload Option", ["Image Upload", "Webcam"])
+
+# if upload_option == "Image Upload":
+#     uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["png", "jpg", "jpeg", "gif"])
+
+#     if uploaded_file is not None and allowed_file(uploaded_file.name):
+#         image = Image.open(uploaded_file)
+#         image = np.array(image.convert('RGB'))  # Ensure image is in RGB format
+#         result = Emotion_Analysis(image)
+
+#         if result is None:
+#             st.image(image, caption="Uploaded Image", use_column_width=True)
+#             st.error("No face detected")
+#         else:
+#             processed_image, pred_emotion = result
+#             st.image(processed_image, caption=f"Predicted Emotion: {pred_emotion}", use_column_width=True)
+
+# elif upload_option == "Webcam":
+#     st.sidebar.write("Webcam Capture")
+#     run_webcam = st.sidebar.button('Run Webcam')
+#     stop_webcam = st.sidebar.button('Stop Webcam')
+#     FRAME_WINDOW = st.image([])
+
+#     if run_webcam:
+#         camera = cv2.VideoCapture(0)
+#         st.session_state['run'] = True
+
+#     if 'run' in st.session_state and st.session_state['run']:
+#         while True:
+#             success, frame = camera.read()
+#             if not success:
+#                 st.error("Unable to read from webcam. Please check your camera settings.")
+#                 break
+#             frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+#             processed_frame = video_frame_callback(frame)
+#             FRAME_WINDOW.image(processed_frame)
+#             if stop_webcam:
+#                 st.session_state['run'] = False
+#                 camera.release()
+#                 break
+# else:
+#     st.write("Please select an option to start.")

requirements.txt

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+Flask
+opencv-python
+numpy
+tensorflow ==2.12.0
+Werkzeug
+gunicorn
+cuda-python
+streamlit