Rename realtime.py to app.py

realtime.py → app.py

@@ -1,214 +1,104 @@
-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.")
+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.")
+