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Browse files- realtime.py +214 -0
- requirements.txt +8 -0
realtime.py
ADDED
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import os
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import cv2
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras.models import model_from_json
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import streamlit as st
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from PIL import Image
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# Load model
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with open("jsn_model.json", "r") as json_file:
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loaded_model_json = json_file.read()
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model = model_from_json(loaded_model_json)
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model.load_weights('weights_model1.h5')
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# Loading the classifier from the file.
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face_haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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UPLOAD_FOLDER = 'static'
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ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
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def allowed_file(filename):
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"""Checks the file format when file is uploaded"""
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return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
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def Emotion_Analysis(image):
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"""It does prediction of Emotions found in the Image provided, saves as Images and returns them"""
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gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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faces = face_haar_cascade.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
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if len(faces) == 0:
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return None
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for (x, y, w, h) in faces:
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roi = gray_frame[y:y + h, x:x + w]
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roi = cv2.resize(roi, (48, 48))
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roi = roi.astype("float") / 255.0
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roi = tf.expand_dims(roi, axis=-1) # Adding channel dimension
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roi = np.expand_dims(roi, axis=0) # Adding batch dimension
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prediction = model.predict(roi)
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EMOTIONS_LIST = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]
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rec_col = {"Happy": (0, 255, 0), "Sad": (255, 0, 0), "Surprise": (255, 204, 55),
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"Angry": (0, 0, 255), "Disgust": (230, 159, 0), "Neutral": (0, 255, 255), "Fear": (128, 0, 128)}
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pred_emotion = EMOTIONS_LIST[np.argmax(prediction)]
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Text = str(pred_emotion)
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cv2.rectangle(image, (x, y), (x + w, y + h), rec_col[str(pred_emotion)], 2)
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cv2.rectangle(image, (x, y - 40), (x + w, y), rec_col[str(pred_emotion)], -1)
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cv2.putText(image, Text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
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return image, pred_emotion
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def video_frame_callback(frame):
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"""Callback function to process each frame of video"""
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image = np.array(frame)
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result = Emotion_Analysis(image)
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if result is not None:
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processed_image, _ = result
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return processed_image
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return frame
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st.title('Emotion Detection App')
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st.sidebar.title("Options")
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# Options for manual upload or webcam capture
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upload_option = st.sidebar.selectbox("Choose Upload Option", ["Image Upload", "Webcam"])
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if upload_option == "Image Upload":
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uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["png", "jpg", "jpeg", "gif"])
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if uploaded_file is not None and allowed_file(uploaded_file.name):
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image = Image.open(uploaded_file)
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image = np.array(image.convert('RGB')) # Ensure image is in RGB format
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result = Emotion_Analysis(image)
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if result is None:
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st.image(image, caption="Uploaded Image", use_column_width=True)
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st.error("No face detected")
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else:
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processed_image, pred_emotion = result
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st.image(processed_image, caption=f"Predicted Emotion: {pred_emotion}", use_column_width=True)
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elif upload_option == "Webcam":
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st.sidebar.write("Webcam Capture")
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run = st.checkbox('Run Webcam')
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FRAME_WINDOW = st.image([])
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camera = cv2.VideoCapture(0)
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while run:
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success, frame = camera.read()
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if not success:
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st.error("Unable to read from webcam. Please check your camera settings.")
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break
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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processed_frame = video_frame_callback(frame)
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FRAME_WINDOW.image(processed_frame)
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camera.release()
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else:
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st.write("Please select an option to start.")
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# import os
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# import cv2
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# import numpy as np
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# import tensorflow as tf
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# from tensorflow.keras.models import model_from_json
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# import streamlit as st
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# from PIL import Image
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# # Load model
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# with open("jsn_model.json", "r") as json_file:
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# loaded_model_json = json_file.read()
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# model = model_from_json(loaded_model_json)
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# model.load_weights('weights_model1.h5')
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# # Loading the classifier from the file.
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# face_haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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# UPLOAD_FOLDER = 'static'
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# ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
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# def allowed_file(filename):
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# """Checks the file format when file is uploaded"""
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# return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
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# def Emotion_Analysis(image):
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# """It does prediction of Emotions found in the Image provided, saves as Images and returns them"""
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# gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# faces = face_haar_cascade.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
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# if len(faces) == 0:
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# return None
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# for (x, y, w, h) in faces:
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# roi = gray_frame[y:y + h, x:x + w]
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# roi = cv2.resize(roi, (48, 48))
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# roi = roi.astype("float") / 255.0
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# roi = tf.expand_dims(roi, axis=-1) # Adding channel dimension
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# roi = np.expand_dims(roi, axis=0) # Adding batch dimension
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# prediction = model.predict(roi)
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# EMOTIONS_LIST = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]
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# rec_col = {"Happy": (0, 255, 0), "Sad": (255, 0, 0), "Surprise": (255, 204, 55),
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# "Angry": (0, 0, 255), "Disgust": (230, 159, 0), "Neutral": (0, 255, 255), "Fear": (128, 0, 128)}
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# pred_emotion = EMOTIONS_LIST[np.argmax(prediction)]
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# Text = str(pred_emotion)
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# cv2.rectangle(image, (x, y), (x + w, y + h), rec_col[str(pred_emotion)], 2)
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# cv2.rectangle(image, (x, y - 40), (x + w, y), rec_col[str(pred_emotion)], -1)
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# cv2.putText(image, Text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
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# return image, pred_emotion
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# def video_frame_callback(frame):
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# """Callback function to process each frame of video"""
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# image = np.array(frame)
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# result = Emotion_Analysis(image)
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# if result is not None:
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# processed_image, _ = result
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# return processed_image
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# return frame
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# st.title('Emotion Detection App')
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# st.sidebar.title("Options")
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# # Options for manual upload or webcam capture
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# upload_option = st.sidebar.selectbox("Choose Upload Option", ["Image Upload", "Webcam"])
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# if upload_option == "Image Upload":
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# uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["png", "jpg", "jpeg", "gif"])
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# if uploaded_file is not None and allowed_file(uploaded_file.name):
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# image = Image.open(uploaded_file)
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# image = np.array(image.convert('RGB')) # Ensure image is in RGB format
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# result = Emotion_Analysis(image)
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# if result is None:
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# st.image(image, caption="Uploaded Image", use_column_width=True)
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# st.error("No face detected")
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# else:
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# processed_image, pred_emotion = result
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# st.image(processed_image, caption=f"Predicted Emotion: {pred_emotion}", use_column_width=True)
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# elif upload_option == "Webcam":
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# st.sidebar.write("Webcam Capture")
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# run_webcam = st.sidebar.button('Run Webcam')
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# stop_webcam = st.sidebar.button('Stop Webcam')
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# FRAME_WINDOW = st.image([])
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# if run_webcam:
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# camera = cv2.VideoCapture(0)
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# st.session_state['run'] = True
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# if 'run' in st.session_state and st.session_state['run']:
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# while True:
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# success, frame = camera.read()
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# if not success:
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# st.error("Unable to read from webcam. Please check your camera settings.")
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# break
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# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# processed_frame = video_frame_callback(frame)
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# FRAME_WINDOW.image(processed_frame)
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# if stop_webcam:
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# st.session_state['run'] = False
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# camera.release()
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# break
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# else:
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# st.write("Please select an option to start.")
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requirements.txt
ADDED
@@ -0,0 +1,8 @@
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1 |
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Flask
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opencv-python
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numpy
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tensorflow ==2.12.0
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Werkzeug
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gunicorn
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cuda-python
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streamlit
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