Uploadin files

.gitignore

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+*.idea

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# Model loading
+model = load_model('model.h5')
+
+# Specifying Height and Width
+IMAGE_HEIGHT, IMAGE_WIDTH = 128, 128
+
+SEQUENCE_LENGTH = 12
+
+CLASSES_LIST = ["NonViolence", "Violence"]
+
+def predict_video(video_file_path, sequence_length):
+
+    video_reader = cv2.VideoCapture(video_file_path)
+
+    # Get original width and height of the video.
+    original_video_width = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
+    original_video_height = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # List to store video frames
+    frames_list = []
+
+    # Storing the predicted class
+    predicted_class_name = ''
+
+    # Get total number of frames
+    video_frames_count = int(video_reader.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # interval after which frames will be added
+    skip_frames_window = max(int(video_frames_count / sequence_length), 1)
+
+    for frame_counter in range(sequence_length):
+        # set the current frame
+        video_reader.set(cv2.CAP_PROP_POS_FRAMES, frame_counter * skip_frames_window)
+
+        success, frame = video_reader.read()
+
+        if not success:
+            break
+
+        # resize the Frame
+        resized_frame = cv2.resize(frame, (IMAGE_HEIGHT, IMAGE_WIDTH))
+
+        # normalizing the resized frame.
+        normalized_frame = resized_frame / 255
+
+        # appending frames into the frames list
+        frames_list.append(normalized_frame)
+
+    # passing frames to model and get predictions
+    predicted_labels_probabilities = model.predict(np.expand_dims(frames_list, axis=0))[0]
+
+    # get index of class with highest probability.
+    predicted_label = np.argmax(predicted_labels_probabilities)
+
+    # Get the class name using the retrieved index.
+    predicted_class_name = CLASSES_LIST[predicted_label]
+
+    video_reader.release()
+
+    return predicted_class_name, predicted_labels_probabilities[predicted_label]
+
+
+def main():
+    st.title("Violence Detection")
+
+    extensions = ['mp4', 'png', 'avi', 'mov']
+    # Upload a video file
+    uploaded_file = st.file_uploader("Upload a video file", type=extensions)
+
+    if uploaded_file:
+        # Get video file path
+        video_file_path = "temp_video.mp4"
+        with open(video_file_path, "wb") as f:
+            f.write(uploaded_file.getvalue())
+
+        # Predict on the video
+        predicted_class, confidence = predict_video(video_file_path, SEQUENCE_LENGTH)
+
+        # Display the predicted class along with the prediction confidence.
+        st.write(f'Predicted: {predicted_class}\nConfidence: {confidence}')
+
+        # Play the actual video
+        st.video(video_file_path)
+
+
+if __name__ == "__main__":
+    main()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d7fe4455c97f6f78f5abedebbf363f160bd0d620dd66f7432db4e5dd9429e7f
+size 1244288

requirements.txt

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+opencv-python==4.8.0
+tensorflow==2.10.0
+streamlit==1.26.0