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agetest_mobilevit_V_1.1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c1a6587212e34bcdcc7c60d50b63810f3f325f28fb3a638a706e3ae905ce2b12
+size 11701594

app.py

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+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import streamlit as st
+from collections import deque
+from keras.models import load_model
+from ultralytics import YOLO
+import csv
+from Crypto.Cipher import AES
+from Crypto.Random import get_random_bytes
+import timm
+import base64
+import pandas as pd
+
+# Set page title and favicon
+st.set_page_config(page_title="Violence Detection and Analysis App", page_icon=":boom:")
+
+# CSS styling
+st.markdown(
+    """
+    <style>
+    .title {
+        color: #1f78b4;
+        text-align: center;
+        font-size: 36px;
+        margin-bottom: 30px;
+    }
+    .header {
+        color: #1f78b4;
+        font-size: 24px;
+        margin-top: 30px;
+    }
+    .btn-download {
+        background-color: #4CAF50;
+        border: none;
+        color: white;
+        padding: 15px 32px;
+        text-align: center;
+        text-decoration: none;
+        display: inline-block;
+        font-size: 16px;
+        margin: 4px 2px;
+        cursor: pointer;
+        border-radius: 10px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Load the pre-trained models for age and gender prediction
+age_model = timm.create_model('mobilevitv2_075.cvnets_in1k', pretrained=True, num_classes=5, global_pool='catavgmax')
+num_in_features = age_model.get_classifier().in_features
+age_model.fc = nn.Sequential(
+    nn.BatchNorm1d(num_in_features),
+    nn.Linear(in_features=num_in_features, out_features=512, bias=False),
+    nn.ReLU(),
+    nn.BatchNorm1d(512),
+    nn.Dropout(0.4),
+    nn.Linear(in_features=512, out_features=5, bias=False)
+)
+
+gender_model = timm.create_model('mobilevitv2_075.cvnets_in1k', pretrained=True, num_classes=2, global_pool='catavgmax')
+num_in_features = gender_model.get_classifier().in_features
+gender_model.fc = nn.Sequential(
+    nn.BatchNorm1d(num_in_features),
+    nn.Linear(in_features=num_in_features, out_features=512, bias=False),
+    nn.ReLU(),
+    nn.BatchNorm1d(512),
+    nn.Dropout(0.4),
+    nn.Linear(in_features=512, out_features=2, bias=False)
+)
+age_model.load_state_dict(torch.load('E:/Drive D/barchart/age models/age_mobile_V_1.1/PT/agetest_mobilevit_V_1.1.pt', map_location=torch.device('cpu')))
+gender_model.load_state_dict(torch.load('E:/Drive D/barchart/gender models/gender_mobilevit_version_1.1/pt/gendertest_mobilevit_V_1.1.pt', map_location=torch.device('cpu')))
+
+# Load the violence detection model
+violence_model = load_model('E:/TNIBF/modelnew (1).h5')
+MODEL = "E:/TNIBF/yolov8_people.pt"
+yolo_model = YOLO(MODEL)
+
+# Function to preprocess image for detection
+def preprocess_image(image, target_size=(256, 256)):
+    img = cv2.resize(image, target_size, interpolation=cv2.INTER_CUBIC)
+    img = img.astype(np.float32) / 255.0
+    img = (img - np.array([0.485, 0.456, 0.406])) / np.array([0.229, 0.224, 0.225])
+    img = np.transpose(img, (2, 0, 1))
+    img = np.expand_dims(img, axis=0)
+    return torch.tensor(img, dtype=torch.float32)
+
+# Function to detect violence in a frame
+def detect_violence(frame):
+    true_count = 0
+    Q = deque(maxlen=128)
+    frame_copy = frame.copy()
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    frame = cv2.resize(frame, (128, 128)).astype("float32") / 255
+    preds = violence_model.predict(np.expand_dims(frame, axis=0))[0]
+    Q.append(preds)
+    results = np.array(Q).mean(axis=0)
+    label = (results > 0.50)[0]
+    text_color = (255, 0, 0) if label else (0, 255, 0)
+    text = "Violence Detected" if label else "No Violence Detected"
+    cv2.putText(frame_copy, text, (35, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.25, text_color, 3)
+    return frame_copy, label
+
+# Function to detect people, their age, and gender in a frame
+def detect_people_age_gender(frame):
+    results = yolo_model(frame)
+    detections = []
+    male_count = 0
+    female_count = 0
+
+    for result in results:
+        boxes = result.boxes.xyxy
+        confidences = result.boxes.conf
+        classes = result.boxes.cls 
+
+        for box, confidence, cls in zip(boxes, confidences, classes):
+            x1, y1, x2, y2 = map(int, box)
+            person_crop = frame[y1:y2, x1:x2]
+            person_crop_resized = preprocess_image(person_crop, target_size=(224, 224))
+
+            age_preds = age_model(person_crop_resized)
+            age_class_index = np.argmax(age_preds.detach().numpy())
+            age_class_names = ['0-15', '15-30', '30-45', '45-60', '60+']
+            age = age_class_names[age_class_index]
+
+            gender_preds = gender_model(person_crop_resized)
+            gender = "Male" if np.argmax(gender_preds.detach().numpy()) == 0 else "Female"
+
+            detections.append((x1, y1, x2, y2, age, gender))
+
+            if gender == 'Male':
+                male_count += 1
+            else:
+                female_count += 1
+
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(frame, f"Age: {age}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+            cv2.putText(frame, f"Gender: {gender}", (x1, y1 - 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+    return frame, detections, male_count, female_count
+
+# Function to encrypt a file
+def encrypt_file(input_file, output_file, key):
+    cipher = AES.new(key, AES.MODE_CBC)
+    with open(input_file, 'rb') as infile:
+        data = infile.read()
+        # Add padding if needed
+        if len(data) % 16 != 0:
+            padding_length = 16 - len(data) % 16
+            data += bytes([padding_length]) * padding_length
+        ciphertext = cipher.encrypt(data)
+    with open(output_file, 'wb') as outfile:
+        outfile.write(cipher.iv)
+        outfile.write(ciphertext)
+
+# Function to decrypt a file
+def decrypt_file(input_file, output_file, key):
+    with open(input_file, 'rb') as infile:
+        iv = infile.read(16)
+        cipher = AES.new(key, AES.MODE_CBC, iv)
+        plaintext = cipher.decrypt(infile.read())
+        # Remove padding
+        padding_length = plaintext[-1]
+        plaintext = plaintext[:-padding_length]
+    with open(output_file, 'wb') as outfile:
+        outfile.write(plaintext)
+
+# Streamlit UI
+def main():
+    st.markdown('<h1 class="title">Violence Detection and Analysis App</h1>', unsafe_allow_html=True)
+
+    uploaded_file = st.file_uploader("Upload Video", type=["mp4"])
+    if uploaded_file is not None:
+        video_path = "temp_video.mp4"
+        with open(video_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+
+        st.video(video_path)
+
+        cap = cv2.VideoCapture(video_path)
+        frame_width = int(cap.get(3))
+        frame_height = int(cap.get(4))
+        out = cv2.VideoWriter("temp_output_video.mp4", cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width, frame_height))
+
+        csv_filename = "temp_detection_results.csv"
+        with open(csv_filename, mode='w', newline='') as file:
+            writer = csv.writer(file)
+            writer.writerow(['Frame', 'Violence', 'People Count', 'Male Count', 'Female Count'])
+
+            frame_number = 0
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                frame_violence, violence_label = detect_violence(frame)
+                frame_result, detections, male_count, female_count = detect_people_age_gender(frame_violence)
+
+                writer.writerow([frame_number, violence_label, len(detections), male_count, female_count])
+                out.write(frame_result)
+                frame_number += 1
+
+        cap.release()
+        out.release()
+
+        key = b'ThisIsASecretKey'
+        encrypted_file = 'temp_encrypted.csv'
+        decrypted_file = 'temp_decrypted.csv'
+
+        encrypt_file(csv_filename, encrypted_file, key)
+        decrypt_file(encrypted_file, decrypted_file, key)
+
+        # Download processed video
+        with open("temp_output_video.mp4", "rb") as f:
+            video_bytes = f.read()
+        video_b64 = base64.b64encode(video_bytes).decode('utf-8')
+        href = f'<a class="btn-download" href="data:video/mp4;base64,{video_b64}" download="processed_video.mp4">Download Processed Video</a>'
+        st.markdown(href, unsafe_allow_html=True)
+
+        # Download encrypted CSV
+        with open(encrypted_file, "rb") as f:
+            encrypted_data = f.read()
+        href = f'<a class="btn-download" href="data:file/csv;base64,{base64.b64encode(encrypted_data).decode()}" download="encrypted_csv.csv">Download Encrypted CSV</a>'
+        st.markdown(href, unsafe_allow_html=True)
+
+        # Download decrypted CSV
+        decrypted_data = pd.read_csv(decrypted_file)
+        st.markdown('<h2 class="header">Decrypted CSV:</h2>', unsafe_allow_html=True)
+        st.dataframe(decrypted_data)
+
+if __name__ == "__main__":
+    main()

gendertest_mobilevit_V_1.1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eef8158029cee77a5eb0b4410e44d8e56e693ed632d968f4ba4c4be8bbfdaadc
+size 11687083

modelnew.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c0ca2e447d364ec829fdfdc0f70faf2c3a1f6d71d7ca24cc835de7f7b9a229fb
+size 9533528

yolov8_people.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35b0bf8bdab93e98441de8a325532d48755ee1b3b3ece70cf3341b7c735e4f03
+size 6229593