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import gradio as gr
import torch
import cv2
import numpy as np
import time
from ultralytics import YOLO
import spaces
import os
class CrowdDetection:
def __init__(self, model_path="yolov8n.pt"):
self.model_path = model_path
@spaces.GPU
def crowd_detection(self, video_path):
try:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not os.path.exists(self.model_path):
model = YOLO("yolov8n.pt")
model.save(self.model_path)
else:
model = YOLO(self.model_path)
model.to(device)
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise ValueError(f"❌ Failed to open video: {video_path}")
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
output_path = "output_crowd.mp4"
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
if not out.isOpened():
cap.release()
raise ValueError(f"❌ Failed to initialize video writer")
CROWD_THRESHOLD = 10
frame_count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_count += 1
results = model(frame)
person_count = sum(1 for result in results for cls in result.boxes.cls.cpu().numpy() if int(cls) == 0)
for result in results:
boxes = result.boxes.xyxy.cpu().numpy()
classes = result.boxes.cls.cpu().numpy()
for box, cls in zip(boxes, classes):
if int(cls) == 0:
x1, y1, x2, y2 = map(int, box)
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(frame, "Person", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
alert_text = "Crowd Alert!" if person_count > CROWD_THRESHOLD else f"People: {person_count}"
cv2.putText(frame, alert_text, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255) if person_count > CROWD_THRESHOLD else (0, 255, 0), 2)
out.write(frame)
cap.release()
out.release()
if frame_count == 0 or not os.path.exists(output_path):
raise ValueError("❌ Processing failed: No frames processed or output not created")
return output_path
except Exception as e:
raise ValueError(f"Error in crowd_detection: {str(e)}")
class PeopleTracking:
def __init__(self, yolo_model_path="yolov8n.pt"):
self.model_path = yolo_model_path
@spaces.GPU
def people_tracking(self, video_path):
try:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not os.path.exists(self.model_path):
model = YOLO("yolov8n.pt")
model.save(self.model_path)
else:
model = YOLO(self.model_path)
model.to(device)
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise ValueError(f"❌ Failed to open video: {video_path}")
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
output_path = "output_tracking.mp4"
out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
if not out.isOpened():
cap.release()
raise ValueError(f"❌ Failed to initialize video writer")
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model.track(frame, persist=True)
for result in results:
boxes = result.boxes.xyxy.cpu().numpy()
classes = result.boxes.cls.cpu().numpy()
ids = result.boxes.id.cpu().numpy() if result.boxes.id is not None else np.arange(len(boxes))
for box, cls, obj_id in zip(boxes, classes, ids):
if int(cls) == 0:
x1, y1, x2, y2 = map(int, box)
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
cv2.putText(frame, f"ID {int(obj_id)}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
out.write(frame)
cap.release()
out.release()
if not os.path.exists(output_path):
raise ValueError("❌ Processing failed")
return output_path
except Exception as e:
raise ValueError(f"Error in people_tracking: {str(e)}")
class FallDetection:
def __init__(self, yolo_model_path="yolov8l.pt"):
self.model_path = yolo_model_path
@spaces.GPU
def fall_detection(self, video_path):
try:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not os.path.exists(self.model_path):
model = YOLO("yolov8l.pt")
model.save(self.model_path)
else:
model = YOLO(self.model_path)
model.to(device)
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise ValueError(f"❌ Failed to open video: {video_path}")
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
output_path = "output_fall.mp4"
out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
if not out.isOpened():
cap.release()
raise ValueError(f"❌ Failed to initialize video writer")
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model(frame)
for result in results:
boxes = result.boxes.xyxy.cpu().numpy()
classes = result.boxes.cls.cpu().numpy()
for box, cls in zip(boxes, classes):
if int(cls) == 0:
x1, y1, x2, y2 = map(int, box)
width = x2 - x1
height = y2 - y1
aspect_ratio = width / height if height > 0 else float('inf')
if aspect_ratio > 0.55:
color = (0, 0, 255)
label = "FALL DETECTED"
else:
color = (0, 255, 0)
label = "Standing"
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
out.write(frame)
cap.release()
out.release()
if not os.path.exists(output_path):
raise ValueError("❌ Processing failed")
return output_path
except Exception as e:
raise ValueError(f"Error in fall_detection: {str(e)}")
class FightDetection:
def __init__(self, yolo_model_path="yolov8n-pose.pt"):
self.model_path = yolo_model_path
@spaces.GPU
def fight_detection(self, video_path):
try:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not os.path.exists(self.model_path):
model = YOLO("yolov8n-pose.pt")
model.save(self.model_path)
else:
model = YOLO(self.model_path)
model.to(device)
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise ValueError(f"❌ Failed to open video: {video_path}")
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
output_path = "output_fight.mp4"
out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
if not out.isOpened():
cap.release()
raise ValueError(f"❌ Failed to initialize video writer")
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model.track(frame, persist=True)
fight_detected = False
person_count = 0
for result in results:
keypoints = result.keypoints.xy.cpu().numpy() if result.keypoints else []
boxes = result.boxes.xyxy.cpu().numpy() if result.boxes else []
classes = result.boxes.cls.cpu().numpy() if result.boxes else []
for box, kp, cls in zip(boxes, keypoints, classes):
if int(cls) == 0:
person_count += 1
x1, y1, x2, y2 = map(int, box)
if len(kp) > 7 and (kp[5][1] < y1 + (y2 - y1) * 0.3 or kp[7][1] < y1 + (y2 - y1) * 0.3):
fight_detected = True
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255) if fight_detected else (0, 255, 0), 2)
label = "FIGHT DETECTED" if fight_detected else "Person"
cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255) if fight_detected else (0, 255, 0), 2)
if fight_detected and person_count > 1:
cv2.putText(frame, "FIGHT ALERT!", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
out.write(frame)
cap.release()
out.release()
if not os.path.exists(output_path):
raise ValueError("❌ Processing failed")
return output_path
except Exception as e:
raise ValueError(f"Error in fight_detection: {str(e)}")
# Unified processing function with status output
def process_video(feature, video):
detectors = {
"Crowd Detection": CrowdDetection,
"People Tracking": PeopleTracking,
"Fall Detection": FallDetection,
"Fight Detection": FightDetection
}
try:
detector = detectors[feature]()
method_name = feature.lower().replace(" ", "_")
output_path = getattr(detector, method_name)(video)
return f"{feature} completed successfully", output_path
except Exception as e:
return f"Error: {str(e)}", None
# Gradio Interface with dual outputs
interface = gr.Interface(
fn=process_video,
inputs=[
gr.Dropdown(choices=["Crowd Detection", "People Tracking", "Fall Detection", "Fight Detection"], label="Select Feature"),
gr.Video(label="Upload Video")
],
outputs=[
gr.Textbox(label="Status"),
gr.Video(label="Processed Video")
],
title="YOLOv8 Multitask Video Processing",
description="Select a feature to process your video: Crowd Detection, People Tracking, Fall Detection, or Fight Detection."
)
if __name__ == "__main__":
interface.launch(debug=True) |