app.py

import gradio as gr
import cv2, os, numpy as np, json, tempfile, time
from ultralytics import YOLO
from filterpy.kalman import KalmanFilter
from scipy.optimize import linear_sum_assignment

# ------------------------------------------------------------
# ⚙️ Load YOLO and flow centers
# ------------------------------------------------------------
MODEL_PATH = "yolov8n.pt"
model = YOLO(MODEL_PATH)
VEHICLE_CLASSES = [2, 3, 5, 7]  # car, motorbike, bus, truck

def load_flow_centers(flow_json):
    data = json.load(open(flow_json))
    centers = np.array(data["flow_centers"])
    centers = centers / (np.linalg.norm(centers, axis=1, keepdims=True) + 1e-6)
    return centers

# ------------------------------------------------------------
# 🧩 Simple Kalman Tracker
# ------------------------------------------------------------
class Track:
    def __init__(self, bbox, tid):
        self.id = tid
        self.kf = KalmanFilter(dim_x=4, dim_z=2)
        self.kf.F = np.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]])
        self.kf.H = np.array([[1,0,0,0],[0,1,0,0]])
        self.kf.P *= 1000.0
        self.kf.R *= 10.0
        self.kf.x[:2] = np.array(self.centroid(bbox)).reshape(2,1)
        self.trace = []

    def centroid(self,b):
        x1,y1,x2,y2=b
        return [(x1+x2)/2,(y1+y2)/2]
    def predict(self): self.kf.predict(); return self.kf.x[:2].reshape(2)
    def update(self,b):
        z=np.array(self.centroid(b)).reshape(2,1)
        self.kf.update(z)
        cx,cy=self.kf.x[:2].reshape(2)
        self.trace.append((float(cx),float(cy)))
        return (cx,cy)

# ------------------------------------------------------------
# 🚦 Wrong-Direction Analyzer
# ------------------------------------------------------------
def analyze_direction(trace, centers):
    if len(trace)<3: return "NA",1.0
    v = np.array(trace[-1]) - np.array(trace[-3])     # motion vector
    if np.linalg.norm(v)<1e-6: return "NA",1.0
    v = v / np.linalg.norm(v)
    sims = np.dot(centers, v)
    max_sim = np.max(sims)
    if max_sim < 0: return "WRONG", float(max_sim)
    return "OK", float(max_sim)

# ------------------------------------------------------------
# 🎥 Process Video
# ------------------------------------------------------------
def process_video(video_path, flow_json):
    centers = load_flow_centers(flow_json)
    cap = cv2.VideoCapture(video_path)
    fps = cap.get(cv2.CAP_PROP_FPS) or 25
    w,h = int(cap.get(3)), int(cap.get(4))

    tmp_out = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
    out = cv2.VideoWriter(tmp_out.name, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w,h))

    tracks, next_id = [], 0
    log = []

    while True:
        ret, frame = cap.read()
        if not ret: break
        results = model(frame, verbose=False)[0]
        detections=[]
        for box in results.boxes:
            if int(box.cls) in VEHICLE_CLASSES and box.conf>0.3:
                detections.append(box.xyxy[0].cpu().numpy())

        # predict existing tracks
        predicted = [trk.predict() for trk in tracks]
        predicted = np.array(predicted) if len(predicted)>0 else np.empty((0,2))

        assigned=set()
        if len(predicted)>0 and len(detections)>0:
            cost=np.zeros((len(predicted),len(detections)))
            for i,p in enumerate(predicted):
                for j,d in enumerate(detections):
                    cx,cy=((d[0]+d[2])/2,(d[1]+d[3])/2)
                    cost[i,j]=np.linalg.norm(p-np.array([cx,cy]))
            r,c=linear_sum_assignment(cost)
            for i,j in zip(r,c):
                if cost[i,j]<80:
                    assigned.add(j)
                    tracks[i].update(detections[j])

        # new tracks
        for j,d in enumerate(detections):
            if j not in assigned:
                trk=Track(d,next_id); next_id+=1
                trk.update(d)
                tracks.append(trk)

        # draw
        for trk in tracks:
            if len(trk.trace)<3: continue
            status, sim = analyze_direction(trk.trace, centers)
            x,y=map(int,trk.trace[-1])
            color=(0,255,0) if status=="OK" else ((0,0,255) if status=="WRONG" else (255,255,255))
            cv2.circle(frame,(x,y),4,color,-1)
            cv2.putText(frame,f"ID:{trk.id} {status}",(x-20,y-10),cv2.FONT_HERSHEY_SIMPLEX,0.5,color,1)
            for i in range(1,len(trk.trace)):
                cv2.line(frame,(int(trk.trace[i-1][0]),int(trk.trace[i-1][1])),
                                (int(trk.trace[i][0]),int(trk.trace[i][1])),color,1)
            log.append({"id":trk.id,"status":status,"cos_sim":round(sim,3)})

        out.write(frame)

    cap.release(); out.release()
    log_path = tempfile.NamedTemporaryFile(suffix=".json", delete=False).name
    with open(log_path,"w") as f: json.dump(log,f,indent=2)
    return tmp_out.name, log_path

# ------------------------------------------------------------
# 🖥️ Gradio Interface
# ------------------------------------------------------------
def run_app(video, flow_file):
    out_path, log_path = process_video(video, flow_file)
    log_data = json.load(open(log_path))
    summary = {"vehicles_analyzed": len(log_data)}
    return out_path, log_data, summary

description_text = """
### 🚦 Wrong-Direction Detection (Stage 3)
Uploads your traffic video and the **flow_stats.json** from Stage 2.  
Outputs an annotated video with ✅ OK / 🚫 WRONG labels per vehicle, plus a JSON log.
"""

example_vid = "10.mp4" if os.path.exists("10.mp4") else None
example_flow = "flow_stats.json" if os.path.exists("flow_stats.json") else None

demo = gr.Interface(
    fn=run_app,
    inputs=[
        gr.Video(label="Upload Traffic Video (.mp4)"),
        gr.File(label="Upload flow_stats.json (Stage 2 Output)")
    ],
    outputs=[
        gr.Video(label="Violation Output Video"),
        gr.JSON(label="Per-Vehicle Log"),
        gr.JSON(label="Summary")
    ],
    title="🚗 Wrong-Direction Detection – Stage 3",
    description=description_text,
    examples=[[example_vid, example_flow]] if example_vid and example_flow else None,
)

if __name__ == "__main__":
    demo.launch()