app.py

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

# ------------------------------------------------------------
# 🔧  Safe-load fix for PyTorch 2.6
# ------------------------------------------------------------
import torch, ultralytics.nn.tasks as ultralytics_tasks
torch.serialization.add_safe_globals([ultralytics_tasks.DetectionModel])

# ------------------------------------------------------------
# ⚙️  YOLO setup
# ------------------------------------------------------------
MODEL_PATH = "yolov8n.pt"
model = YOLO(MODEL_PATH)
VEHICLE_CLASSES = [2, 3, 5, 7]  # car, motorcycle, bus, truck


# ------------------------------------------------------------
# 🧩  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)


# ------------------------------------------------------------
# 🧮  Direction analyzer
# ------------------------------------------------------------
def analyze_direction(trace, centers):
    if len(trace) < 3:
        return "NA", 1.0
    v = np.array(trace[-1]) - np.array(trace[-3])
    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)


# ------------------------------------------------------------
# 🧭  Load normalized flow centers
# ------------------------------------------------------------
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


# ------------------------------------------------------------
# 🎥  Process video
# ------------------------------------------------------------
def process_video(video_path, flow_json, show_only_wrong=False):
    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))

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

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

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

        # Assign detections to tracks
        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:
                t = Track(d, next_id)
                next_id += 1
                t.update(d)
                tracks.append(t)

        # --- 🧩 Draw + Log (toggle support) ---
        for trk in tracks:
            if len(trk.trace) < 3:
                continue
            status, sim = analyze_direction(trk.trace, centers)

            # Skip OKs if toggle is enabled
            if show_only_wrong and status != "WRONG":
                continue

            x, y = map(int, trk.trace[-1])
            color = (0,255,0) if status=="OK" else ((0,0,255) if status=="WRONG" else (200,200,200))
            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 once per unique vehicle
            if len(trk.trace) > 5 and not any(entry["id"] == trk.id for entry in log):
                log.append({"id": trk.id, "status": status, "cos_sim": round(sim,3)})

        out.write(frame)

    cap.release()
    out.release()

    # Unique summary
    unique_ids = {entry["id"] for entry in log}
    summary = {"vehicles_analyzed": len(unique_ids)}

    # Create ZIP bundle
    zip_path = tempfile.NamedTemporaryFile(suffix=".zip", delete=False).name
    with zipfile.ZipFile(zip_path, "w") as zf:
        zf.write(out_path, arcname="violation_output.mp4")
        zf.writestr("per_vehicle_log.json", json.dumps(log, indent=2))
        zf.writestr("summary.json", json.dumps(summary, indent=2))

    return out_path, log, summary, zip_path


# ------------------------------------------------------------
# 🖥️  Gradio interface
# ------------------------------------------------------------
def run_app(video, flow_file, show_only_wrong):
    vid, log_json, summary, zip_file = process_video(video, flow_file, show_only_wrong)
    return vid, log_json, summary, zip_file


description_text = """
### 🚦 Wrong-Direction Detection (Stage 3)
Upload your traffic video and the **flow_stats.json** from Stage 2.  
You can toggle whether to display all detections or only WRONG-direction vehicles.
"""

demo = gr.Interface(
    fn=run_app,
    inputs=[
        gr.Video(label="Upload Traffic Video (.mp4)"),
        gr.File(label="Upload flow_stats.json (Stage 2 Output)"),
        gr.Checkbox(label="Show Only Wrong Labels", value=False)
    ],
    outputs=[
        gr.Video(label="Violation Output Video"),
        gr.JSON(label="Per-Vehicle Log"),
        gr.JSON(label="Summary"),
        gr.File(label="⬇️ Download All Outputs (ZIP)")
    ],
    title="🚗 Wrong-Direction Detection – Stage 3 (Toggle + ZIP)",
    description=description_text,
    examples=None,
)

# Disable analytics / flagging / SSR
demo.flagging_mode = "never"
demo.cache_examples = False
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False, show_api=False)