fall_detection_system.py

# -*- coding: utf-8 -*-
"""fall_detection_system.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/15iogmvi7LKpzek-Zt_uNfiQoYdvVO-nY
"""

#@title ⬇️ Install Ultralytics + deps
!pip -q install ultralytics opencv-python numpy requests

import torch
print("Torch:", torch.__version__, "| CUDA available:", torch.cuda.is_available())

from google.colab import drive
drive.mount('/content/drive')

# ==== BIG, BOLD FALL MESSAGE VERSION (with extra red alert banner) ====
import cv2, math, os, time
import numpy as np
from ultralytics import YOLO

# --- EDIT PATHS ---
INPUT_VIDEO = "/content/drive/MyDrive/AI ML Projects/Fall Detection System/Fall Tiktok.mp4"
OUT_VIDEO   = "/content/drive/MyDrive/AI ML Projects/Fall Detection System/out_fall_annotated.mp4"
MODEL_WEIGHTS = "yolov8n-pose.pt"  # or "yolov8s-pose.pt"

# --- RULE THRESHOLDS (make easier if needed) ---
CONFIDENCE     = 0.25
IMG_SIZE       = 640
ANGLE_MAX_FALL = 35     # <= ~horizontal torso
HW_MAX_FALL    = 0.80   # <= flat-ish bbox (height/width)
PERSIST_FRAMES = 10     # consecutive frames to confirm FALL (try 8–12)
STAND_MIN_ANG  = 60     # standing if >=

# COCO-17 skeleton edges
COCO_EDGES = [
    (5,6),(5,7),(7,9),(6,8),(8,10),(5,11),(6,12),(11,12),
    (11,13),(13,15),(12,14),(14,16),(0,5),(0,6)
]

def torso_angle_deg(kps):
    try:
        sx = (kps[5][0] + kps[6][0]) / 2.0
        sy = (kps[5][1] + kps[6][1]) / 2.0
        hx = (kps[11][0] + kps[12][0]) / 2.0
        hy = (kps[11][1] + kps[12][1]) / 2.0
        dx, dy = abs(sx - hx), abs(sy - hy)
        return math.degrees(math.atan2(dy, dx))   # 0≈horizontal, 90≈vertical
    except:
        return 90.0

def draw_skeleton(img, kps, color, t=2):
    for a,b in COCO_EDGES:
        x1,y1 = int(kps[a][0]), int(kps[a][1])
        x2,y2 = int(kps[b][0]), int(kps[b][1])
        cv2.line(img, (x1,y1), (x2,y2), color, t)
    for (x,y) in kps[:, :2]:
        cv2.circle(img, (int(x),int(y)), 3, color, -1)

def center_banner(img, text, color=(0,0,255)):
    """Huge centered banner + top strip + red border."""
    H, W = img.shape[:2]
    # Center banner
    font_scale = max(1.0, min(W, H) / 600.0) * 1.8
    thickness  = 5
    (tw, th), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness)
    bx1 = max(10, (W - (tw + 80)) // 2)
    by1 = max(10, (H - (th + 80)) // 2)
    bx2 = min(W-10, bx1 + tw + 80)
    by2 = min(H-10, by1 + th + 80)

    overlay = img.copy()
    cv2.rectangle(overlay, (bx1, by1), (bx2, by2), color, -1)
    cv2.addWeighted(overlay, 0.45, img, 0.55, 0, img)

    tx = bx1 + (bx2 - bx1 - tw) // 2
    ty = by1 + (by2 - by1 + th) // 2
    # white text with black outline
    cv2.putText(img, text, (tx+2, ty+2), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0,0,0), thickness+2, cv2.LINE_AA)
    cv2.putText(img, text, (tx,   ty),   cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255,255,255), thickness, cv2.LINE_AA)

    # Top strip banner
    strip_h = max(50, th + 30)
    cv2.rectangle(img, (0,0), (W, strip_h), color, -1)
    cv2.putText(img, "FALL DOWN DETECTED", (20, strip_h - 15),
                cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255,255,255), 3, cv2.LINE_AA)
    # Red border
    cv2.rectangle(img, (0,0), (W-1,H-1), color, 12)

def box_label(img, x1, y1, txt, color):
    """Black bg + bold text always visible even near frame top."""
    font_scale = 0.95
    thickness  = 2
    (tw, th), _ = cv2.getTextSize(txt, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness)
    # Put label inside the box if the top is too near the frame edge
    y_text = y1 + th + 12 if y1 < (th + 14) else y1 - 8
    # Compute background box
    bg_x1 = x1
    bg_y1 = y_text - th - 10 if y1 >= (th + 14) else y1 + 2
    bg_x2 = x1 + tw + 14
    bg_y2 = y_text + 2 if y1 >= (th + 14) else y1 + th + 14
    cv2.rectangle(img, (bg_x1, bg_y1), (bg_x2, bg_y2), (0,0,0), -1)
    cv2.putText(img, txt, (x1 + 6, y_text), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, thickness, cv2.LINE_AA)

# --- Load model & video IO ---
model = YOLO(MODEL_WEIGHTS)
cap = cv2.VideoCapture(INPUT_VIDEO)
if not cap.isOpened():
    raise RuntimeError(f"Could not open: {INPUT_VIDEO}")

fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
W   = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)  or 1280)
H   = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) or 720)
os.makedirs(os.path.dirname(OUT_VIDEO), exist_ok=True)
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
writer = cv2.VideoWriter(OUT_VIDEO, fourcc, fps, (W, H))

persist = {}  # pid -> frames satisfying fall-like condition
frame_idx = 0

while True:
    ok, frame = cap.read()
    if not ok: break
    any_fall = False

    r = model(frame, conf=CONFIDENCE, imgsz=IMG_SIZE, verbose=False)[0]
    if r.boxes is not None and r.keypoints is not None:
        boxes = r.boxes.xyxy.cpu().numpy()
        kpss  = r.keypoints.xy.cpu().numpy()

        for i, box in enumerate(boxes):
            x1,y1,x2,y2 = map(int, box)
            h = max(1, y2-y1); w = max(1, x2-x1)
            hw = h/float(w)

            kps = kpss[i]
            ang = torso_angle_deg(kps)

            # fall-like this frame; then persistence to confirm
            fall_like = (ang <= ANGLE_MAX_FALL) and (hw <= HW_MAX_FALL)
            pid = i
            persist[pid] = persist.get(pid, 0) + 1 if fall_like else max(0, persist.get(pid, 0) - 1)
            fallen = persist[pid] >= PERSIST_FRAMES

            if fallen:
                any_fall = True
                color, thick = (0,0,255), 4
                cv2.rectangle(frame, (x1,y1), (x2,y2), color, thick)
                draw_skeleton(frame, kps, color, t=2)
                # Super clear per-person message (inside or near the box)
                box_label(frame, x1, y1, "PERSON FALLEN", (0,0,255))
            else:
                # show status for context (standing vs other)
                if ang >= STAND_MIN_ANG:
                    color, label = (0,255,0), "STANDING"
                else:
                    color, label = (0,165,255), "MOVING/OTHER"
                cv2.rectangle(frame, (x1,y1), (x2,y2), color, 2)
                draw_skeleton(frame, kps, color, t=2)
                box_label(frame, x1, y1, f"{label}  θ={ang:.0f}  h/w={hw:.2f}", color)

    # 🔴 EXTRA-LOUD global banner if any person is fallen
    if any_fall:
        center_banner(frame, "FALL DOWN DETECTED", color=(0,0,255))

    writer.write(frame)
    frame_idx += 1

cap.release()
writer.release()
print(f"[OK] Saved annotated video to:\n{OUT_VIDEO}")