forked from dsutanto/bytetrack-counter-cpp
1290 lines
42 KiB
Python
1290 lines
42 KiB
Python
"""
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Edge production live counter — RTSP + YOLO RKNN + ByteTrack + line crossing.
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Runs on RK3588 hardware with RKNN model (320×320 input).
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Uses ByteTrack (Kalman filter + two-stage IoU association) for tracking.
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"""
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import numpy as np
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import cv2
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import csv
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import os
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import signal
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import time
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from datetime import datetime
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from pathlib import Path
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from dotenv import load_dotenv
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load_dotenv()
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from rknnlite.api import RKNNLite
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from batch_store import BatchStore
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# --- config (override via env / .env) ---
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OUTPUT_DIR = os.getenv("OUTPUT_DIR", "/opt/jetson-counter")
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DB_PATH = os.getenv("DB_PATH", f"{OUTPUT_DIR}/jetson_counter.db")
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STATE_FILE = os.getenv("STATE_FILE", f"{OUTPUT_DIR}/current_batch.json")
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SOURCE = os.getenv("SOURCE", "rtsp://user:pass@192.168.0.100:554/stream1")
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MODEL_PATH = os.getenv("MODEL_PATH", "/opt/jetson-counter/yolo11n.rknn")
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CAMERA_NAME = os.getenv("CAMERA_NAME", "CC1")
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OBJECT_LABEL = os.getenv("OBJECT_LABEL", "ayam-potong")
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CLASS_AYAM = os.getenv("CLASS_AYAM", "ayam")
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CLASS_TALENAN = os.getenv("CLASS_TALENAN", "talenan")
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LINE_X = int(os.getenv("LINE_X")) if os.getenv("LINE_X") else None
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LINE_X_FRAC = float(os.getenv("LINE_X_FRAC", "0.5"))
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CROSS_DIRECTION = os.getenv("CROSS_DIRECTION", "rtl").lower()
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IMGSZ = int(os.getenv("IMGSZ", "320"))
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HALF = os.getenv("HALF", "false").lower() == "true"
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CONF = float(os.getenv("CONF", "0.3"))
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DEVICE = int(os.getenv("DEVICE", "0"))
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# RKNN NPU core mask
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CORE_MASK = int(os.getenv("CORE_MASK", "1"))
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# YOLO decoder config
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NUM_CLASSES = int(os.getenv("NUM_CLASSES", "2"))
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SCORE_SIGMOID = os.getenv("SCORE_SIGMOID", "false").lower() == "true"
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# ByteTrack settings
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TRACK_HIGH_THRESH = float(os.getenv("TRACK_HIGH_THRESH", "0.5"))
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TRACK_LOW_THRESH = float(os.getenv("TRACK_LOW_THRESH", "0.1"))
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TRACK_MATCH_THRESH = float(os.getenv("TRACK_MATCH_THRESH", "0.8"))
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TRACK_BUFFER = int(os.getenv("TRACK_BUFFER", "30"))
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TRACK_MIN_HITS = int(os.getenv("TRACK_MIN_HITS", "3"))
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DAILY_CUTOFF_TIME = os.getenv("DAILY_CUTOFF_TIME", "20:00")
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BATCH_TIMEOUT_SECONDS = float(os.getenv("BATCH_TIMEOUT_SECONDS", "300"))
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IGNORE_BATCH_LABEL_TIMEOUT = float(
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os.getenv("IGNORE_BATCH_LABEL_TIMEOUT_SECONDS", "30")
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)
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MIN_OBJECT_PER_BATCH = int(os.getenv("MIN_OBJECT_PER_BATCH", "60"))
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MIN_DURATION_PER_BATCH = int(os.getenv("MIN_DURATION_PER_BATCH", "60"))
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EXPORT_CSV = os.getenv("EXPORT_CSV", "true").lower() == "true"
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CROSS_CSV = os.getenv("CROSS_CSV", f"{OUTPUT_DIR}/batch_crossings.csv")
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WARMUP_FRAMES = int(os.getenv("WARMUP_FRAMES", "30"))
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RECONNECT_DELAY_SEC = int(os.getenv("RECONNECT_DELAY_SEC", "3"))
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MAX_RECONNECT_ATTEMPTS = int(os.getenv("MAX_RECONNECT_ATTEMPTS", "0"))
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FLUSH_EVERY_N_FRAMES = int(os.getenv("FLUSH_EVERY_N_FRAMES", "100"))
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TRACKED_PRUNE_SEC = int(os.getenv("TRACKED_PRUNE_SEC", "300"))
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RECORD_VIDEO = os.getenv("RECORD_VIDEO", "false").lower() == "true"
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VIDEO_SEGMENT_SEC = int(os.getenv("VIDEO_SEGMENT_SEC", "3600"))
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OUTPUT_FPS = int(os.getenv("OUTPUT_FPS", "15"))
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LIVE_STREAM_ENABLED = os.getenv("LIVE_STREAM_ENABLED", "false").lower() == "true"
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LIVE_STREAM_FRAME_PATH = os.getenv(
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"LIVE_STREAM_FRAME_PATH", "/dev/shm/jetson-counter/live_frame.jpg"
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)
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LIVE_STREAM_QUALITY = int(os.getenv("LIVE_STREAM_QUALITY", "75"))
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LIVE_STREAM_EVERY_N = int(os.getenv("LIVE_STREAM_EVERY_N", "2"))
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RTSP_FFMPEG_OPTIONS = os.getenv(
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"OPENCV_FFMPEG_CAPTURE_OPTIONS",
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"rtsp_transport;tcp|fflags;nobuffer|flags;low_delay",
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)
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IS_LIVE = SOURCE.lower().startswith(("rtsp://", "http://"))
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CROSS_FLASH_FRAMES = 12
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POPUP_LIFETIME = 20
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LINE_PULSE_FRAMES = 12
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COUNT_PULSE_FRAMES = 15
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BATCH_PULSE_FRAMES = 20
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SKELETON = [(0, 1), (4, 3), (1, 2), (3, 2), (2, 6), (2, 5), (2, 7), (7, 8)]
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SK_COLORS = [
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(0, 255, 255),
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(0, 255, 255),
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(255, 0, 255),
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(255, 0, 255),
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(0, 255, 0),
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(255, 255, 0),
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(0, 0, 255),
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(200, 200, 0),
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]
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C_PANEL = (28, 24, 18)
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C_BORDER = (90, 85, 75)
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C_ACCENT = (255, 200, 60)
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C_GREEN = (80, 220, 100)
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C_TEXT = (235, 235, 235)
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C_MUTED = (150, 150, 150)
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C_AYAM_BOX = (0, 165, 255)
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C_TALENAN_BOX = (220, 120, 60)
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C_LINE_CORE = (180, 220, 255)
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C_LINE_GLOW = (100, 160, 220)
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shutdown_requested = False
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def request_shutdown(signum, frame):
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global shutdown_requested
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shutdown_requested = True
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print("\nShutdown requested — finishing current frame...")
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signal.signal(signal.SIGINT, request_shutdown)
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signal.signal(signal.SIGTERM, request_shutdown)
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# =============================================================================
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# YOLO output decoder (NMS only — boxes are pre-decoded by the model)
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# =============================================================================
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def _nms(boxes, scores, iou_thr=0.45):
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order = np.argsort(scores)[::-1]
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keep = []
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while len(order) > 0:
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idx = order[0]
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keep.append(idx)
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if len(order) == 1:
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break
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xx1 = np.maximum(boxes[idx, 0], boxes[order[1:], 0])
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yy1 = np.maximum(boxes[idx, 1], boxes[order[1:], 1])
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xx2 = np.minimum(boxes[idx, 2], boxes[order[1:], 2])
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yy2 = np.minimum(boxes[idx, 3], boxes[order[1:], 3])
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w = np.maximum(0.0, xx2 - xx1)
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h = np.maximum(0.0, yy2 - yy1)
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inter = w * h
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area_i = (boxes[idx, 2] - boxes[idx, 0]) * (boxes[idx, 3] - boxes[idx, 1])
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area_o = (boxes[order[1:], 2] - boxes[order[1:], 0]) * (
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boxes[order[1:], 3] - boxes[order[1:], 1]
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)
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iou = inter / (area_i + area_o - inter + 1e-16)
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order = order[1:][iou < iou_thr]
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return np.array(keep)
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# =============================================================================
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# IoU helpers (xyxy format)
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# =============================================================================
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def _ious_xyxy(boxes_a, boxes_b):
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"""Pairwise IoU: (N,4) vs (M,4) → (N,M) matrix."""
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n, m = len(boxes_a), len(boxes_b)
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if n == 0 or m == 0:
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return np.zeros((n, m), dtype=np.float32)
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xx1 = np.maximum(boxes_a[:, None, 0], boxes_b[None, :, 0])
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yy1 = np.maximum(boxes_a[:, None, 1], boxes_b[None, :, 1])
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xx2 = np.minimum(boxes_a[:, None, 2], boxes_b[None, :, 2])
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yy2 = np.minimum(boxes_a[:, None, 3], boxes_b[None, :, 3])
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iw = np.maximum(0.0, xx2 - xx1)
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ih = np.maximum(0.0, yy2 - yy1)
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inter = iw * ih
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area_a = (boxes_a[:, 2] - boxes_a[:, 0]) * (boxes_a[:, 3] - boxes_a[:, 1])
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area_b = (boxes_b[:, 2] - boxes_b[:, 0]) * (boxes_b[:, 3] - boxes_b[:, 1])
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return inter / (area_a[:, None] + area_b[None, :] - inter + 1e-16)
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def _greedy_match(cost_matrix, threshold=0.3):
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"""Greedy linear assignment. Returns pairs (row_idx, col_idx)."""
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if cost_matrix.size == 0:
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return []
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n, m = cost_matrix.shape
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flat = [(cost_matrix[i, j], i, j) for i in range(n) for j in range(m)]
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flat.sort()
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row_used = set()
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col_used = set()
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pairs = []
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for cost, i, j in flat:
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if cost >= threshold:
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break
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if i in row_used or j in col_used:
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continue
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row_used.add(i)
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col_used.add(j)
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pairs.append((i, j))
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return pairs
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# =============================================================================
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# Kalman filter box tracker (state: x, y, w, h, vx, vy, vw, vh)
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# =============================================================================
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class KalmanBoxTracker:
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count = 0
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def __init__(self, bbox_xyxy):
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KalmanBoxTracker.count += 1
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self.track_id = KalmanBoxTracker.count
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x1, y1, x2, y2 = bbox_xyxy
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w, h = x2 - x1, y2 - y1
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x, y = x1 + w / 2, y1 + h / 2
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self.kf = _KalmanFilter()
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self.kf.x[:4, 0] = np.array([x, y, w, h], dtype=np.float32)
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self.time_since_update = 0
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self.hits = 1
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self.hit_streak = 1
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self.age = 1
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def predict(self):
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if self.kf.x[6] + self.kf.x[2] <= 0:
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self.kf.x[6] *= 0.0
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self.kf.predict()
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self.age += 1
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self.time_since_update += 1
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def update(self, bbox_xyxy):
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self.time_since_update = 0
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self.hits += 1
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self.hit_streak += 1
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x1, y1, x2, y2 = bbox_xyxy
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w, h = x2 - x1, y2 - y1
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x, y = x1 + w / 2, y1 + h / 2
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self.kf.update(np.array([x, y, w, h], dtype=np.float32))
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def get_state(self):
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"""Returns xyxy bbox from Kalman state."""
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xx = self.kf.x[:4, 0]
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x, y, w, h = xx[0], xx[1], xx[2], xx[3]
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x1 = x - w / 2
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y1 = y - h / 2
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x2 = x + w / 2
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y2 = y + h / 2
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return np.array([x1, y1, x2, y2], dtype=np.float32)
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def get_cx(self):
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return float(self.kf.x[0, 0])
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class _KalmanFilter:
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"""8-state constant-velocity Kalman filter for bounding box tracking."""
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def __init__(self):
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ndim, dt = 4, 1.0
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self.motion_mat = np.eye(2 * ndim, 2 * ndim, dtype=np.float32)
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for i in range(ndim):
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self.motion_mat[i, ndim + i] = dt
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self.update_mat = np.eye(ndim, 2 * ndim, dtype=np.float32)
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self._std_weight_position = 1.0 / 20
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self._std_weight_velocity = 1.0 / 160
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self.x = np.zeros((8, 1), dtype=np.float32)
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self.P = np.eye(8, dtype=np.float32) * 10.0
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def predict(self):
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std_pos = [
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self._std_weight_position * self.x[2],
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self._std_weight_position * self.x[3],
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self._std_weight_position * self.x[2],
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self._std_weight_position * self.x[3],
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]
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std_vel = [
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self._std_weight_velocity * self.x[2],
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self._std_weight_velocity * self.x[3],
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self._std_weight_velocity * self.x[2],
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self._std_weight_velocity * self.x[3],
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]
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Q = np.diag(np.square(np.concatenate([std_pos, std_vel])))
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self.x = self.motion_mat @ self.x
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self.P = self.motion_mat @ self.P @ self.motion_mat.T + Q
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def update(self, z):
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R = np.diag(
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np.square(
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[
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self._std_weight_position * z[2],
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self._std_weight_position * z[3],
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self._std_weight_position * z[2],
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self._std_weight_position * z[3],
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]
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)
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)
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H = self.update_mat
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S = H @ self.P @ H.T + R
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K = self.P @ H.T @ np.linalg.inv(S)
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y = z.reshape(4, 1) - H @ self.x
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self.x = self.x + K @ y
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I_KH = np.eye(8) - K @ H
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self.P = I_KH @ self.P @ I_KH.T + K @ R @ K.T
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# =============================================================================
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# ByteTrack multi-object tracker
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# =============================================================================
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class ByteTracker:
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"""ByteTrack: two-stage association with Kalman filter prediction."""
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def __init__(
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self,
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track_high_thresh=0.5,
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track_low_thresh=0.1,
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match_thresh=0.8,
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track_buffer=30,
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min_hits=3,
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):
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self.high_thresh = track_high_thresh
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self.low_thresh = track_low_thresh
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self.match_thresh = match_thresh
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self.track_buffer = track_buffer
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self.min_hits = min_hits
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self.tracked_tracks = []
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self.lost_tracks = []
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self.removed_tracks = []
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self.frame_id = 0
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def update(self, boxes_xyxy, scores):
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self.frame_id += 1
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# --- separate detections by score ---
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if len(boxes_xyxy) > 0:
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remain = scores > self.low_thresh
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dets = boxes_xyxy[remain]
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det_scores = scores[remain]
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is_high = det_scores > self.high_thresh
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is_low = ~is_high
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else:
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dets = np.zeros((0, 4), dtype=np.float32)
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det_scores = np.zeros(0, dtype=np.float32)
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is_high = np.zeros(0, dtype=bool)
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is_low = np.zeros(0, dtype=bool)
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# --- Kalman predict all existing tracks ---
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track_pool = self.tracked_tracks + self.lost_tracks
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num_tracks = len(track_pool)
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# Per-frame tracking results
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matched_track_idx = set()
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det_to_track = {}
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tracked_map = {}
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lost_map = {}
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# Pre-allocate these for scoping
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high_idx = np.array([], dtype=np.int64)
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low_idx = np.array([], dtype=np.int64)
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match_pairs_high = []
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if num_tracks > 0:
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track_boxes = np.zeros((num_tracks, 4), dtype=np.float32)
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for ti, trk in enumerate(track_pool):
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trk.predict()
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track_boxes[ti] = trk.get_state()
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# --- first association: high-score ↔ all tracks ---
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high_idx = np.where(is_high)[0]
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high_dets = dets[is_high]
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unmatched_tracks = list(range(num_tracks))
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if len(high_dets) > 0:
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iou_mat = _ious_xyxy(high_dets, track_boxes)
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cost_mat = 1.0 - iou_mat
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matches = _greedy_match(cost_mat, threshold=1.0 - self.match_thresh)
|
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for di, ti in matches:
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det_global = int(high_idx[di])
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track_pool[ti].update(dets[det_global])
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track_pool[ti].hit_streak = max(1, track_pool[ti].hit_streak)
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matched_track_idx.add(ti)
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det_to_track[det_global] = track_pool[ti].track_id
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tracked_map[track_pool[ti].track_id] = track_pool[ti].get_cx()
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||
match_pairs_high.append((det_global, ti))
|
||
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unmatched_tracks = [
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t for t in range(num_tracks) if t not in matched_track_idx
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||
]
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||
|
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# --- second association: low-score ↔ unmatched tracks ---
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||
low_idx = np.where(is_low)[0]
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low_dets = dets[is_low]
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||
|
||
if len(low_dets) > 0 and len(unmatched_tracks) > 0:
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unmatched_boxes = track_boxes[unmatched_tracks]
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iou_mat = _ious_xyxy(low_dets, unmatched_boxes)
|
||
cost_mat = 1.0 - iou_mat
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matches2 = _greedy_match(cost_mat, threshold=0.5)
|
||
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||
for di, uti in matches2:
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||
det_global = int(low_idx[di])
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||
pool_idx = unmatched_tracks[uti]
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||
track_pool[pool_idx].update(dets[det_global])
|
||
track_pool[pool_idx].hit_streak = max(
|
||
1, track_pool[pool_idx].hit_streak
|
||
)
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||
matched_track_idx.add(pool_idx)
|
||
det_to_track[det_global] = track_pool[pool_idx].track_id
|
||
tracked_map[track_pool[pool_idx].track_id] = track_pool[
|
||
pool_idx
|
||
].get_cx()
|
||
|
||
# --- reset hit_streak for unmatched tracks ---
|
||
for ti, trk in enumerate(track_pool):
|
||
if ti not in matched_track_idx:
|
||
trk.hit_streak = 0
|
||
|
||
# --- lifecycle management ---
|
||
new_tracked = []
|
||
new_lost = []
|
||
for trk in track_pool:
|
||
if trk.time_since_update > self.track_buffer:
|
||
self.removed_tracks.append(trk)
|
||
elif trk.time_since_update > 0:
|
||
new_lost.append(trk)
|
||
else:
|
||
new_tracked.append(trk)
|
||
self.tracked_tracks = new_tracked
|
||
self.lost_tracks = new_lost
|
||
|
||
# --- confirmed tracks (both tracked and lost) ---
|
||
for trk in self.tracked_tracks + self.lost_tracks:
|
||
if trk.hit_streak >= self.min_hits or trk.hits >= self.min_hits:
|
||
tracked_map.setdefault(trk.track_id, trk.get_cx())
|
||
|
||
for trk in self.lost_tracks:
|
||
if trk.hit_streak >= self.min_hits or trk.hits >= self.min_hits:
|
||
lost_map[trk.track_id] = trk.get_cx()
|
||
|
||
# --- new tracks from unmatched high-score dets ---
|
||
high_all = np.where(is_high)[0]
|
||
matched_det_ids = set(det_to_track.keys())
|
||
for dg in high_all:
|
||
if int(dg) not in matched_det_ids:
|
||
trk = KalmanBoxTracker(dets[dg])
|
||
self.tracked_tracks.append(trk)
|
||
det_to_track[int(dg)] = trk.track_id
|
||
tracked_map[trk.track_id] = trk.get_cx()
|
||
|
||
return tracked_map, det_to_track, lost_map
|
||
|
||
|
||
# =============================================================================
|
||
# RKNN YOLO wrapper (detect output format: (1, 4+num_classes, N))
|
||
# =============================================================================
|
||
|
||
|
||
class RKNNYOLO:
|
||
def __init__(
|
||
self,
|
||
model_path,
|
||
core_mask=1,
|
||
imgsz=320,
|
||
conf=0.3,
|
||
iou=0.45,
|
||
num_classes=2,
|
||
num_keypoints=0,
|
||
score_sigmoid=False,
|
||
):
|
||
self.imgsz = imgsz
|
||
self.conf = conf
|
||
self.iou = iou
|
||
self.num_classes = num_classes
|
||
self.num_keypoints = num_keypoints
|
||
self.score_sigmoid = score_sigmoid
|
||
|
||
self.rknn = RKNNLite(verbose=False)
|
||
ret = self.rknn.load_rknn(model_path)
|
||
if ret != 0:
|
||
raise RuntimeError(f"Failed to load RKNN model: {model_path}")
|
||
ret = self.rknn.init_runtime(core_mask=core_mask)
|
||
if ret != 0:
|
||
raise RuntimeError(f"Failed to init RKNN runtime (core_mask={core_mask})")
|
||
|
||
try:
|
||
sdk_ver = self.rknn.get_sdk_version()
|
||
print(f"RKNN SDK version: {sdk_ver}")
|
||
except Exception:
|
||
pass
|
||
|
||
print(f"RKNN model loaded: {model_path} imgsz={imgsz} core_mask={core_mask}")
|
||
|
||
def _preprocess(self, frame):
|
||
h0, w0 = frame.shape[:2]
|
||
scale = min(self.imgsz / h0, self.imgsz / w0)
|
||
nh, nw = int(h0 * scale), int(w0 * scale)
|
||
resized = cv2.resize(frame, (nw, nh), interpolation=cv2.INTER_LINEAR)
|
||
|
||
letterbox = np.full((self.imgsz, self.imgsz, 3), 114, dtype=np.uint8)
|
||
dy = (self.imgsz - nh) // 2
|
||
dx = (self.imgsz - nw) // 2
|
||
letterbox[dy : dy + nh, dx : dx + nw] = resized
|
||
|
||
rgb = cv2.cvtColor(letterbox, cv2.COLOR_BGR2RGB)
|
||
gains = np.array([scale, scale, dy, dx], dtype=np.float32)
|
||
return rgb, gains
|
||
|
||
def __call__(self, frame):
|
||
h0, w0 = frame.shape[:2]
|
||
rgb, gains = self._preprocess(frame)
|
||
scale, _, pad_y, pad_x = gains
|
||
|
||
inp = np.expand_dims(rgb, axis=0)
|
||
inp = np.ascontiguousarray(inp.astype(np.uint8))
|
||
|
||
outputs = self.rknn.inference(inputs=[inp])
|
||
|
||
if len(outputs) == 0:
|
||
return []
|
||
|
||
out = outputs[0]
|
||
out = np.squeeze(out, axis=0)
|
||
|
||
if out.shape[0] == self.num_classes + 4:
|
||
out = out.T
|
||
|
||
boxes_cxcywh = out[:, :4].copy()
|
||
cls_raw = out[:, 4:].copy()
|
||
|
||
if self.score_sigmoid:
|
||
cls_scores = 1.0 / (1.0 + np.exp(-np.clip(cls_raw, -10, 10)))
|
||
else:
|
||
cls_scores = cls_raw
|
||
|
||
boxes_xyxy = np.stack(
|
||
[
|
||
boxes_cxcywh[:, 0] - boxes_cxcywh[:, 2] / 2,
|
||
boxes_cxcywh[:, 1] - boxes_cxcywh[:, 3] / 2,
|
||
boxes_cxcywh[:, 0] + boxes_cxcywh[:, 2] / 2,
|
||
boxes_cxcywh[:, 1] + boxes_cxcywh[:, 3] / 2,
|
||
],
|
||
axis=1,
|
||
)
|
||
|
||
max_scores = cls_scores.max(axis=1)
|
||
class_ids = cls_scores.argmax(axis=1)
|
||
mask = max_scores > self.conf
|
||
|
||
if mask.sum() == 0:
|
||
return []
|
||
|
||
bboxes = boxes_xyxy[mask].astype(np.float32)
|
||
scores = max_scores[mask].astype(np.float32)
|
||
clses = class_ids[mask]
|
||
|
||
bboxes[:, 0] = (bboxes[:, 0] - pad_x) / scale
|
||
bboxes[:, 1] = (bboxes[:, 1] - pad_y) / scale
|
||
bboxes[:, 2] = (bboxes[:, 2] - pad_x) / scale
|
||
bboxes[:, 3] = (bboxes[:, 3] - pad_y) / scale
|
||
|
||
bboxes[:, 0] = np.clip(bboxes[:, 0], 0, w0)
|
||
bboxes[:, 1] = np.clip(bboxes[:, 1], 0, h0)
|
||
bboxes[:, 2] = np.clip(bboxes[:, 2], 0, w0)
|
||
bboxes[:, 3] = np.clip(bboxes[:, 3], 0, h0)
|
||
|
||
detections = []
|
||
for cls_id in range(self.num_classes):
|
||
idx = np.where(clses == cls_id)[0]
|
||
if len(idx) == 0:
|
||
continue
|
||
keep = _nms(bboxes[idx], scores[idx], iou_thr=self.iou)
|
||
for k in keep:
|
||
j = idx[k]
|
||
detections.append(
|
||
{
|
||
"bbox": bboxes[j].tolist(),
|
||
"score": float(scores[j]),
|
||
"cls": int(clses[j]),
|
||
"keypoints": None,
|
||
}
|
||
)
|
||
return detections
|
||
|
||
def release(self):
|
||
self.rknn.release()
|
||
|
||
|
||
# =============================================================================
|
||
# Drawing helpers
|
||
# =============================================================================
|
||
|
||
|
||
def resolve_line_x(frame_width):
|
||
if LINE_X is not None:
|
||
return LINE_X
|
||
if LINE_X_FRAC != 0.5:
|
||
return int(frame_width * LINE_X_FRAC)
|
||
return frame_width // 2
|
||
|
||
|
||
def crossed_line(prev_cx, cx, line_x, direction=CROSS_DIRECTION):
|
||
if direction == "ltr":
|
||
return prev_cx < line_x <= cx
|
||
if direction == "both":
|
||
return (prev_cx > line_x >= cx) or (prev_cx < line_x <= cx)
|
||
return prev_cx > line_x >= cx
|
||
|
||
|
||
def now_str():
|
||
return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
|
||
|
||
def open_capture(source):
|
||
if source.lower().startswith(("rtsp://", "http://")):
|
||
os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = RTSP_FFMPEG_OPTIONS
|
||
cap = cv2.VideoCapture(source, cv2.CAP_FFMPEG)
|
||
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
|
||
return cap
|
||
|
||
|
||
def warmup_stream(cap, n=WARMUP_FRAMES):
|
||
print("Warming up stream...")
|
||
for _ in range(n):
|
||
cap.read()
|
||
print("Stream ready!")
|
||
|
||
|
||
def open_video_writer(path, w, h, fps):
|
||
return cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*"avc1"), fps, (w, h))
|
||
|
||
|
||
class CsvLogger:
|
||
def __init__(self, path, header):
|
||
Path(path).parent.mkdir(parents=True, exist_ok=True)
|
||
new_file = not Path(path).exists() or Path(path).stat().st_size == 0
|
||
self.file = open(path, "a", newline="", buffering=1)
|
||
self.writer = csv.writer(self.file)
|
||
if new_file:
|
||
self.writer.writerow(header)
|
||
self.file.flush()
|
||
|
||
def write_row(self, row):
|
||
self.writer.writerow(row)
|
||
self.file.flush()
|
||
|
||
def close(self):
|
||
self.file.close()
|
||
|
||
|
||
class VideoSegmentWriter:
|
||
def __init__(self, output_dir, w, h, fps, segment_sec):
|
||
self.output_dir = Path(output_dir)
|
||
self.output_dir.mkdir(parents=True, exist_ok=True)
|
||
self.w, self.h, self.fps = w, h, fps
|
||
self.segment_sec = segment_sec
|
||
self.segment_start = time.monotonic()
|
||
self.writer = None
|
||
self._open_next()
|
||
|
||
def _segment_path(self):
|
||
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||
return str(self.output_dir / f"live_{ts}.mp4")
|
||
|
||
def _open_next(self):
|
||
if self.writer is not None:
|
||
self.writer.release()
|
||
path = self._segment_path()
|
||
self.writer = open_video_writer(path, self.w, self.h, self.fps)
|
||
self.segment_start = time.monotonic()
|
||
print(f"Recording segment: {path}")
|
||
|
||
def write(self, frame):
|
||
if time.monotonic() - self.segment_start >= self.segment_sec:
|
||
self._open_next()
|
||
self.writer.write(frame)
|
||
|
||
def release(self):
|
||
if self.writer is not None:
|
||
self.writer.release()
|
||
|
||
|
||
def prune_stale_tracks(tracked, now_mono):
|
||
stale = [
|
||
tid for tid, (_, ts) in tracked.items() if now_mono - ts > TRACKED_PRUNE_SEC
|
||
]
|
||
for tid in stale:
|
||
del tracked[tid]
|
||
|
||
|
||
def overlay_rect(img, x1, y1, x2, y2, color, alpha=0.65):
|
||
x1, y1 = max(0, x1), max(0, y1)
|
||
x2, y2 = min(img.shape[1], x2), min(img.shape[0], y2)
|
||
if x2 <= x1 or y2 <= y1:
|
||
return
|
||
roi = img[y1:y2, x1:x2]
|
||
patch = np.full_like(roi, color, dtype=np.uint8)
|
||
cv2.addWeighted(patch, alpha, roi, 1 - alpha, 0, roi)
|
||
|
||
|
||
def draw_pill(img, text, x, y, bg, fg=C_TEXT, font_scale=0.45, pad_x=6, pad_y=4):
|
||
font = cv2.FONT_HERSHEY_SIMPLEX
|
||
(tw, th), baseline = cv2.getTextSize(text, font, font_scale, 1)
|
||
x1, y1 = x, y - th - pad_y
|
||
x2, y2 = x + tw + pad_x * 2, y + baseline + pad_y
|
||
cv2.rectangle(img, (x1, y1), (x2, y2), bg, -1)
|
||
cv2.rectangle(img, (x1, y1), (x2, y2), C_BORDER, 1)
|
||
cv2.putText(img, text, (x + pad_x, y), font, font_scale, fg, 1, cv2.LINE_AA)
|
||
|
||
|
||
def draw_elegant_counting_line(img, line_x, h, pulse_remaining=0):
|
||
strength = pulse_remaining / max(LINE_PULSE_FRAMES, 1)
|
||
glow_alpha = 0.12 + 0.18 * strength
|
||
for offset in (14, 9, 5):
|
||
color = tuple(int(c * glow_alpha) for c in C_LINE_GLOW)
|
||
cv2.line(img, (line_x - offset, 0), (line_x - offset, h), color, 1, cv2.LINE_AA)
|
||
cv2.line(img, (line_x + offset, 0), (line_x + offset, h), color, 1, cv2.LINE_AA)
|
||
dash_len, gap = 18, 12
|
||
y = 0
|
||
while y < h:
|
||
y_end = min(y + dash_len, h)
|
||
cv2.line(img, (line_x, y), (line_x, y_end), C_LINE_CORE, 2, cv2.LINE_AA)
|
||
y += dash_len + gap
|
||
cv2.putText(
|
||
img,
|
||
"COUNT LINE",
|
||
(line_x - 46, 24),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.42,
|
||
C_LINE_CORE,
|
||
1,
|
||
cv2.LINE_AA,
|
||
)
|
||
|
||
|
||
def draw_hero_count(img, line_x, h, count, pulse_remaining=0):
|
||
text = str(count)
|
||
font = cv2.FONT_HERSHEY_SIMPLEX
|
||
boost = 0.35 * (pulse_remaining / max(COUNT_PULSE_FRAMES, 1))
|
||
font_scale, thickness = 1.6 + boost, 3
|
||
(tw, th), _ = cv2.getTextSize(text, font, font_scale, thickness)
|
||
pad = 14
|
||
tx, ty = line_x - tw // 2, h // 2 + th // 2
|
||
overlay_rect(
|
||
img, tx - pad, ty - th - pad, tx + tw + pad, ty + pad // 2, C_PANEL, alpha=0.78
|
||
)
|
||
cv2.rectangle(
|
||
img, (tx - pad, ty - th - pad), (tx + tw + pad, ty + pad // 2), C_LINE_CORE, 2
|
||
)
|
||
cv2.putText(img, text, (tx, ty), font, font_scale, C_GREEN, thickness, cv2.LINE_AA)
|
||
|
||
|
||
def draw_hud(
|
||
img, w, batch_num, batch_count, total_ayam, elapsed_sec, rate, camera_id, clock
|
||
):
|
||
bar_h = 52
|
||
overlay_rect(img, 0, 0, w, bar_h, C_PANEL, alpha=0.72)
|
||
cv2.line(img, (0, bar_h), (w, bar_h), C_BORDER, 1)
|
||
cv2.putText(
|
||
img, "BATCH", (16, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA
|
||
)
|
||
batch_label = str(batch_num) if batch_num else "\u2014"
|
||
cv2.putText(
|
||
img,
|
||
batch_label,
|
||
(16, 44),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.9,
|
||
C_ACCENT,
|
||
2,
|
||
cv2.LINE_AA,
|
||
)
|
||
cv2.putText(
|
||
img, "COUNT", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA
|
||
)
|
||
cv2.putText(
|
||
img,
|
||
str(batch_count),
|
||
(100, 44),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.9,
|
||
C_GREEN,
|
||
2,
|
||
cv2.LINE_AA,
|
||
)
|
||
cv2.putText(
|
||
img, "TOTAL", (190, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA
|
||
)
|
||
cv2.putText(
|
||
img,
|
||
str(total_ayam),
|
||
(190, 44),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.7,
|
||
C_TEXT,
|
||
1,
|
||
cv2.LINE_AA,
|
||
)
|
||
cv2.putText(
|
||
img,
|
||
"UPTIME",
|
||
(280, 20),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.45,
|
||
C_MUTED,
|
||
1,
|
||
cv2.LINE_AA,
|
||
)
|
||
cv2.putText(
|
||
img,
|
||
f"{elapsed_sec / 3600:.1f}h",
|
||
(280, 44),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.7,
|
||
C_TEXT,
|
||
1,
|
||
cv2.LINE_AA,
|
||
)
|
||
cv2.putText(
|
||
img, "RATE", (380, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA
|
||
)
|
||
cv2.putText(
|
||
img,
|
||
f"{rate:.1f}/min",
|
||
(380, 44),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.7,
|
||
C_ACCENT,
|
||
1,
|
||
cv2.LINE_AA,
|
||
)
|
||
# cv2.putText(img, clock, (w - 180, 36), cv2.FONT_HERSHEY_SIMPLEX, 0.55, C_TEXT, 1, cv2.LINE_AA)
|
||
cv2.putText(
|
||
img,
|
||
f"CAM {camera_id}",
|
||
(w - 180, 20),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.45,
|
||
C_MUTED,
|
||
1,
|
||
cv2.LINE_AA,
|
||
)
|
||
|
||
|
||
def draw_footer(img, w, h, frame_idx, live_tag):
|
||
bar_h = 28
|
||
overlay_rect(img, 0, h - bar_h, w, h, C_PANEL, alpha=0.55)
|
||
cv2.putText(
|
||
img,
|
||
f"{live_tag} | Frame {frame_idx}",
|
||
(12, h - 9),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.45,
|
||
C_MUTED,
|
||
1,
|
||
cv2.LINE_AA,
|
||
)
|
||
|
||
|
||
def draw_skeleton_bold(img, kpts):
|
||
for (a, b), color in zip(SKELETON, SK_COLORS):
|
||
if a < len(kpts) and b < len(kpts):
|
||
xa, ya = int(kpts[a][0]), int(kpts[a][1])
|
||
xb, yb = int(kpts[b][0]), int(kpts[b][1])
|
||
if xa > 0 and ya > 0 and xb > 0 and yb > 0:
|
||
cv2.line(img, (xa, ya), (xb, yb), color, 3, cv2.LINE_AA)
|
||
for kp in kpts:
|
||
x, y = int(kp[0]), int(kp[1])
|
||
if x > 0 and y > 0:
|
||
cv2.circle(img, (x, y), 6, (255, 255, 255), -1, cv2.LINE_AA)
|
||
cv2.circle(img, (x, y), 6, (40, 40, 40), 2, cv2.LINE_AA)
|
||
|
||
|
||
def draw_popups(img, popups, frame_idx):
|
||
alive = []
|
||
for pop in popups:
|
||
age = frame_idx - pop["born"]
|
||
if age > POPUP_LIFETIME:
|
||
continue
|
||
alive.append(pop)
|
||
fade = 1.0 - age / POPUP_LIFETIME
|
||
y = pop["y"] - int(age * 1.8)
|
||
color = (int(C_GREEN[0] * fade), int(C_GREEN[1] * fade), int(C_GREEN[2] * fade))
|
||
cv2.putText(
|
||
img,
|
||
pop["text"],
|
||
(pop["x"], y),
|
||
cv2.FONT_HERSHEY_SIMPLEX,
|
||
0.7,
|
||
color,
|
||
2,
|
||
cv2.LINE_AA,
|
||
)
|
||
return alive
|
||
|
||
|
||
def draw_batch_banner(img, w, batch_num, pulse_remaining):
|
||
if pulse_remaining <= 0:
|
||
return
|
||
text = f"NEW BATCH {batch_num}"
|
||
font = cv2.FONT_HERSHEY_SIMPLEX
|
||
(tw, th), _ = cv2.getTextSize(text, font, 0.8, 2)
|
||
x1, y1 = w // 2 - tw // 2 - 16, 62
|
||
x2, y2 = w // 2 + tw // 2 + 16, 62 + th + 20
|
||
overlay_rect(img, x1, y1, x2, y2, C_PANEL, alpha=0.7)
|
||
cv2.rectangle(img, (x1, y1), (x2, y2), C_ACCENT, 2)
|
||
cv2.putText(
|
||
img, text, (w // 2 - tw // 2, 62 + th + 4), font, 0.8, C_ACCENT, 2, cv2.LINE_AA
|
||
)
|
||
|
||
|
||
def connect_stream(source, warmup=WARMUP_FRAMES):
|
||
attempts = 0
|
||
while not shutdown_requested:
|
||
cap = open_capture(source)
|
||
if not cap.isOpened():
|
||
attempts += 1
|
||
if MAX_RECONNECT_ATTEMPTS and attempts >= MAX_RECONNECT_ATTEMPTS:
|
||
raise RuntimeError(
|
||
f"Cannot open source after {attempts} attempts: {source}"
|
||
)
|
||
print(f"Cannot open source, retry in {RECONNECT_DELAY_SEC}s...")
|
||
time.sleep(RECONNECT_DELAY_SEC)
|
||
continue
|
||
if warmup > 0 and source.lower().startswith(("rtsp://", "http://")):
|
||
warmup_stream(cap, warmup)
|
||
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||
if not fps or fps <= 1:
|
||
fps = OUTPUT_FPS
|
||
return cap, w, h, fps
|
||
return None, 0, 0, OUTPUT_FPS
|
||
|
||
|
||
# =============================================================================
|
||
# Main loop
|
||
# =============================================================================
|
||
|
||
|
||
def run():
|
||
global shutdown_requested
|
||
|
||
store = BatchStore(
|
||
db_path=DB_PATH,
|
||
state_file=STATE_FILE,
|
||
camera_name=CAMERA_NAME,
|
||
object_label=OBJECT_LABEL,
|
||
cutoff_time=DAILY_CUTOFF_TIME,
|
||
batch_timeout=BATCH_TIMEOUT_SECONDS,
|
||
ignore_batch_label_timeout=IGNORE_BATCH_LABEL_TIMEOUT,
|
||
min_object_per_batch=MIN_OBJECT_PER_BATCH,
|
||
min_duration_per_batch=MIN_DURATION_PER_BATCH,
|
||
logger=lambda msg: print(f"[{now_str()}] {msg}"),
|
||
)
|
||
store.start_cutoff_watcher()
|
||
|
||
cross_logger = None
|
||
if EXPORT_CSV:
|
||
cross_logger = CsvLogger(
|
||
CROSS_CSV, ["batch", "frame", "timestamp", "chicken_id"]
|
||
)
|
||
|
||
model = RKNNYOLO(
|
||
model_path=MODEL_PATH,
|
||
core_mask=CORE_MASK,
|
||
imgsz=IMGSZ,
|
||
conf=CONF,
|
||
num_classes=NUM_CLASSES,
|
||
score_sigmoid=SCORE_SIGMOID,
|
||
)
|
||
|
||
CLASS_IDS = {
|
||
os.getenv("CLASS_AYAM", "ayam"): 0,
|
||
os.getenv("CLASS_TALENAN", "talenan"): 1,
|
||
}
|
||
ayam_cls = CLASS_IDS[CLASS_AYAM]
|
||
talenan_cls = CLASS_IDS[CLASS_TALENAN]
|
||
|
||
ayam_tracker = ByteTracker(
|
||
track_high_thresh=TRACK_HIGH_THRESH,
|
||
track_low_thresh=TRACK_LOW_THRESH,
|
||
match_thresh=TRACK_MATCH_THRESH,
|
||
track_buffer=TRACK_BUFFER,
|
||
min_hits=TRACK_MIN_HITS,
|
||
)
|
||
talenan_tracker = ByteTracker(
|
||
track_high_thresh=TRACK_HIGH_THRESH,
|
||
track_low_thresh=TRACK_LOW_THRESH,
|
||
match_thresh=TRACK_MATCH_THRESH,
|
||
track_buffer=TRACK_BUFFER,
|
||
min_hits=TRACK_MIN_HITS,
|
||
)
|
||
|
||
ayam_line_crossed = set()
|
||
talenan_line_crossed = set()
|
||
|
||
ayam_cross_flash = {}
|
||
talenan_cross_flash = {}
|
||
line_pulse = count_pulse = batch_pulse = 0
|
||
popups = []
|
||
|
||
session_start = time.time()
|
||
frame_idx = 0
|
||
video_writer = None
|
||
|
||
cap, w, h, fps = connect_stream(SOURCE)
|
||
if cap is None:
|
||
store.shutdown()
|
||
model.release()
|
||
return
|
||
|
||
line_x = resolve_line_x(w)
|
||
print(
|
||
f"RKNN+ByteTrack counter | {w}x{h} @ {fps}fps | line x={line_x} | cross={CROSS_DIRECTION}"
|
||
)
|
||
print(f"Model: {MODEL_PATH} | imgsz={IMGSZ} | core_mask={CORE_MASK}")
|
||
print(
|
||
f"ByteTrack: high_thresh={TRACK_HIGH_THRESH} low_thresh={TRACK_LOW_THRESH} "
|
||
f"match_thresh={TRACK_MATCH_THRESH} buffer={TRACK_BUFFER}"
|
||
)
|
||
print(f"DB: {DB_PATH}")
|
||
print(f"State: {STATE_FILE}")
|
||
|
||
if RECORD_VIDEO:
|
||
video_writer = VideoSegmentWriter(OUTPUT_DIR, w, h, fps, VIDEO_SEGMENT_SEC)
|
||
|
||
reconnect_count = 0
|
||
|
||
while not shutdown_requested:
|
||
ret, frame = cap.read()
|
||
if not ret:
|
||
if not IS_LIVE:
|
||
break
|
||
reconnect_count += 1
|
||
print(
|
||
f"Stream dropped (attempt {reconnect_count}), reconnecting in {RECONNECT_DELAY_SEC}s..."
|
||
)
|
||
cap.release()
|
||
time.sleep(RECONNECT_DELAY_SEC)
|
||
cap, w, h, fps = connect_stream(SOURCE)
|
||
if cap is None:
|
||
break
|
||
line_x = resolve_line_x(w)
|
||
continue
|
||
|
||
now = time.time()
|
||
elapsed = now - session_start
|
||
mono = time.monotonic()
|
||
ayam_crossed_frame = batch_closed_frame = batch_started_frame = False
|
||
|
||
detections = model(frame)
|
||
|
||
if detections:
|
||
ayam_boxes_xyxy = []
|
||
ayam_scores = []
|
||
ayam_kpts_list = []
|
||
ayam_cx_list = []
|
||
|
||
talenan_boxes_xyxy = []
|
||
talenan_scores = []
|
||
talenan_kpts_list = []
|
||
talenan_cx_list = []
|
||
|
||
for det in detections:
|
||
bbox = det["bbox"]
|
||
score = det["score"]
|
||
cls_id = det["cls"]
|
||
kpts = det["keypoints"]
|
||
cx = (bbox[0] + bbox[2]) / 2.0
|
||
|
||
if cls_id == talenan_cls:
|
||
talenan_boxes_xyxy.append(bbox)
|
||
talenan_scores.append(score)
|
||
talenan_kpts_list.append(kpts)
|
||
talenan_cx_list.append(cx)
|
||
elif cls_id == ayam_cls:
|
||
ayam_boxes_xyxy.append(bbox)
|
||
ayam_scores.append(score)
|
||
ayam_kpts_list.append(kpts)
|
||
ayam_cx_list.append(cx)
|
||
|
||
ayam_boxes_xyxy = np.array(ayam_boxes_xyxy, dtype=np.float32)
|
||
ayam_scores = np.array(ayam_scores, dtype=np.float32)
|
||
talenan_boxes_xyxy = np.array(talenan_boxes_xyxy, dtype=np.float32)
|
||
talenan_scores = np.array(talenan_scores, dtype=np.float32)
|
||
|
||
ayam_track_map, ayam_det_to_track, ayam_lost_map = ayam_tracker.update(
|
||
ayam_boxes_xyxy, ayam_scores
|
||
)
|
||
talenan_track_map, talenan_det_to_track, talenan_lost_map = (
|
||
talenan_tracker.update(talenan_boxes_xyxy, talenan_scores)
|
||
)
|
||
|
||
# Process talenan crossings
|
||
for di in range(len(talenan_boxes_xyxy)):
|
||
tid = talenan_det_to_track.get(di)
|
||
if tid is None:
|
||
continue
|
||
cx = talenan_cx_list[di]
|
||
bbox = talenan_boxes_xyxy[di]
|
||
|
||
if tid in talenan_tracked:
|
||
prev_cx = talenan_tracked[tid][0]
|
||
if (
|
||
crossed_line(prev_cx, cx, line_x)
|
||
and tid not in talenan_line_crossed
|
||
):
|
||
talenan_line_crossed.add(tid)
|
||
if store.record_talenan_crossing(tid):
|
||
batch_closed_frame = True
|
||
talenan_cross_flash[tid] = CROSS_FLASH_FRAMES
|
||
popups.append(
|
||
{
|
||
"x": int(cx) - 20,
|
||
"y": int((bbox[1] + bbox[3]) / 2),
|
||
"born": frame_idx,
|
||
"text": "BATCH CLOSED",
|
||
}
|
||
)
|
||
talenan_tracked[tid] = (cx, mono)
|
||
|
||
# Process ayam crossings (including lost tracks for line-cross continuity)
|
||
for di in range(len(ayam_boxes_xyxy)):
|
||
tid = ayam_det_to_track.get(di)
|
||
if tid is None:
|
||
continue
|
||
cx = ayam_cx_list[di]
|
||
|
||
if tid in ayam_tracked:
|
||
prev_cx = ayam_tracked[tid][0]
|
||
if (
|
||
crossed_line(prev_cx, cx, line_x)
|
||
and tid not in ayam_line_crossed
|
||
):
|
||
ayam_line_crossed.add(tid)
|
||
_, started_new = store.record_ayam_crossing(tid)
|
||
if cross_logger:
|
||
cross_logger.write_row(
|
||
[
|
||
store.current_batch_number,
|
||
frame_idx,
|
||
datetime.now().isoformat(),
|
||
tid,
|
||
]
|
||
)
|
||
ayam_crossed_frame = True
|
||
if started_new:
|
||
batch_started_frame = True
|
||
ayam_cross_flash[tid] = CROSS_FLASH_FRAMES
|
||
popups.append(
|
||
{
|
||
"x": int(cx) - 12,
|
||
"y": int(
|
||
(ayam_boxes_xyxy[di][1] + ayam_boxes_xyxy[di][3])
|
||
/ 2
|
||
),
|
||
"born": frame_idx,
|
||
"text": "+1",
|
||
}
|
||
)
|
||
ayam_tracked[tid] = (cx, mono)
|
||
|
||
# Also track lost tracks for line-crossing continuity
|
||
for tid, cx in ayam_lost_map.items():
|
||
if tid not in ayam_tracked:
|
||
ayam_tracked[tid] = (cx, mono)
|
||
|
||
# Draw talenan
|
||
for di in range(len(talenan_boxes_xyxy)):
|
||
tid = talenan_det_to_track.get(di)
|
||
if tid is None:
|
||
continue
|
||
bbox = talenan_boxes_xyxy[di]
|
||
x1, y1, x2, y2 = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
|
||
flash = talenan_cross_flash.get(tid, 0)
|
||
color = C_GREEN if flash > 0 else C_TALENAN_BOX
|
||
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)
|
||
draw_pill(frame, f"TALENAN {tid}", x1, y1 - 4, color)
|
||
|
||
# Draw ayam
|
||
for di in range(len(ayam_boxes_xyxy)):
|
||
tid = ayam_det_to_track.get(di)
|
||
if tid is None:
|
||
continue
|
||
bbox = ayam_boxes_xyxy[di]
|
||
x1, y1, x2, y2 = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])
|
||
flash = ayam_cross_flash.get(tid, 0)
|
||
color = C_GREEN if flash > 0 else C_AYAM_BOX
|
||
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)
|
||
draw_pill(frame, f"ID {tid}", x1, y1 - 4, color)
|
||
kpts = ayam_kpts_list[di] if di < len(ayam_kpts_list) else None
|
||
if kpts is not None:
|
||
draw_skeleton_bold(frame, kpts)
|
||
|
||
if ayam_crossed_frame:
|
||
line_pulse = LINE_PULSE_FRAMES
|
||
count_pulse = COUNT_PULSE_FRAMES
|
||
if batch_closed_frame:
|
||
line_pulse = LINE_PULSE_FRAMES
|
||
if batch_started_frame:
|
||
batch_pulse = BATCH_PULSE_FRAMES
|
||
|
||
batch_num = store.current_batch_number or 0
|
||
batch_count = store.current_batch_count
|
||
display_total = store.display_total()
|
||
rate = (display_total / elapsed * 60) if elapsed > 0 else 0.0
|
||
|
||
draw_elegant_counting_line(frame, line_x, h, line_pulse)
|
||
draw_hero_count(frame, line_x, h, batch_count, count_pulse)
|
||
draw_hud(
|
||
frame,
|
||
w,
|
||
batch_num,
|
||
batch_count,
|
||
display_total,
|
||
elapsed,
|
||
rate,
|
||
CAMERA_NAME,
|
||
now_str(),
|
||
)
|
||
draw_batch_banner(frame, w, batch_num, batch_pulse)
|
||
draw_footer(
|
||
frame, w, h, frame_idx, "LIVE-RKNN-BT" if IS_LIVE else "FILE-RKNN-BT"
|
||
)
|
||
popups = draw_popups(frame, popups, frame_idx)
|
||
|
||
for flash_store in (ayam_cross_flash, talenan_cross_flash):
|
||
for tid in list(flash_store):
|
||
flash_store[tid] -= 1
|
||
if flash_store[tid] <= 0:
|
||
del flash_store[tid]
|
||
line_pulse = max(0, line_pulse - 1)
|
||
count_pulse = max(0, count_pulse - 1)
|
||
batch_pulse = max(0, batch_pulse - 1)
|
||
|
||
if video_writer is not None:
|
||
video_writer.write(frame)
|
||
|
||
if LIVE_STREAM_ENABLED and frame_idx % LIVE_STREAM_EVERY_N == 0:
|
||
try:
|
||
Path(LIVE_STREAM_FRAME_PATH).parent.mkdir(parents=True, exist_ok=True)
|
||
_, jpeg = cv2.imencode(
|
||
".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, LIVE_STREAM_QUALITY]
|
||
)
|
||
with open(LIVE_STREAM_FRAME_PATH, "wb") as f:
|
||
f.write(jpeg.tobytes())
|
||
except Exception:
|
||
pass
|
||
|
||
frame_idx += 1
|
||
if frame_idx % FLUSH_EVERY_N_FRAMES == 0:
|
||
print(
|
||
f"[{now_str()}] Frame {frame_idx} | Batch {batch_num}: {batch_count} "
|
||
f"| Total: {display_total} | Uptime {elapsed / 3600:.2f}h"
|
||
)
|
||
prune_stale_tracks(ayam_tracked, mono)
|
||
prune_stale_tracks(talenan_tracked, mono)
|
||
|
||
cap.release()
|
||
if video_writer is not None:
|
||
video_writer.release()
|
||
if cross_logger:
|
||
cross_logger.close()
|
||
model.release()
|
||
store.shutdown()
|
||
|
||
print("\n=== Batch Summary (SQLite) ===")
|
||
print(f"Database: {DB_PATH}")
|
||
|
||
|
||
ayam_tracked = {}
|
||
talenan_tracked = {}
|
||
|
||
|
||
if __name__ == "__main__":
|
||
run()
|