323 lines
46 KiB
Plaintext
323 lines
46 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "37fe6724-f5fe-412a-ab9a-6a1df878c308",
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"metadata": {},
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"source": [
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"## Import Library"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "11b66fe3-8d38-4bf9-b9c5-f8bd3213bd55",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Selesai Import Library\n"
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]
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}
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],
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"source": [
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"import cv2 # Import library OpenCV untuk pengolahan citra dan video\n",
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"import imutils # Import library imutils untuk mempermudah manipulasi citra\n",
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"import numpy as np # Import library numpy untuk operasi numerik\n",
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"from ultralytics import YOLO # Import class YOLO dari library ultralytics untuk deteksi objek\n",
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"from collections import defaultdict # Import class defaultdict dari library collections untuk struktur data default dictionary\n",
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"\n",
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"print(\"Selesai Import Library\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "243e5a8f-46c2-4fe1-b174-52a46f0a26ee",
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"metadata": {},
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"source": [
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"## Deklarasi Variable"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "bbeb303b-5683-44cc-a924-0f2481d75528",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"selesai deklarasi variable\n"
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]
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}
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],
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"source": [
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"color = (0, 255, 0) # Warna hijau untuk penggambaran objek dan garis\n",
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"color_red = (0, 0, 255) # Warna merah untuk teks dan garis\n",
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"thickness = 2 # Ketebalan garis untuk penggambaran objek dan garis\n",
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"\n",
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"font = cv2.FONT_HERSHEY_SIMPLEX # Jenis font untuk teks\n",
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"font_scale = 0.5 # Skala font untuk teks\n",
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"\n",
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"# Path video yang akan diproses\n",
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"video_path = \"video/video2.mp4\"\n",
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"model_path = \"models/yolov8n.pt\"\n",
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"\n",
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"# Buka video\n",
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"cap = cv2.VideoCapture(video_path)\n",
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"# Inisialisasi model YOLO dengan file weight yang telah dilatih sebelumnya\n",
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"model = YOLO(model_path)\n",
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"\n",
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"# Ukuran frame video\n",
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"width = 1280\n",
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"height = 720\n",
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"\n",
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"# Inisialisasi objek untuk menyimpan video hasil pemrosesan\n",
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"fourcc = cv2.VideoWriter_fourcc(*'XVID')\n",
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"writer = cv2.VideoWriter(\"video.avi\", fourcc, 20.0, (width, height))\n",
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"\n",
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"# Id objek kendaraan yang ingin dilacak berdasarkan kelas di file coco-classes.txt\n",
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"vehicle_ids = [2, 3, 5, 7]\n",
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"# Dictionary untuk menyimpan sejarah pergerakan setiap kendaraan yang terdeteksi\n",
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"track_history = defaultdict(lambda: [])\n",
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"\n",
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"up = {} # Dictionary untuk kendaraan yang melewati garis atas\n",
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"down = {} # Dictionary untuk kendaraan yang melewati garis bawah\n",
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"threshold = 400 # Ambang batas garis pemisah kendaraan\n",
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"\n",
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"print(\"selesai deklarasi variable\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "00596875-56e1-445a-bd8b-b2b3a73a411a",
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"metadata": {},
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"source": [
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"### Fungsi untuk mengambil titik tengah dari bounding box objek "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "ffcffbd1-ad9b-4908-8930-bea2ba6b6ecb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Selesai membuat fungsi\n"
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]
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}
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],
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"source": [
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"def pega_centro(x, y, w, h):\n",
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" x1 = int(w / 2)\n",
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" y1 = int(h / 2)\n",
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" cx = x + x1\n",
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" cy = y + y1\n",
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" return cx, cy\n",
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"\n",
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"print(\"Selesai membuat fungsi\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9f2e6c12-a70b-49f2-9083-a9c85b04e842",
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"metadata": {},
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"source": [
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"### Background subtraction menggunakan MOG2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "4b0f68b8-9216-49e6-892e-bbf2282d73b3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"selesai\n"
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]
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}
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],
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"source": [
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"subtracao = cv2.createBackgroundSubtractorMOG2()\n",
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"print(\"selesai\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0e9ea925-a617-45d3-b50c-273f4ee0163b",
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"metadata": {},
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"source": [
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"## Proses Video "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "705c59f4-fba5-498d-9e51-d002a0dc3226",
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"metadata": {},
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"outputs": [
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[11], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m frame_bw \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mcvtColor(frame, cv2\u001b[38;5;241m.\u001b[39mCOLOR_BGR2GRAY) \u001b[38;5;66;03m# Konversi ke citra grayscale untuk mode black and white\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# Deteksi objek menggunakan model YOLO\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframe_color\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 16\u001b[0m bboxes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(results\u001b[38;5;241m.\u001b[39mboxes\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mtolist(), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mint\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;66;03m# Koordinat bounding box objek yang terdeteksi\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis\u001b[39;00m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/model.py:469\u001b[0m, in \u001b[0;36mModel.track\u001b[0;34m(self, source, stream, persist, **kwargs)\u001b[0m\n\u001b[1;32m 467\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconf\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconf\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0.1\u001b[39m \u001b[38;5;66;03m# ByteTrack-based method needs low confidence predictions as input\u001b[39;00m\n\u001b[1;32m 468\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrack\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 469\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/model.py:430\u001b[0m, in \u001b[0;36mModel.predict\u001b[0;34m(self, source, stream, predictor, **kwargs)\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m prompts \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_prompts\u001b[39m\u001b[38;5;124m\"\u001b[39m): \u001b[38;5;66;03m# for SAM-type models\u001b[39;00m\n\u001b[1;32m 429\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor\u001b[38;5;241m.\u001b[39mset_prompts(prompts)\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor\u001b[38;5;241m.\u001b[39mpredict_cli(source\u001b[38;5;241m=\u001b[39msource) \u001b[38;5;28;01mif\u001b[39;00m is_cli \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredictor\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/predictor.py:204\u001b[0m, in \u001b[0;36mBasePredictor.__call__\u001b[0;34m(self, source, model, stream, *args, **kwargs)\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream_inference(source, model, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream_inference\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/utils/_contextlib.py:35\u001b[0m, in \u001b[0;36m_wrap_generator.<locals>.generator_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# Issuing `None` to a generator fires it up\u001b[39;00m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m---> 35\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# Forward the response to our caller and get its next request\u001b[39;00m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/predictor.py:283\u001b[0m, in \u001b[0;36mBasePredictor.stream_inference\u001b[0;34m(self, source, model, *args, **kwargs)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;66;03m# Inference\u001b[39;00m\n\u001b[1;32m 282\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m profilers[\u001b[38;5;241m1\u001b[39m]:\n\u001b[0;32m--> 283\u001b[0m preds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minference\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39membed:\n\u001b[1;32m 285\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m [preds] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(preds, torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;28;01melse\u001b[39;00m preds \u001b[38;5;66;03m# yield embedding tensors\u001b[39;00m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/engine/predictor.py:140\u001b[0m, in \u001b[0;36mBasePredictor.inference\u001b[0;34m(self, im, *args, **kwargs)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Runs inference on a given image using the specified model and arguments.\"\"\"\u001b[39;00m\n\u001b[1;32m 135\u001b[0m visualize \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 136\u001b[0m increment_path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_dir \u001b[38;5;241m/\u001b[39m Path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m])\u001b[38;5;241m.\u001b[39mstem, mkdir\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mvisualize \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msource_type\u001b[38;5;241m.\u001b[39mtensor)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 139\u001b[0m )\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maugment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maugment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvisualize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvisualize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/autobackend.py:384\u001b[0m, in \u001b[0;36mAutoBackend.forward\u001b[0;34m(self, im, augment, visualize, embed)\u001b[0m\n\u001b[1;32m 381\u001b[0m im \u001b[38;5;241m=\u001b[39m im\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# torch BCHW to numpy BHWC shape(1,320,192,3)\u001b[39;00m\n\u001b[1;32m 383\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpt \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnn_module: \u001b[38;5;66;03m# PyTorch\u001b[39;00m\n\u001b[0;32m--> 384\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maugment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maugment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvisualize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvisualize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mjit: \u001b[38;5;66;03m# TorchScript\u001b[39;00m\n\u001b[1;32m 386\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel(im)\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/tasks.py:83\u001b[0m, in \u001b[0;36mBaseModel.forward\u001b[0;34m(self, x, *args, **kwargs)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mdict\u001b[39m): \u001b[38;5;66;03m# for cases of training and validating while training.\u001b[39;00m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/tasks.py:101\u001b[0m, in \u001b[0;36mBaseModel.predict\u001b[0;34m(self, x, profile, visualize, augment, embed)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m augment:\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_predict_augment(x)\n\u001b[0;32m--> 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_predict_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprofile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvisualize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membed\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/tasks.py:122\u001b[0m, in \u001b[0;36mBaseModel._predict_once\u001b[0;34m(self, x, profile, visualize, embed)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m profile:\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_profile_one_layer(m, x, dt)\n\u001b[0;32m--> 122\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# run\u001b[39;00m\n\u001b[1;32m 123\u001b[0m y\u001b[38;5;241m.\u001b[39mappend(x \u001b[38;5;28;01mif\u001b[39;00m m\u001b[38;5;241m.\u001b[39mi \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;66;03m# save output\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m visualize:\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/modules/block.py:171\u001b[0m, in \u001b[0;36mSPPF.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 169\u001b[0m y1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mm(x)\n\u001b[1;32m 170\u001b[0m y2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mm(y1)\n\u001b[0;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcv2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mm\u001b[49m\u001b[43m(\u001b[49m\u001b[43my2\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/ultralytics/nn/modules/conv.py:54\u001b[0m, in \u001b[0;36mConv.forward_fuse\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward_fuse\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m 53\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Perform transposed convolution of 2D data.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mact(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 460\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/Desktop/Python/YOLOv8-Projects/Traffic Analysis Projects/Highway Car Counter/env/lib/python3.10/site-packages/torch/nn/modules/conv.py:456\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m 454\u001b[0m weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m 455\u001b[0m _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 456\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
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]
|
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}
|
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],
|
||
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"source": [
|
||
|
|
"# Loop utama untuk membaca setiap frame dari video\n",
|
||
|
|
"while True:\n",
|
||
|
|
" ret, frame = cap.read() # Membaca frame dari video\n",
|
||
|
|
" if ret == False: # Keluar dari loop jika tidak ada frame yang dapat dibaca\n",
|
||
|
|
" break\n",
|
||
|
|
" \n",
|
||
|
|
" try:\n",
|
||
|
|
" frame = imutils.resize(frame, width = 1280, height = 720) # ubah frame menjadi tinggi 720 x lebar 1280\n",
|
||
|
|
" frame_color = frame.copy() # Salin frame ke mode warna untuk pengolahan dan penggambaran\n",
|
||
|
|
" frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale\n",
|
||
|
|
" frame_gray = cv2.cvtColor(frame_gray, cv2.COLOR_GRAY2BGR) # Konversi kembali ke citra BGR untuk tampilan grayscale\n",
|
||
|
|
" frame_bw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi ke citra grayscale untuk mode black and white\n",
|
||
|
|
"\n",
|
||
|
|
" # Deteksi objek menggunakan model YOLO\n",
|
||
|
|
" results = model.track(frame_color, persist=True, verbose=False)[0]\n",
|
||
|
|
" bboxes = np.array(results.boxes.data.tolist(), dtype=\"int\") # Koordinat bounding box objek yang terdeteksi\n",
|
||
|
|
"\n",
|
||
|
|
" # Gambar garis pembatas untuk menghitung jumlah kendaraan yang melewati garis\n",
|
||
|
|
" cv2.line(frame_color, (0, threshold), (1280, threshold), color, thickness)\n",
|
||
|
|
" cv2.putText(frame_color, \"Pembatas Jalan\", (620, 445), font, 0.7, color_red, thickness)\n",
|
||
|
|
"\n",
|
||
|
|
" # Loop untuk setiap objek yang terdeteksi\n",
|
||
|
|
" for box in bboxes:\n",
|
||
|
|
" x1, y1, x2, y2, track_id, score, class_id = box # Ambil koordinat dan informasi lainnya\n",
|
||
|
|
" cx = int((x1 + x2) / 2) # Hitung koordinat x pusat objek\n",
|
||
|
|
" cy = int((y1 + y2) / 2) # Hitung koordinat y pusat objek\n",
|
||
|
|
" if class_id in vehicle_ids: # Periksa apakah objek merupakan kendaraan yang ingin dilacak\n",
|
||
|
|
" class_name = results.names[int(class_id)].upper() # Dapatkan nama kelas objek\n",
|
||
|
|
"\n",
|
||
|
|
" track = track_history[track_id] # Ambil sejarah pergerakan objek berdasarkan ID\n",
|
||
|
|
" track.append((cx, cy)) # Tambahkan koordinat pusat objek ke dalam sejarah pergerakan\n",
|
||
|
|
" if len(track) > 20: # Batasi panjang sejarah pergerakan agar tidak terlalu panjang\n",
|
||
|
|
" track.pop(0) # Hapus elemen pertama jika sejarah sudah melebihi batas\n",
|
||
|
|
"\n",
|
||
|
|
" points = np.hstack(track).astype(\"int32\").reshape(-1, 1, 2) # Konversi sejarah pergerakan ke format yang sesuai untuk penggambaran\n",
|
||
|
|
" cv2.polylines(frame_color, [points], isClosed=False, color=color, thickness=thickness) # Gambar garis yang merepresentasikan sejarah pergerakan\n",
|
||
|
|
" cv2.rectangle(frame_color, (x1, y1), (x2, y2), color, thickness) # Gambar bounding box objek\n",
|
||
|
|
" text = \"ID: {} {}\".format(track_id, class_name) # Buat teks ID objek dan nama kelasnya\n",
|
||
|
|
" cv2.putText(frame_color, text, (x1, y1 - 5), font, font_scale, color, thickness) # Tampilkan teks di atas objek\n",
|
||
|
|
"\n",
|
||
|
|
" if cy > threshold - 5 and cy < threshold + 5 and cx < 670: # Periksa apakah objek melewati garis atas\n",
|
||
|
|
" down[track_id] = x1, y1, x2, y2 # Simpan informasi objek yang melewati garis atas\n",
|
||
|
|
"\n",
|
||
|
|
" if cy > threshold - 5 and cy < threshold + 5 and cx > 670: # Periksa apakah objek melewati garis bawah\n",
|
||
|
|
" up[track_id] = x1, y1, x2, y2 # Simpan informasi objek yang melewati garis bawah\n",
|
||
|
|
"\n",
|
||
|
|
" up_text = \"Kanan:{}\".format(len(list(up.keys()))) # Buat teks jumlah kendaraan yang melewati garis atas\n",
|
||
|
|
" down_text = \"Kiri:{}\".format(len(list(down.keys()))) # Buat teks jumlah kendaraan yang melewati garis bawah\n",
|
||
|
|
"\n",
|
||
|
|
" cv2.putText(frame_color, up_text, (1150, threshold - 5), font, 0.8, color_red, thickness) # Tampilkan teks jumlah kendaraan yang melewati garis atas\n",
|
||
|
|
" cv2.putText(frame_color, down_text, (0, threshold - 5), font, 0.8, color_red, thickness) # Tampilkan teks jumlah kendaraan yang melewati garis bawah\n",
|
||
|
|
"\n",
|
||
|
|
" # Background subtraction dan deteksi kontur\n",
|
||
|
|
" grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Konversi frame ke citra grayscale\n",
|
||
|
|
" blur = cv2.GaussianBlur(grey, (3, 3), 5) # Reduksi noise menggunakan Gaussian Blur\n",
|
||
|
|
" img_sub = subtracao.apply(blur) # Background subtraction\n",
|
||
|
|
" dilat = cv2.dilate(img_sub, np.ones((5, 5))) # Dilasi untuk meningkatkan ketebalan objek\n",
|
||
|
|
" kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) # Kernel untuk operasi morfologi\n",
|
||
|
|
" dilatada = cv2.morphologyEx(dilat, cv2.MORPH_CLOSE, kernel) # Operasi closing untuk mengisi lubang kecil pada objek\n",
|
||
|
|
" dilatada = cv2.morphologyEx(dilatada, cv2.MORPH_CLOSE, kernel) # Operasi closing tambahan\n",
|
||
|
|
" contorno, h = cv2.findContours(dilatada, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Deteksi kontur objek\n",
|
||
|
|
"\n",
|
||
|
|
" writer.write(frame_color) # Menyimpan frame hasil pemrosesan\n",
|
||
|
|
" # Menampilkan gambar\n",
|
||
|
|
" cv2.imshow(\"Warna\", frame_color) # Tampilkan mode warna\n",
|
||
|
|
" cv2.imshow(\"Grayscale\", frame_gray) # Tampilkan mode grayscale\n",
|
||
|
|
" cv2.imshow(\"Detectar\", dilatada) # Tampilkan mode Detectar dilatada\n",
|
||
|
|
" if cv2.waitKey(10) & 0xFF == ord(\"q\"): # Keluar saat tombol q ditekan\n",
|
||
|
|
" break\n",
|
||
|
|
"\n",
|
||
|
|
" except Exception as e:\n",
|
||
|
|
" print(\"Terjadi kesalahan:\", str(e)) # Tangkap dan tampilkan kesalahan yang terjadi\n",
|
||
|
|
" continue # Lanjutkan ke iterasi berikutnya\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "ae345f06-2af7-4b93-b833-a14cc20f7d64",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Menutup Window OpenCV"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "15c70b25-1b92-43d8-9167-ebb88b2a8df7",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"cap.release() # Bebaskan sumber daya setelah selesai pemrosesan video\n",
|
||
|
|
"writer.release() # Tutup objek writer\n",
|
||
|
|
"cv2.destroyAllWindows() # Tutup semua jendela yang dibuka oleh OpenCV"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.10.10"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|