yolo.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "import os\n",
                "from roboflow import Roboflow\n",
                "from ultralytics import YOLO\n",
                "from settings import *"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "test_number = 2 #The number of the test about to be performed!!!"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Datasets/License-Plate-Dataset\n",
                        "Results/Results_Output_Actual/rps_test2\n",
                        "Results/Results_Output_Actual/rps_test1/weights/best.pt\n"
                    ]
                }
            ],
            "source": [
                "train_path = f'{YOLO_DATASETS_DIR}/License-Plate-Dataset'\n",
                "result_path = f'{YOLO_RESULTS_DIR}/Results_Output_Actual/rps_test{test_number}'\n",
                "weight_path = (f'{YOLO_RESULTS_DIR}/Results_Output_Actual/rps_test{test_number-1}/weights/best.pt') if test_number-1 > 0 else 'yolov8n.pt'\n",
                "test_images_path = f'{train_path}/test/images'\n",
                "\n",
                "print(train_path)\n",
                "print(result_path)\n",
                "print(weight_path)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "//////////////////////////////////////////////////////////////////MODEL SETUP///////////////////////////////////////////////////////////////////////////"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "ename": "FileNotFoundError",
                    "evalue": "[Errno 2] No such file or directory: 'Results/Results_Output_Actual/rps_test1/weights/best.pt'",
                    "output_type": "error",
                    "traceback": [
                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
                        "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
                        "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mYOLO\u001b[49m\u001b[43m(\u001b[49m\u001b[43mweight_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m data_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrain_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/data.yaml\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      3\u001b[0m results \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mtrain(data\u001b[38;5;241m=\u001b[39mdata_path, epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m50\u001b[39m, batch\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, cache \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m, project \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mResults_Output_Actual\u001b[39m\u001b[38;5;124m'\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrps_test\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtest_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
                        "File \u001b[0;32m~/Downloads/Final-Project/.venv/lib/python3.11/site-packages/ultralytics/models/yolo/model.py:23\u001b[0m, in \u001b[0;36mYOLO.__init__\u001b[0;34m(self, model, task, verbose)\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m \u001b[38;5;241m=\u001b[39m new_instance\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;66;03m# Continue with default YOLO initialization\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m)\u001b[49m\n",
                        "File \u001b[0;32m~/Downloads/Final-Project/.venv/lib/python3.11/site-packages/ultralytics/engine/model.py:142\u001b[0m, in \u001b[0;36mModel.__init__\u001b[0;34m(self, model, task, verbose)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new(model, task\u001b[38;5;241m=\u001b[39mtask, verbose\u001b[38;5;241m=\u001b[39mverbose)\n\u001b[1;32m    141\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 142\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_name \u001b[38;5;241m=\u001b[39m model\n",
                        "File \u001b[0;32m~/Downloads/Final-Project/.venv/lib/python3.11/site-packages/ultralytics/engine/model.py:229\u001b[0m, in \u001b[0;36mModel._load\u001b[0;34m(self, weights, task)\u001b[0m\n\u001b[1;32m    227\u001b[0m suffix \u001b[38;5;241m=\u001b[39m Path(weights)\u001b[38;5;241m.\u001b[39msuffix\n\u001b[1;32m    228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m suffix \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.pt\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 229\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mckpt \u001b[38;5;241m=\u001b[39m \u001b[43mattempt_load_one_weight\u001b[49m\u001b[43m(\u001b[49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    230\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39margs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtask\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    231\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moverrides \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39margs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_ckpt_args(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39margs)\n",
                        "File \u001b[0;32m~/Downloads/Final-Project/.venv/lib/python3.11/site-packages/ultralytics/nn/tasks.py:790\u001b[0m, in \u001b[0;36mattempt_load_one_weight\u001b[0;34m(weight, device, inplace, fuse)\u001b[0m\n\u001b[1;32m    788\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mattempt_load_one_weight\u001b[39m(weight, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, fuse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m    789\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Loads a single model weights.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 790\u001b[0m     ckpt, weight \u001b[38;5;241m=\u001b[39m \u001b[43mtorch_safe_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# load ckpt\u001b[39;00m\n\u001b[1;32m    791\u001b[0m     args \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mDEFAULT_CFG_DICT, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(ckpt\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_args\u001b[39m\u001b[38;5;124m\"\u001b[39m, {}))}  \u001b[38;5;66;03m# combine model and default args, preferring model args\u001b[39;00m\n\u001b[1;32m    792\u001b[0m     model \u001b[38;5;241m=\u001b[39m (ckpt\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mema\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m ckpt[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;241m.\u001b[39mto(device)\u001b[38;5;241m.\u001b[39mfloat()  \u001b[38;5;66;03m# FP32 model\u001b[39;00m\n",
                        "File \u001b[0;32m~/Downloads/Final-Project/.venv/lib/python3.11/site-packages/ultralytics/nn/tasks.py:716\u001b[0m, in \u001b[0;36mtorch_safe_load\u001b[0;34m(weight)\u001b[0m\n\u001b[1;32m    708\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    709\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m temporary_modules(\n\u001b[1;32m    710\u001b[0m         {\n\u001b[1;32m    711\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124multralytics.yolo.utils\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124multralytics.utils\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    714\u001b[0m         }\n\u001b[1;32m    715\u001b[0m     ):  \u001b[38;5;66;03m# for legacy 8.0 Classify and Pose models\u001b[39;00m\n\u001b[0;32m--> 716\u001b[0m         ckpt \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcpu\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    718\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mModuleNotFoundError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# e.name is missing module name\u001b[39;00m\n\u001b[1;32m    719\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodels\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
                        "File \u001b[0;32m~/Downloads/Final-Project/.venv/lib/python3.11/site-packages/torch/serialization.py:998\u001b[0m, in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[1;32m    995\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m pickle_load_args\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    996\u001b[0m     pickle_load_args[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m--> 998\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43m_open_file_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m opened_file:\n\u001b[1;32m    999\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m _is_zipfile(opened_file):\n\u001b[1;32m   1000\u001b[0m         \u001b[38;5;66;03m# The zipfile reader is going to advance the current file position.\u001b[39;00m\n\u001b[1;32m   1001\u001b[0m         \u001b[38;5;66;03m# If we want to actually tail call to torch.jit.load, we need to\u001b[39;00m\n\u001b[1;32m   1002\u001b[0m         \u001b[38;5;66;03m# reset back to the original position.\u001b[39;00m\n\u001b[1;32m   1003\u001b[0m         orig_position \u001b[38;5;241m=\u001b[39m opened_file\u001b[38;5;241m.\u001b[39mtell()\n",
                        "File \u001b[0;32m~/Downloads/Final-Project/.venv/lib/python3.11/site-packages/torch/serialization.py:445\u001b[0m, in \u001b[0;36m_open_file_like\u001b[0;34m(name_or_buffer, mode)\u001b[0m\n\u001b[1;32m    443\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_open_file_like\u001b[39m(name_or_buffer, mode):\n\u001b[1;32m    444\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m _is_path(name_or_buffer):\n\u001b[0;32m--> 445\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_open_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    446\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    447\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m mode:\n",
                        "File \u001b[0;32m~/Downloads/Final-Project/.venv/lib/python3.11/site-packages/torch/serialization.py:426\u001b[0m, in \u001b[0;36m_open_file.__init__\u001b[0;34m(self, name, mode)\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, mode):\n\u001b[0;32m--> 426\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m)\n",
                        "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Results/Results_Output_Actual/rps_test1/weights/best.pt'"
                    ]
                }
            ],
            "source": [
                "model = YOLO(weight_path)\n",
                "data_path = f'{train_path}/data.yaml'\n",
                "results = model.train(data=data_path, epochs = 50, batch=-1, cache = True, project = 'Results_Output_Actual', name=f'rps_test{test_number}')"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "///////////////////////////////////////////////////////////////TESTING//////////////////////////////////////////////////////////////////////////////////"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "results = model(test_images_path)\n",
                "os.makedirs(f'{result_path}/Test_Images_Results', exist_ok=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# for i, result in enumerate(results):\n",
                "#     boxes = result.boxes  # Boxes object for bounding box outputs\n",
                "#     masks = result.masks  # Masks object for segmentation masks outputs\n",
                "#     keypoints = result.keypoints  # Keypoints object for pose outputs\n",
                "#     probs = result.probs  # Probs object for classification outputs\n",
                "#     #result.show()  # display to screen\n",
                "#     result.save(filename=f'{result_path}/Test_Images_Results/result{i}.jpg')  # save to disk\n",
                "#     #print(result.boxes.data.tolist())"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "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": "3.11.8"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}