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{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loading Roboflow workspace...\n",
"loading Roboflow project...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading Dataset Version Zip in cvparsing-2 to yolov9:: 100%|██████████| 63864/63864 [00:04<00:00, 15236.33it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"Extracting Dataset Version Zip to cvparsing-2 in yolov9:: 100%|██████████| 2344/2344 [00:00<00:00, 5118.00it/s]\n"
]
}
],
"source": [
"!pip install roboflow\n",
"\n",
"from roboflow import Roboflow\n",
"rf = Roboflow(api_key=\"ZvM6LUyWI7hiVw6K64bt\")\n",
"project = rf.workspace(\"capitaletech-wrnth\").project(\"annotation-moxcs\")\n",
"version = project.version(2)\n",
"dataset = version.download(\"yolov8\")\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: ultralytics in d:\\fu\\dat\\.venv\\lib\\site-packages (8.2.90)\n",
"Requirement already satisfied: numpy<2.0.0,>=1.23.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (1.26.4)\n",
"Requirement already satisfied: matplotlib>=3.3.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (3.9.2)\n",
"Requirement already satisfied: opencv-python>=4.6.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (4.10.0.84)\n",
"Requirement already satisfied: pillow>=7.1.2 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (10.4.0)\n",
"Requirement already satisfied: pyyaml>=5.3.1 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (6.0.2)\n",
"Requirement already satisfied: requests>=2.23.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (2.32.3)\n",
"Requirement already satisfied: scipy>=1.4.1 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (1.14.1)\n",
"Requirement already satisfied: torch>=1.8.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (2.4.1)\n",
"Requirement already satisfied: torchvision>=0.9.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (0.19.1)\n",
"Requirement already satisfied: tqdm>=4.64.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (4.66.5)\n",
"Requirement already satisfied: psutil in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (6.0.0)\n",
"Requirement already satisfied: py-cpuinfo in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (9.0.0)\n",
"Requirement already satisfied: pandas>=1.1.4 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (2.2.2)\n",
"Requirement already satisfied: seaborn>=0.11.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (0.13.2)\n",
"Requirement already satisfied: ultralytics-thop>=2.0.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from ultralytics) (2.0.6)\n",
"Requirement already satisfied: contourpy>=1.0.1 in d:\\fu\\dat\\.venv\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (1.3.0)\n",
"Requirement already satisfied: cycler>=0.10 in d:\\fu\\dat\\.venv\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (4.53.1)\n",
"Requirement already satisfied: kiwisolver>=1.3.1 in d:\\fu\\dat\\.venv\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (1.4.7)\n",
"Requirement already satisfied: packaging>=20.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (24.1)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in d:\\fu\\dat\\.venv\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (3.1.4)\n",
"Requirement already satisfied: python-dateutil>=2.7 in d:\\fu\\dat\\.venv\\lib\\site-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in d:\\fu\\dat\\.venv\\lib\\site-packages (from pandas>=1.1.4->ultralytics) (2024.1)\n",
"Requirement already satisfied: tzdata>=2022.7 in d:\\fu\\dat\\.venv\\lib\\site-packages (from pandas>=1.1.4->ultralytics) (2024.1)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in d:\\fu\\dat\\.venv\\lib\\site-packages (from requests>=2.23.0->ultralytics) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in d:\\fu\\dat\\.venv\\lib\\site-packages (from requests>=2.23.0->ultralytics) (3.7)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in d:\\fu\\dat\\.venv\\lib\\site-packages (from requests>=2.23.0->ultralytics) (2.2.2)\n",
"Requirement already satisfied: certifi>=2017.4.17 in d:\\fu\\dat\\.venv\\lib\\site-packages (from requests>=2.23.0->ultralytics) (2024.8.30)\n",
"Requirement already satisfied: filelock in d:\\fu\\dat\\.venv\\lib\\site-packages (from torch>=1.8.0->ultralytics) (3.16.0)\n",
"Requirement already satisfied: typing-extensions>=4.8.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from torch>=1.8.0->ultralytics) (4.12.2)\n",
"Requirement already satisfied: sympy in d:\\fu\\dat\\.venv\\lib\\site-packages (from torch>=1.8.0->ultralytics) (1.13.2)\n",
"Requirement already satisfied: networkx in d:\\fu\\dat\\.venv\\lib\\site-packages (from torch>=1.8.0->ultralytics) (3.3)\n",
"Requirement already satisfied: jinja2 in d:\\fu\\dat\\.venv\\lib\\site-packages (from torch>=1.8.0->ultralytics) (3.1.4)\n",
"Requirement already satisfied: fsspec in d:\\fu\\dat\\.venv\\lib\\site-packages (from torch>=1.8.0->ultralytics) (2024.9.0)\n",
"Requirement already satisfied: colorama in d:\\fu\\dat\\.venv\\lib\\site-packages (from tqdm>=4.64.0->ultralytics) (0.4.6)\n",
"Requirement already satisfied: six>=1.5 in d:\\fu\\dat\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in d:\\fu\\dat\\.venv\\lib\\site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n"
]
}
],
"source": [
"!pip install ultralytics"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"yaml_text = \"\"\"train: /cvparsing-2/train/images\n",
"val: /cvparsing-2/valid/images\n",
"test: /cvparsing-2/test/images\n",
"\n",
"nc: 14\n",
"names: ['Achievement', 'Certifications', 'Community', 'Contact', 'Education', 'Experience', 'Interests', 'Languages', 'Name', 'Profil', 'Projects', 'image', 'resume', 'skills']\"\"\"\n",
"\n",
"with open(\"./data.yaml\", 'w') as file:\n",
" file.write(yaml_text),\n",
"\n",
"# To display the content of the file, you can use the 'cat' command like this:\n",
"# %cat /kaggle/working/data.yaml\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"!yolo train model=yolov9c.yaml data=D:/FU/DAT/src/notebook/datasets/data.yaml epochs=100 imgsz=640 device=0"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ultralytics YOLOv8.2.90 Python-3.11.9 torch-2.4.1+cu118 CUDA:0 (NVIDIA GeForce RTX 4050 Laptop GPU, 6140MiB)\n",
"Setup complete (20 CPUs, 15.7 GB RAM, 33.9/97.7 GB disk)\n"
]
}
],
"source": [
"# %pip install ultralytics\n",
"import ultralytics\n",
"ultralytics.checks()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\FU\\DAT\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"d:\\FU\\DAT\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:159: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\htbqn\\.cache\\huggingface\\hub\\models--microsoft--trocr-base-handwritten. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
"To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
" warnings.warn(message)\n",
"d:\\FU\\DAT\\.venv\\Lib\\site-packages\\transformers\\tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
" warnings.warn(\n",
"Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-handwritten and are newly initialized: ['encoder.pooler.dense.bias', 'encoder.pooler.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
"d:\\FU\\DAT\\.venv\\Lib\\site-packages\\transformers\\generation\\utils.py:1258: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from transformers import TrOCRProcessor, VisionEncoderDecoderModel\n",
"from PIL import Image\n",
"import requests\n",
"\n",
"# load image from the IAM database\n",
"# url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'\n",
"image = Image.open(r'./images.png').convert(\"RGB\")\n",
"\n",
"processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')\n",
"model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')\n",
"pixel_values = processor(images=image, return_tensors=\"pt\").pixel_values\n",
"\n",
"generated_ids = model.generate(pixel_values)\n",
"generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 2, 288, 321, 2]])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generated_ids"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import onnxruntime as ort\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"# Load the ONNX model\n",
"model_path = \"../model/section_detection.onnx\"\n",
"session = ort.InferenceSession(model_path)\n",
"\n",
"# Load and preprocess the image\n",
"image_path = 'D:/FU/DAT/src/notebook/datasets/train/images/1629756071561_jpg.rf.05f192117b5f0f8125474abdf3392f72.jpg'\n",
"image = Image.open(image_path)\n",
"image_data = np.array(image).astype('float32').transpose(2, 0, 1)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 3, 640, 640)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_data = np.expand_dims(image_data, axis=0)\n",
"image_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"input_name = session.get_inputs()[0].name\n",
"output_name = session.get_outputs()[0].name"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"result = session.run([output_name], {input_name: image_data})[0]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(18, 8400)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[0].shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING Unable to automatically guess model task, assuming 'task=detect'. Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify','pose' or 'obb'.\n",
"Loading ..\\model\\section_detection.onnx for ONNX Runtime inference...\n",
"\n",
"image 1/1 D:\\FU\\DAT\\src\\notebook\\datasets\\train\\images\\1629756071561_jpg.rf.05f192117b5f0f8125474abdf3392f72.jpg: 640x640 2 Achievements, 147.6ms\n",
"Speed: 2.5ms preprocess, 147.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n"
]
}
],
"source": [
"from ultralytics import YOLO\n",
"\n",
"# Load the YOLOv8 model'\n",
"\n",
"# Load the exported ONNX model\n",
"onnx_model = YOLO(\"../model/section_detection.onnx\")\n",
"\n",
"# Run inference\n",
"results = onnx_model(\"D:/FU/DAT/src/notebook/datasets/train/images/1629756071561_jpg.rf.05f192117b5f0f8125474abdf3392f72.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"for result in 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",
" obb = result.obb # Oriented boxes object for OBB outputs\n",
" result.show() # display to screen\n",
" result.save(filename=\"result.jpg\") # save to disk"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"You are already logged into Roboflow. To make a different login,run roboflow.login(force=True).\n"
]
}
],
"source": [
"!roboflow login"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"ename": "RoboflowAPINotAuthorizedError",
"evalue": "Unauthorized access to roboflow API - check API key. Visit https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key to learn how to retrieve one.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mHTTPError\u001b[0m Traceback (most recent call last)",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\inference\\core\\roboflow_api.py:80\u001b[0m, in \u001b[0;36mwrap_roboflow_api_errors.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 80\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\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[0;32m 81\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mConnectionError, \u001b[38;5;167;01mConnectionError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m error:\n",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\inference\\core\\roboflow_api.py:227\u001b[0m, in \u001b[0;36mget_roboflow_model_data\u001b[1;34m(api_key, model_id, endpoint_type, device_id)\u001b[0m\n\u001b[0;32m 223\u001b[0m api_url \u001b[38;5;241m=\u001b[39m _add_params_to_url(\n\u001b[0;32m 224\u001b[0m url\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;00mAPI_BASE_URL\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mendpoint_type\u001b[38;5;241m.\u001b[39mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 225\u001b[0m params\u001b[38;5;241m=\u001b[39mparams,\n\u001b[0;32m 226\u001b[0m )\n\u001b[1;32m--> 227\u001b[0m api_data \u001b[38;5;241m=\u001b[39m \u001b[43m_get_from_url\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_url\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 228\u001b[0m cache\u001b[38;5;241m.\u001b[39mset(\n\u001b[0;32m 229\u001b[0m api_data_cache_key,\n\u001b[0;32m 230\u001b[0m api_data,\n\u001b[0;32m 231\u001b[0m expire\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m,\n\u001b[0;32m 232\u001b[0m )\n",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\inference\\core\\roboflow_api.py:473\u001b[0m, in \u001b[0;36m_get_from_url\u001b[1;34m(url, json_response)\u001b[0m\n\u001b[0;32m 472\u001b[0m response \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(wrap_url(url))\n\u001b[1;32m--> 473\u001b[0m \u001b[43mapi_key_safe_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 474\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m json_response:\n",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\inference\\core\\utils\\requests.py:15\u001b[0m, in \u001b[0;36mapi_key_safe_raise_for_status\u001b[1;34m(response)\u001b[0m\n\u001b[0;32m 14\u001b[0m response\u001b[38;5;241m.\u001b[39murl \u001b[38;5;241m=\u001b[39m API_KEY_PATTERN\u001b[38;5;241m.\u001b[39msub(deduct_api_key, response\u001b[38;5;241m.\u001b[39murl)\n\u001b[1;32m---> 15\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\requests\\models.py:1021\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1020\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[1;32m-> 1021\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
"\u001b[1;31mHTTPError\u001b[0m: 401 Client Error: Unauthorized for url: https://api.roboflow.com/ort/annotation-moxcs/2?nocache=true&device=ABAOXOMTIEU&dynamic=true",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mRoboflowAPINotAuthorizedError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[4], line 10\u001b[0m\n\u001b[0;32m 7\u001b[0m image \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(image_file)\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# load a pre-trained yolov8n model\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mget_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mannotation-moxcs/2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# run inference on our chosen image, image can be a url, a numpy array, a PIL image, etc.\u001b[39;00m\n\u001b[0;32m 13\u001b[0m results \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39minfer(image)[\u001b[38;5;241m0\u001b[39m]\n",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\inference\\models\\utils.py:275\u001b[0m, in \u001b[0;36mget_model\u001b[1;34m(model_id, api_key, **kwargs)\u001b[0m\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_model\u001b[39m(model_id, api_key\u001b[38;5;241m=\u001b[39mAPI_KEY, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Model:\n\u001b[1;32m--> 275\u001b[0m task, model \u001b[38;5;241m=\u001b[39m \u001b[43mget_model_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 276\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ROBOFLOW_MODEL_TYPES[(task, model)](model_id, api_key\u001b[38;5;241m=\u001b[39mapi_key, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\inference\\core\\registries\\roboflow.py:115\u001b[0m, in \u001b[0;36mget_model_type\u001b[1;34m(model_id, api_key)\u001b[0m\n\u001b[0;32m 108\u001b[0m save_model_metadata_in_cache(\n\u001b[0;32m 109\u001b[0m dataset_id\u001b[38;5;241m=\u001b[39mdataset_id,\n\u001b[0;32m 110\u001b[0m version_id\u001b[38;5;241m=\u001b[39mversion_id,\n\u001b[0;32m 111\u001b[0m project_task_type\u001b[38;5;241m=\u001b[39mproject_task_type,\n\u001b[0;32m 112\u001b[0m model_type\u001b[38;5;241m=\u001b[39mmodel_type,\n\u001b[0;32m 113\u001b[0m )\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m project_task_type, model_type\n\u001b[1;32m--> 115\u001b[0m api_data \u001b[38;5;241m=\u001b[39m \u001b[43mget_roboflow_model_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 116\u001b[0m \u001b[43m \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 117\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 118\u001b[0m \u001b[43m \u001b[49m\u001b[43mendpoint_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mModelEndpointType\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mORT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 119\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mGLOBAL_DEVICE_ID\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 120\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mort\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m api_data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 122\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ModelArtefactError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError loading model artifacts from Roboflow API.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\inference\\core\\roboflow_api.py:93\u001b[0m, in \u001b[0;36mwrap_roboflow_api_errors.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 91\u001b[0m error_handler \u001b[38;5;241m=\u001b[39m user_handler_override\u001b[38;5;241m.\u001b[39mget(status_code, default_handler)\n\u001b[0;32m 92\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error_handler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 93\u001b[0m \u001b[43merror_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43merror\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m RoboflowAPIUnsuccessfulRequestError(\n\u001b[0;32m 95\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnsuccessful request to Roboflow API with response code: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mstatus_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 96\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merror\u001b[39;00m\n\u001b[0;32m 97\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mInvalidJSONError \u001b[38;5;28;01mas\u001b[39;00m error:\n",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\inference\\core\\roboflow_api.py:60\u001b[0m, in \u001b[0;36m<lambda>\u001b[1;34m(e)\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mraise_from_lambda\u001b[39m(\n\u001b[0;32m 54\u001b[0m inner_error: \u001b[38;5;167;01mException\u001b[39;00m, exception_type: Type[\u001b[38;5;167;01mException\u001b[39;00m], message: \u001b[38;5;28mstr\u001b[39m\n\u001b[0;32m 55\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 56\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception_type(message) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01minner_error\u001b[39;00m\n\u001b[0;32m 59\u001b[0m DEFAULT_ERROR_HANDLERS \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m---> 60\u001b[0m \u001b[38;5;241m401\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m e: \u001b[43mraise_from_lambda\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 61\u001b[0m \u001b[43m \u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mRoboflowAPINotAuthorizedError\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mUnauthorized access to roboflow API - check API key. Visit \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mhttps://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key to learn how to retrieve one.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[0;32m 66\u001b[0m \u001b[38;5;241m404\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m e: raise_from_lambda(\n\u001b[0;32m 67\u001b[0m e, RoboflowAPINotNotFoundError, NOT_FOUND_ERROR_MESSAGE\n\u001b[0;32m 68\u001b[0m ),\n\u001b[0;32m 69\u001b[0m }\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrap_roboflow_api_errors\u001b[39m(\n\u001b[0;32m 73\u001b[0m http_errors_handlers: Optional[\n\u001b[0;32m 74\u001b[0m Dict[\u001b[38;5;28mint\u001b[39m, Callable[[Union[requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mHTTPError]], \u001b[38;5;28;01mNone\u001b[39;00m]]\n\u001b[0;32m 75\u001b[0m ] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 76\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mcallable\u001b[39m:\n\u001b[0;32m 77\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorator\u001b[39m(function: \u001b[38;5;28mcallable\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mcallable\u001b[39m:\n",
"File \u001b[1;32mc:\\Users\\htbqn\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\inference\\core\\roboflow_api.py:56\u001b[0m, in \u001b[0;36mraise_from_lambda\u001b[1;34m(inner_error, exception_type, message)\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mraise_from_lambda\u001b[39m(\n\u001b[0;32m 54\u001b[0m inner_error: \u001b[38;5;167;01mException\u001b[39;00m, exception_type: Type[\u001b[38;5;167;01mException\u001b[39;00m], message: \u001b[38;5;28mstr\u001b[39m\n\u001b[0;32m 55\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 56\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception_type(message) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01minner_error\u001b[39;00m\n",
"\u001b[1;31mRoboflowAPINotAuthorizedError\u001b[0m: Unauthorized access to roboflow API - check API key. Visit https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key to learn how to retrieve one."
]
}
],
"source": [
"from inference import get_model\n",
"import supervision as sv\n",
"import cv2\n",
"\n",
"# define the image url to use for inference\n",
"image_file = \"taylor-swift-album-1989.jpeg\"\n",
"image = cv2.imread(image_file)\n",
"\n",
"# load a pre-trained yolov8n model\n",
"model = get_model(model_id=\"annotation-moxcs/2\")\n",
"\n",
"# run inference on our chosen image, image can be a url, a numpy array, a PIL image, etc.\n",
"results = model.infer(image)[0]\n",
"\n",
"# load the results into the supervision Detections api\n",
"detections = sv.Detections.from_inference(results)\n",
"\n",
"# create supervision annotators\n",
"bounding_box_annotator = sv.BoundingBoxAnnotator()\n",
"label_annotator = sv.LabelAnnotator()\n",
"\n",
"# annotate the image with our inference results\n",
"annotated_image = bounding_box_annotator.annotate(\n",
" scene=image, detections=detections)\n",
"annotated_image = label_annotator.annotate(\n",
" scene=annotated_image, detections=detections)\n",
"\n",
"# display the image\n",
"sv.plot_image(annotated_image)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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|