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Gijs-Koot/cv_starter_nb.ipynb

Last active August 22, 2024 07:53
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cv_starter_nb.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "PaSeWCj7FxNn"
},
"source": [
"# Computer vision starter notebook\n",
"\n",
"Examples of running a pretrained CV model from HuggingFace"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"from io import BytesIO\n",
"import requests\n",
"\n",
"img_dog = Image.open(BytesIO(requests.get(\"https://farm3.staticflickr.com/2633/3938105788_410f0def89_z.jpg\").content))\n",
"img_dog"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mUjnv-9jOlhn"
},
"outputs": [],
"source": [
"# use pretrained yolo8 model\n",
"\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolov8n.pt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WxSpVLt6PDph",
"outputId": "bb306a65-9ec1-466f-8ef5-6883535dbfdb",
"scrolled": true
},
"outputs": [],
"source": [
"# inference\n",
"\n",
"results = model([img_dog], conf=.2)\n",
"\n",
"# draw boxes on copy of the image\n",
"\n",
"from PIL import ImageDraw\n",
"\n",
"res_img_dog = img_dog.copy()\n",
"draw = ImageDraw.Draw(res_img_dog)\n",
"\n",
"# Iterate over the results and draw bounding boxes\n",
"for result in results:\n",
" for box in result.boxes:\n",
" # Get the bounding box coordinates\n",
" x1, y1, x2, y2 = box.xyxy[0].tolist()\n",
" \n",
" # Draw the bounding box on the image\n",
" draw.rectangle([x1, y1, x2, y2], outline=\"red\", width=2)\n",
" \n",
" # Optionally, you can add labels or other information\n",
" draw.text((x1, y1), result.names[int(box.cls)], fill=\"red\")\n",
"\n",
"res_img_dog"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Aerial image from WMS service\n",
"\n",
"ulx, uly = 168130, 452830\n",
"\n",
"url = \"https://service.pdok.nl/hwh/luchtfotorgb/wms/v1_0?\"\n",
"params = f\"service=wms&request=GetMap&layers=Actueel_orthoHR&format=image/png&bbox={ulx},{uly},{ulx + 100},{uly + 100}&&width=1000&height=1000&crs=EPSG:28992&version=1_0\"\n",
"url + params"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = requests.get(url + params)\n",
"aerial_img = Image.open(BytesIO(response.content))\n",
"\n",
"aerial_img"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# obtain model from https://huggingface.co/finloop/yolov8s-seg-solar-panels\n",
"\n",
"!wget \"https://huggingface.co/finloop/yolov8s-seg-solar-panels/resolve/main/best.pt?download=true\" -O solar.pt\n",
"!ls"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"solar_panel_model = YOLO('solar.pt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"# draw boxes on copy of the image\n",
"\n",
"from PIL import ImageDraw\n",
"\n",
"res_aerial_img = aerial_img.resize((1000, 1000))\n",
"results = solar_panel_model([res_aerial_img], conf=.01)\n",
"draw = ImageDraw.Draw(res_aerial_img)\n",
"\n",
"# Iterate over the results and draw bounding boxes\n",
"for result in results:\n",
" for box in result.boxes:\n",
" # Get the bounding box coordinates\n",
" x1, y1, x2, y2 = box.xyxy[0].tolist()\n",
" \n",
" # Draw the bounding box on the image\n",
" draw.rectangle([x1, y1, x2, y2], outline=\"red\", width=2)\n",
" \n",
" # Optionally, you can add labels or other information\n",
" draw.text((x1, y1), result.names[int(box.cls)], fill=\"red\")\n",
"\n",
"res_aerial_img"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 400,
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"d6412397c31145ec9bb5e72b8585bcd2",
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"d65cc12c4c334bf9ade9ae8c678d2304",
"e03959ef7f2f4ac29c7fce2c077d1166",
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]
},
"id": "lZ_KLB9CPdtp",
"outputId": "a9118e2b-3f8e-471f-efc6-da36bee0b93e"
},
"source": [
"# 4. Zero-shot object detection model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 400,
"referenced_widgets": [
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},
"id": "lZ_KLB9CPdtp",
"outputId": "a9118e2b-3f8e-471f-efc6-da36bee0b93e"
},
"outputs": [],
"source": [
"import requests\n",
"\n",
"import torch\n",
"from PIL import Image\n",
"from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection\n",
"\n",
"model_id = \"IDEA-Research/grounding-dino-tiny\"\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"processor = AutoProcessor.from_pretrained(model_id)\n",
"model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 400,
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"source": [
"# use model on aerial image\n",
"\n",
"text = \"a car.\"\n",
"\n",
"inputs = processor(images=aerial_img, text=text, return_tensors=\"pt\").to(device)\n",
"with torch.no_grad():\n",
" outputs = model(**inputs)\n",
"\n",
"results = processor.post_process_grounded_object_detection(\n",
" outputs,\n",
" inputs.input_ids,\n",
" box_threshold=0.25,\n",
" text_threshold=0.25,\n",
" target_sizes=[aerial_img.size[::-1]]\n",
")"
]
},
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"res_zs_aerial_img = aerial_img.copy()\n",
"\n",
"draw = ImageDraw.Draw(res_zs_aerial_img)\n",
"for score, label, box in zip(results[0][\"scores\"], results[0][\"labels\"], results[0][\"boxes\"]):\n",
" if score > 0.2: # Confidence threshold\n",
" box = [round(i, 2) for i in box.tolist()]\n",
" draw.rectangle(box, outline=\"red\", width=2)\n",
" draw.text((box[0], box[1]), f\"{label}: {score:.2f}\", fill=\"red\")\n",
"\n",
"# Save the output image\n",
"\n",
"res_zs_aerial_img"
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