470 lines
23 KiB
Plaintext
470 lines
23 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "3fe8177c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import fiftyone as fo\n",
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"from PIL import Image\n",
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"from detection import detect"
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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": 3,
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"id": "32f0f8ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"name = \"dataset-small\"\n",
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"dataset_dir = \"/home/zenon/Documents/master-thesis/evaluation/dataset-small\""
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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": 4,
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"id": "6343aa55",
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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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" 100% |█████████████████| 401/401 [633.3ms elapsed, 0s remaining, 633.2 samples/s] \n"
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]
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}
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],
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"source": [
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"# The splits to load\n",
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"splits = [\"val\"]\n",
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"\n",
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"# Load the dataset, using tags to mark the samples in each split\n",
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"dataset = fo.Dataset(name)\n",
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"for split in splits:\n",
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" dataset.add_dir(\n",
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" dataset_dir=dataset_dir,\n",
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" dataset_type=fo.types.YOLOv5Dataset,\n",
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" split=split,\n",
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" tags=split,\n",
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" )\n",
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"\n",
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"classes = dataset.default_classes\n",
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"predictions_view = dataset.view()"
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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": 5,
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"id": "29827e3f",
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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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" 100% |█████████████████| 401/401 [5.4m elapsed, 0s remaining, 1.4 samples/s] \n"
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]
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}
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],
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"source": [
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"# Do detections with model and save bounding boxes\n",
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"with fo.ProgressBar() as pb:\n",
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" for sample in pb(predictions_view):\n",
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" image = Image.open(sample.filepath)\n",
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" w, h = image.size\n",
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" pred = detect(sample.filepath, '../weights/yolo.onnx', '../weights/resnet.onnx')\n",
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"\n",
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" detections = []\n",
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" for _, row in pred.iterrows():\n",
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" xmin, xmax = int(row['xmin']), int(row['xmax'])\n",
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" ymin, ymax = int(row['ymin']), int(row['ymax'])\n",
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" rel_box = [\n",
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" xmin / w, ymin / h, (xmax - xmin) / w, (ymax - ymin) / h\n",
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" ]\n",
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" detections.append(\n",
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" fo.Detection(label=classes[int(row['cls'])],\n",
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" bounding_box=rel_box,\n",
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" confidence=int(row['cls_conf'])))\n",
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"\n",
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" sample[\"yolo_resnet\"] = fo.Detections(detections=detections)\n",
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" sample.save()"
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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": 6,
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"id": "8ad67806",
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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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"Evaluating detections...\n",
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" 100% |█████████████████| 401/401 [1.2s elapsed, 0s remaining, 339.9 samples/s] \n",
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"Performing IoU sweep...\n",
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" 100% |█████████████████| 401/401 [1.4s elapsed, 0s remaining, 288.5 samples/s] \n"
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]
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}
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],
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"source": [
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"results = predictions_view.evaluate_detections(\n",
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" \"yolo_resnet\",\n",
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" gt_field=\"ground_truth\",\n",
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" eval_key=\"eval\",\n",
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" compute_mAP=True,\n",
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")"
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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": 18,
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"id": "b180420b",
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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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" precision recall f1-score support\n",
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"\n",
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" Healthy 0.80 0.81 0.81 430\n",
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" Stressed 0.77 0.72 0.75 315\n",
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"\n",
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" micro avg 0.79 0.77 0.78 745\n",
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" macro avg 0.79 0.77 0.78 745\n",
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"weighted avg 0.79 0.77 0.78 745\n",
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"\n",
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"0.6336217415940075\n"
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]
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},
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{
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"data": {
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"output_type": "display_data"
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"data": {
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8e819dea581e48d69877fb551a949e49",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"FigureWidget({\n",
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" 'data': [{'mode': 'markers',\n",
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" 'opacity': 0.1,\n",
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" 'type': 'scatter',\n",
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" 'uid': '918e4315-b093-4d2b-8af4-789b5a5d5152',\n",
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" 'x': array([0, 1, 2, 0, 1, 2, 0, 1, 2]),\n",
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" 'y': array([0, 0, 0, 1, 1, 1, 2, 2, 2])},\n",
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" {'colorscale': [[0.0, 'rgb(255,245,235)'], [0.125,\n",
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" 'rgb(254,230,206)'], [0.25, 'rgb(253,208,162)'],\n",
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" [0.375, 'rgb(253,174,107)'], [0.5, 'rgb(253,141,60)'],\n",
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" [0.625, 'rgb(241,105,19)'], [0.75, 'rgb(217,72,1)'],\n",
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" [0.875, 'rgb(166,54,3)'], [1.0, 'rgb(127,39,4)']],\n",
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" 'hoverinfo': 'skip',\n",
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" 'showscale': False,\n",
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" 'type': 'heatmap',\n",
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" 'uid': 'c2b76e66-be9d-4ca2-9060-d350e1d5e030',\n",
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" 'z': array([[ 86, 68, 0],\n",
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" [ 0, 228, 87],\n",
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" [348, 0, 82]]),\n",
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" 'zmax': 348,\n",
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" 'zmin': 0},\n",
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" {'colorbar': {'len': 1, 'lenmode': 'fraction'},\n",
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" 'colorscale': [[0.0, 'rgb(255,245,235)'], [0.125,\n",
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" 'rgb(254,230,206)'], [0.25, 'rgb(253,208,162)'],\n",
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" [0.375, 'rgb(253,174,107)'], [0.5, 'rgb(253,141,60)'],\n",
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" [0.625, 'rgb(241,105,19)'], [0.75, 'rgb(217,72,1)'],\n",
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" [0.875, 'rgb(166,54,3)'], [1.0, 'rgb(127,39,4)']],\n",
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" 'hovertemplate': '<b>count: %{z}</b><br>truth: %{y}<br>predicted: %{x}<extra></extra>',\n",
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" 'opacity': 0.25,\n",
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" 'type': 'heatmap',\n",
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" 'uid': 'faa2cc05-e7dd-41df-8b22-533e4ae70f67',\n",
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" 'z': array([[ 86, 68, 0],\n",
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" [ 0, 228, 87],\n",
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" [348, 0, 82]]),\n",
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" 'zmax': 348,\n",
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" 'zmin': 0}],\n",
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" 'layout': {'clickmode': 'event',\n",
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" 'margin': {'b': 0, 'l': 0, 'r': 0, 't': 30},\n",
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" 'template': '...',\n",
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" 'title': {},\n",
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" 'xaxis': {'constrain': 'domain',\n",
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" 'range': [-0.5, 2.5],\n",
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" 'tickmode': 'array',\n",
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" 'ticktext': [Healthy, Stressed, (none)],\n",
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" 'tickvals': array([0, 1, 2])},\n",
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" 'yaxis': {'constrain': 'domain',\n",
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" 'range': [-0.5, 2.5],\n",
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" 'scaleanchor': 'x',\n",
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" 'scaleratio': 1,\n",
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" 'tickmode': 'array',\n",
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" 'ticktext': array(['(none)', 'Stressed', 'Healthy'], dtype=object),\n",
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" 'tickvals': array([0, 1, 2])}}\n",
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"})"
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"data": {
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" 'data': [{'customdata': array([99. , 99. , 99. , 99. , 99. , 99. , 99. , 99. , 99. , 99. , 99. , 98. ,\n",
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" 98. , 98. , 97. , 97. , 97. , 97. , 97. , 96. , 96. , 95. , 94.2, 94. ,\n",
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" 93.2, 93. , 92.9, 91.9, 91.2, 91. , 91. , 90.8, 89.8, 88.8, 88. , 87.9,\n",
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" 87. , 86.9, 86. , 85.9, 85.8, 85. , 84.8, 84.6, 83.1, 81.9, 81.8, 81.1,\n",
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" 80.1, 79.9, 79.7, 78.9, 78.6, 77.9, 77.7, 76.8, 76. , 75.6, 74.6, 74.2,\n",
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" 'hovertemplate': ('<b>class: %{text}</b><br>recal' ... 'customdata:.3f}<extra></extra>'),\n",
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" 'text': array(['Healthy', 'Healthy', 'Healthy', 'Healthy', 'Healthy', 'Healthy',\n",
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" 'Healthy', 'Healthy', 'Healthy', 'Healthy', 'Healthy', 'Healthy',\n",
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|
|
" 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21, 0.22, 0.23,\n",
|
|
" 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32, 0.33, 0.34, 0.35,\n",
|
|
" 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47,\n",
|
|
" 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59,\n",
|
|
" 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 , 0.71,\n",
|
|
" 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83,\n",
|
|
" 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95,\n",
|
|
" 0.96, 0.97, 0.98, 0.99, 1. ]),\n",
|
|
" 'y': array([1. , 1. , 1. , 1. , 1. , 1. ,\n",
|
|
" 1. , 1. , 1. , 1. , 0.97379592, 0.94476123,\n",
|
|
" 0.94476123, 0.94476123, 0.93427565, 0.91445476, 0.85844913, 0.85844913,\n",
|
|
" 0.85790639, 0.85788924, 0.85788924, 0.85747126, 0.85663314, 0.85452586,\n",
|
|
" 0.85298487, 0.84306319, 0.8375 , 0.8375 , 0.8375 , 0.8375 ,\n",
|
|
" 0.8375 , 0.8375 , 0.8375 , 0.8375 , 0.8375 , 0.8375 ,\n",
|
|
" 0.83717347, 0.83658868, 0.83565817, 0.8336283 , 0.82847628, 0.82488659,\n",
|
|
" 0.82126275, 0.8175916 , 0.81473127, 0.80427951, 0.8016311 , 0.80115198,\n",
|
|
" 0.8008129 , 0.80053685, 0.79883857, 0.79853619, 0.79853619, 0.79782171,\n",
|
|
" 0.79757033, 0.79595576, 0.79417872, 0.79338307, 0.79116989, 0.78813658,\n",
|
|
" 0.78474129, 0.78273994, 0.77996131, 0.77679573, 0.70534431, 0.70114991,\n",
|
|
" 0.69945778, 0.62345091, 0.54988677, 0.54689953, 0.46774707, 0.31001791,\n",
|
|
" 0.1547619 , 0. , 0. , 0. , 0. , 0. ,\n",
|
|
" 0. , 0. , 0. , 0. , 0. , 0. ,\n",
|
|
" 0. , 0. , 0. , 0. , 0. , 0. ,\n",
|
|
" 0. , 0. , 0. , 0. , 0. , 0. ,\n",
|
|
" 0. , 0. , 0. , 0. , 0. ])}],\n",
|
|
" 'layout': {'margin': {'b': 0, 'l': 0, 'r': 0, 't': 30},\n",
|
|
" 'shapes': [{'line': {'dash': 'dash'}, 'type': 'line', 'x0': 0, 'x1': 1, 'y0': 1, 'y1': 0}],\n",
|
|
" 'template': '...',\n",
|
|
" 'xaxis': {'constrain': 'domain', 'range': [0, 1], 'title': {'text': 'Recall'}},\n",
|
|
" 'yaxis': {'constrain': 'domain',\n",
|
|
" 'range': [0, 1],\n",
|
|
" 'scaleanchor': 'x',\n",
|
|
" 'scaleratio': 1,\n",
|
|
" 'title': {'text': 'Precision'}}}\n",
|
|
"})"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Get the 10 most common classes in the dataset\n",
|
|
"counts = dataset.count_values(\"ground_truth.detections.label\")\n",
|
|
"classes_top10 = sorted(counts, key=counts.get, reverse=True)\n",
|
|
"\n",
|
|
"# Print a classification report for the top-10 classes\n",
|
|
"results.print_report(classes=classes_top10)\n",
|
|
"\n",
|
|
"print(results.mAP())\n",
|
|
"\n",
|
|
"# Plot confusion matrix\n",
|
|
"matrix = results.plot_confusion_matrix(classes=classes)\n",
|
|
"matrix.show()\n",
|
|
"\n",
|
|
"pr_curves = results.plot_pr_curves(classes=[\"Healthy\", \"Stressed\"])\n",
|
|
"pr_curves.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "d1137788",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Session launched. Run `session.show()` to open the App in a cell output.\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/javascript": [
|
|
"window.open('http://localhost:5151/');"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.Javascript object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"session = fo.launch_app(dataset, auto=False)\n",
|
|
"session.view = predictions_view\n",
|
|
"session.open_tab()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "535003f4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"session.plots.attach(matrix)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d3ba32f0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.7.15"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|