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Add anonymizer poc
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@tsundvoll
tsundvoll committed Oct 1, 2024
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"from PIL import Image\n",
"from ultralytics import YOLO"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"model_path = \"./models/yolov8n.pt\"\n",
"model = YOLO(model_path)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"img_path_1 = \"./photos/apprentices.jpg\"\n",
"img_path_2 = \"./photos/digital_field_worker.jpg\"\n",
"img_path_3 = \"./photos/JS in sunset.jpg\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"YOLOv8n summary: 225 layers, 3,157,200 parameters, 0 gradients, 8.9 GFLOPs\n"
]
},
{
"data": {
"text/plain": [
"(225, 3157200, 0, 8.8575488)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.info()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"image 1/1 /Users/TSUNDV/equinor/inspection-data-analyzer/anonymizer/photos/apprentices.jpg: 448x640 2 persons, 46.7ms\n",
"Speed: 1.5ms preprocess, 46.7ms inference, 0.3ms postprocess per image at shape (1, 3, 448, 640)\n",
"Results saved to \u001b[1m/Users/TSUNDV/equinor/inspection-data-analyzer/runs/detect/predict\u001b[0m\n",
"2 labels saved to /Users/TSUNDV/equinor/inspection-data-analyzer/runs/detect/predict/labels\n",
"[ultralytics.engine.results.Results object with attributes:\n",
"\n",
"boxes: ultralytics.engine.results.Boxes object\n",
"keypoints: None\n",
"masks: None\n",
"names: {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}\n",
"obb: None\n",
"orig_img: array([[[155, 159, 164],\n",
" [150, 154, 159],\n",
" [155, 160, 163],\n",
" ...,\n",
" [ 48, 57, 66],\n",
" [ 50, 59, 68],\n",
" [ 51, 60, 69]],\n",
"\n",
" [[156, 160, 165],\n",
" [154, 158, 163],\n",
" [156, 161, 164],\n",
" ...,\n",
" [ 52, 61, 70],\n",
" [ 57, 66, 75],\n",
" [ 53, 62, 71]],\n",
"\n",
" [[154, 158, 163],\n",
" [154, 158, 163],\n",
" [153, 157, 162],\n",
" ...,\n",
" [ 51, 60, 69],\n",
" [ 53, 62, 71],\n",
" [ 52, 61, 70]],\n",
"\n",
" ...,\n",
"\n",
" [[ 88, 82, 83],\n",
" [ 93, 87, 88],\n",
" [ 94, 88, 89],\n",
" ...,\n",
" [ 84, 91, 100],\n",
" [ 79, 86, 95],\n",
" [ 80, 87, 96]],\n",
"\n",
" [[ 89, 83, 84],\n",
" [ 93, 87, 88],\n",
" [ 98, 92, 93],\n",
" ...,\n",
" [ 83, 90, 99],\n",
" [ 82, 89, 98],\n",
" [ 84, 91, 100]],\n",
"\n",
" [[ 94, 88, 89],\n",
" [ 97, 91, 92],\n",
" [100, 94, 95],\n",
" ...,\n",
" [ 83, 90, 99],\n",
" [ 83, 90, 99],\n",
" [ 86, 93, 102]]], dtype=uint8)\n",
"orig_shape: (1333, 2000)\n",
"path: '/Users/TSUNDV/equinor/inspection-data-analyzer/anonymizer/photos/apprentices.jpg'\n",
"probs: None\n",
"save_dir: '/Users/TSUNDV/equinor/inspection-data-analyzer/runs/detect/predict'\n",
"speed: {'preprocess': 1.516103744506836, 'inference': 46.656131744384766, 'postprocess': 0.3299713134765625}]\n"
]
}
],
"source": [
"# Predict\n",
"results = model(img_path_1)\n",
"print(results)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# Predict with segmentation\n",
"model_seg = YOLO(\"./models/yolov8n-seg.pt\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image_path = \"./photos/JS in sunset.jpg\"\n",
"im1 = cv2.imread(image_path)\n",
"results = model_seg.predict(source=im1, save=True, save_txt=True)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"0: 448x640 1 person, 47.7ms\n",
"Speed: 1.6ms preprocess, 47.7ms inference, 0.9ms postprocess per image at shape (1, 3, 448, 640)\n",
"Results saved to \u001b[1m/Users/TSUNDV/equinor/inspection-data-analyzer/runs/segment/predict4\u001b[0m\n",
"1 label saved to /Users/TSUNDV/equinor/inspection-data-analyzer/runs/segment/predict4/labels\n"
]
}
],
"source": [
"im1 = cv2.imread(img_path_2)\n",
"results = model_seg.predict(source=im1, classes=[0], save=True) #, stream=True)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"0: 448x640 (no detections), 43.6ms\n",
"No masks found\n"
]
}
],
"source": [
"raw_image = np.array(Image.open(img_path_3))\n",
"CLASS_PERSON = 0\n",
"\n",
"results = model_seg.predict(source=raw_image, classes=[CLASS_PERSON], stream=True)\n",
"\n",
"image_anonymized = raw_image\n",
"\n",
"for result in results:\n",
" if not result.masks:\n",
" print(\"No masks found\")\n",
" break\n",
"\n",
" masks = result.masks.data.cpu().numpy()\n",
" for mask in masks:\n",
" h, w, _ = raw_image.shape\n",
" mask = cv2.resize(mask, (w, h))\n",
"\n",
" colored_mask = np.expand_dims(mask, 0).repeat(3, axis=0)\n",
" colored_mask = np.moveaxis(colored_mask, 0, -1)\n",
"\n",
" color = (0, 0, 0)\n",
" masked = np.ma.MaskedArray(image_anonymized, mask=colored_mask, fill_value=color)\n",
" image_anonymized = masked.filled()\n",
"\n",
"im = Image.fromarray(image_anonymized)\n",
"im.save(\"your_file3.jpeg\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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