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depth_imgs-one.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "88f87ccc",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/miniconda3/envs/midas/lib/python3.8/site-packages/tqdm/auto.py:22: 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"
]
}
],
"source": [
"import cv2\n",
"import torch\n",
"import urllib.request\n",
"import matplotlib.pyplot as plt\n",
"import plotly.express as px\n",
"from plotly.subplots import make_subplots\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c2b8fd26",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using cache found in /home/ubuntu/.cache/torch/hub/intel-isl_MiDaS_master\n"
]
}
],
"source": [
"model_type = \"DPT_Large\" # MiDaS v3 - Large (highest accuracy, slowest inference speed)\n",
"#model_type = \"DPT_Hybrid\" # MiDaS v3 - Hybrid (medium accuracy, medium inference speed)\n",
"#model_type = \"MiDaS_small\" # MiDaS v2.1 - Small (lowest accuracy, highest inference speed)\n",
"\n",
"midas = torch.hub.load(\"intel-isl/MiDaS\", model_type)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "78330d56",
"metadata": {},
"outputs": [
{
"data": {
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" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): Identity()\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU()\n",
" (drop1): Dropout(p=0.0, inplace=False)\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop2): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (22): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): Identity()\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU()\n",
" (drop1): Dropout(p=0.0, inplace=False)\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop2): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (23): Block(\n",
" (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (attn): Attention(\n",
" (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
" (attn_drop): Dropout(p=0.0, inplace=False)\n",
" (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (proj_drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (drop_path): Identity()\n",
" (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (mlp): Mlp(\n",
" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (act): GELU()\n",
" (drop1): Dropout(p=0.0, inplace=False)\n",
" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (drop2): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)\n",
" (pre_logits): Identity()\n",
" (head): Linear(in_features=1024, out_features=1000, bias=True)\n",
" )\n",
" (act_postprocess1): Sequential(\n",
" (0): ProjectReadout(\n",
" (project): Sequential(\n",
" (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
" (1): GELU()\n",
" )\n",
" )\n",
" (1): Transpose()\n",
" (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
" (3): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
" (4): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(4, 4))\n",
" )\n",
" (act_postprocess2): Sequential(\n",
" (0): ProjectReadout(\n",
" (project): Sequential(\n",
" (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
" (1): GELU()\n",
" )\n",
" )\n",
" (1): Transpose()\n",
" (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
" (3): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))\n",
" (4): ConvTranspose2d(512, 512, kernel_size=(2, 2), stride=(2, 2))\n",
" )\n",
" (act_postprocess3): Sequential(\n",
" (0): ProjectReadout(\n",
" (project): Sequential(\n",
" (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
" (1): GELU()\n",
" )\n",
" )\n",
" (1): Transpose()\n",
" (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
" (3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
" )\n",
" (act_postprocess4): Sequential(\n",
" (0): ProjectReadout(\n",
" (project): Sequential(\n",
" (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
" (1): GELU()\n",
" )\n",
" )\n",
" (1): Transpose()\n",
" (2): Unflatten(dim=2, unflattened_size=torch.Size([24, 24]))\n",
" (3): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
" (4): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
" )\n",
" )\n",
" (scratch): Module(\n",
" (layer1_rn): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (layer2_rn): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (layer3_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (layer4_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (refinenet1): FeatureFusionBlock_custom(\n",
" (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
" (resConfUnit1): ResidualConvUnit_custom(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (activation): ReLU()\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (resConfUnit2): ResidualConvUnit_custom(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (activation): ReLU()\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (refinenet2): FeatureFusionBlock_custom(\n",
" (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
" (resConfUnit1): ResidualConvUnit_custom(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (activation): ReLU()\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (resConfUnit2): ResidualConvUnit_custom(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (activation): ReLU()\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (refinenet3): FeatureFusionBlock_custom(\n",
" (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
" (resConfUnit1): ResidualConvUnit_custom(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (activation): ReLU()\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (resConfUnit2): ResidualConvUnit_custom(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (activation): ReLU()\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (refinenet4): FeatureFusionBlock_custom(\n",
" (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
" (resConfUnit1): ResidualConvUnit_custom(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (activation): ReLU()\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (resConfUnit2): ResidualConvUnit_custom(\n",
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (activation): ReLU()\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (output_conv): Sequential(\n",
" (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (1): Interpolate()\n",
" (2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (3): ReLU(inplace=True)\n",
" (4): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))\n",
" (5): ReLU(inplace=True)\n",
" (6): Identity()\n",
" )\n",
" )\n",
")"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
"midas.to(device)\n",
"midas.eval()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a221abed",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"device(type='cuda')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3144d493",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using cache found in /home/ubuntu/.cache/torch/hub/intel-isl_MiDaS_master\n"
]
}
],
"source": [
"midas_transforms = torch.hub.load(\"intel-isl/MiDaS\", \"transforms\")\n",
"\n",
"if model_type == \"DPT_Large\" or model_type == \"DPT_Hybrid\":\n",
" transform = midas_transforms.dpt_transform\n",
"else:\n",
" transform = midas_transforms.small_transform"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "71661559",
"metadata": {},
"outputs": [],
"source": [
"from fastai.vision.all import *"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c6dc4dbf",
"metadata": {},
"outputs": [],
"source": [
"src_folder = Path('./DS_video/no_fire_imgs_full/')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cba3ecd8",
"metadata": {},
"outputs": [],
"source": [
"video_paths = src_folder.ls()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "40fc182b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(#59) [Path('DS_video/no_fire_imgs_full/597_seq1430_1502'),Path('DS_video/no_fire_imgs_full/597_seq563_740'),Path('DS_video/no_fire_imgs_full/30_seq0_665'),Path('DS_video/no_fire_imgs_full/705_seq0_1147'),Path('DS_video/no_fire_imgs_full/173_seq0_745'),Path('DS_video/no_fire_imgs_full/758_seq0_1328'),Path('DS_video/no_fire_imgs_full/168_seq0_1155'),Path('DS_video/no_fire_imgs_full/686_seq6_1502'),Path('DS_video/no_fire_imgs_full/561_seq0_1376'),Path('DS_video/no_fire_imgs_full/52_seq0_1164')...]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"video_paths"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "80a97740",
"metadata": {},
"outputs": [],
"source": [
"imgs = [video/'10.jpg' for video in video_paths]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a9637ccf",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[Path('DS_video/no_fire_imgs_full/597_seq1430_1502/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/597_seq563_740/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/30_seq0_665/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/705_seq0_1147/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/173_seq0_745/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/758_seq0_1328/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/168_seq0_1155/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/686_seq6_1502/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/561_seq0_1376/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/52_seq0_1164/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/556_seq940_963/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/62_seq0_774/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/260_seq0_93/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/264_seq0_291/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/298_seq0_326/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/246_seq737_938/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/556_seq965_1502/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/260_seq573_759/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/173_seq751_776/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/216_seq1036_1277/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/334_seq0_1061/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/597_seq0_562/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/881_seq0_1204/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/173_seq777_887/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/173_seq1029_1092/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/246_seq939_950/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/224_seq296_327/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/26_seq510_859/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/262_seq0_588/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/369_seq1341_1391/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/318_seq0_534/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/298_seq366_383/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/502_seq0_1072/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/224_seq0_276/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/367_seq121_1271/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/315_seq0_472/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/26_seq0_484/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/793_seq0_1057/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/369_seq0_1241/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/240_seq0_789/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/894_seq1_1502/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/387_seq513_721/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/712_seq0_1293/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/387_seq0_512/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/597_seq1043_1131/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/741_seq0_162/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/398_seq704_721/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/177_seq0_1311/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/556_seq841_931/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/601_seq0_1253/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/284_seq0_214/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/696_seq266_276/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/344_seq775_1501/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/695_seq0_102/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/35_seq0_58/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/348_seq0_904/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/216_seq0_861/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/705_seq1169_1502/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/607_seq0_1502/10.jpg')]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imgs"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0bc38f72",
"metadata": {},
"outputs": [],
"source": [
"folder_dest = Path('./DS_video/no_fire_depth_imgs/')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "72532278",
"metadata": {},
"outputs": [],
"source": [
"img_path = imgs[0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "bc3290e8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Path('DS_video/no_fire_imgs_full/597_seq1430_1502/10.jpg')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img_path"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "c6aea077",
"metadata": {},
"outputs": [],
"source": [
"output_path = str(folder_dest/f'{img_path.parent.name}.jpg')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "6e00dbe9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'DS_video/no_fire_depth_imgs/597_seq1430_1502.jpg'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_path"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "728ea009",
"metadata": {},
"outputs": [],
"source": [
"for img_path in imgs:\n",
" img = cv2.imread(str(img_path))\n",
" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
" input_batch = transform(img).to(device)\n",
" with torch.no_grad():\n",
" prediction = midas(input_batch)\n",
" prediction = torch.nn.functional.interpolate(\n",
" prediction.unsqueeze(1),\n",
" size=img.shape[:2],\n",
" mode=\"bicubic\",\n",
" align_corners=False,\n",
" ).squeeze()\n",
"\n",
" output = prediction.cpu().numpy()\n",
" output = (output/40*255).astype(int)\n",
"# (folder_dest/img_path.parent.name).mkdir(exist_ok=True)\n",
" output_path = str(folder_dest/f'{img_path.parent.name}.jpg')\n",
" cv2.imwrite(output_path, output)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "11886e70",
"metadata": {},
"outputs": [],
"source": [
"nb_test=5\n",
"start=6\n",
"imgs_test = imgs[start:start+nb_test]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "97254b60",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Path('DS_video/no_fire_imgs_full/597_seq1430_1502/10.jpg'),\n",
" Path('DS_video/no_fire_imgs_full/597_seq563_740/10.jpg'),\n",
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" Path('DS_video/no_fire_imgs_full/705_seq0_1147/10.jpg'),\n",
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},
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"metadata": {},
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}
],
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},
{
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"id": "098ba990",
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"outputs": [
{
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]
},
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"metadata": {},
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],
"source": [
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]
},
{
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"id": "df047aaa",
"metadata": {
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},
"outputs": [
{
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