From b53ff568a535297087bad8ab153788362fc205d2 Mon Sep 17 00:00:00 2001
From: fresleven <khotayush@gmail.com>
Date: Fri, 24 Feb 2023 14:56:37 -0600
Subject: [PATCH] update simdata
---
crop/SimData.ipynb | 65 ++++++++++++++++++++++++++++++----------------
1 file changed, 43 insertions(+), 22 deletions(-)
diff --git a/crop/SimData.ipynb b/crop/SimData.ipynb
index 4ec0073..25d8be8 100644
--- a/crop/SimData.ipynb
+++ b/crop/SimData.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
@@ -11,24 +11,26 @@
"import pillow_heif\n",
"import pandas as pd\n",
"import numpy as np\n",
- "from random import randint"
+ "from torchvision.utils import save_image\n",
+ "import torchvision.transforms.functional as fn"
]
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# TODO: overlap?\n",
- "def generate_data(bg: Image, beetle_set: list[Image], num_beetles_arr: list[int]):\n",
+ "def generate_data(bg: list[Image], beetle_set: list[Image], num_beetles_arr: list[int]):\n",
" set_size = len(beetle_set)\n",
- " width, height = bg.size\n",
- "\n",
+ " set_bg_size = len(bg)\n",
" sim_arr, coords_arr = [],[]\n",
"\n",
" for num_beetles in num_beetles_arr:\n",
- " bg_temp = bg.copy()\n",
+ " bg_id = np.random.randint(0, set_bg_size)\n",
+ " bg_temp = bg[bg_id].copy()\n",
+ " width, height = bg_temp.size\n",
" beetle_coords = []\n",
" for _ in range(num_beetles):\n",
" # get random beetle image\n",
@@ -54,7 +56,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
@@ -71,60 +73,79 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"bg = Image.open(\"bg.png\")\n",
"beetles = []\n",
- "for file in glob.glob(r\"/raid/projects/akhot2/group-01-phys371-sp2023/beetles/*\"):\n",
+ "for file in glob.glob(r\"/raid/projects/akhot2/group-01-phys371-sp2023/crop/beetles/*\"):\n",
" b0 = Image.open(file)\n",
" beetles.append(b0);\n",
"\n",
"# map corners of trap to corners of image\n",
"coeffs = find_coeffs([(128,6), (1904,62), (2113,3137), (3,3228)], \n",
" [(0,0), (bg.size[0], 0), (bg.size[0], bg.size[1]), (0, bg.size[1])])\n",
- "new_size = (1280, 1280)\n",
- "bg_flat = bg.transform(bg.size, Image.PERSPECTIVE, coeffs, Image.BICUBIC)\n",
- "bg_resize = bg_flat.resize(new_size, Image.ANTIALIAS)"
+ "bg_flat = bg.transform(bg.size, Image.PERSPECTIVE, coeffs, Image.BICUBIC)"
]
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
- "beetle_counts = np.random.randint(1, 6, size=100)\n",
- "sim_img_arr, coords_arr = generate_data(bg_flat, [b0], beetle_counts)"
+ "beetle_counts = np.random.randint(0, 6, size=1)\n",
+ "sim_img_arr, coords_arr = generate_data([bg_flat], beetles, beetle_counts)"
]
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"def export(img_arr, coords_arr):\n",
+ " transform = transforms.Compose([transforms.ToTensor()])\n",
" s = \"\"\n",
+ " image_set = \"train\"\n",
+ " change = len(img_arr)*0.8\n",
" for i in range(len(img_arr)):\n",
" s = \"\"\n",
" for coord in coords_arr[i]:\n",
" c, x, y, w, h, a = coord\n",
" img = img_arr[i]\n",
- " s += f\"{0} {x/img.size[0]} {y/img.size[1]} {w/img.size[0]} {h/img.size[1]}\\n\"\n",
" \n",
- " image_set = \"test\"\n",
+ " s += f\"{0} {x/img.size[0]} {y/img.size[1]} {w/img.size[0]} {h/img.size[1]}\\n\"\n",
+ " if i > change:\n",
+ " image_set = \"test\"\n",
" with open(\"data/\" +image_set+ f\"/labels/sim{i}.txt\", \"w\") as f:\n",
" f.write(s)\n",
- " img.save(\"data/\" +image_set+ f\"/images/sim{i}.png\")"
+ " save_image(img, \"data/\" +image_set+ f\"/images/sim{i}.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 51,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "tensor or list of tensors expected, got <class 'PIL.Image.Image'>",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [51]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[43msim_img_arr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoords_arr\u001b[49m\u001b[43m)\u001b[49m\n",
+ "Input \u001b[0;32mIn [50]\u001b[0m, in \u001b[0;36mexport\u001b[0;34m(img_arr, coords_arr)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39mimage_set\u001b[38;5;241m+\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/labels/sim\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 14\u001b[0m f\u001b[38;5;241m.\u001b[39mwrite(s)\n\u001b[0;32m---> 15\u001b[0m \u001b[43msave_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mimage_set\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/images/sim\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m.png\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/autograd/grad_mode.py:27\u001b[0m, in \u001b[0;36m_DecoratorContextManager.__call__.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclone():\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\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",
+ "File \u001b[0;32m/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torchvision/utils.py:152\u001b[0m, in \u001b[0;36msave_image\u001b[0;34m(tensor, fp, format, **kwargs)\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mis_scripting() \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mis_tracing():\n\u001b[1;32m 151\u001b[0m _log_api_usage_once(save_image)\n\u001b[0;32m--> 152\u001b[0m grid \u001b[38;5;241m=\u001b[39m \u001b[43mmake_grid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\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[1;32m 153\u001b[0m \u001b[38;5;66;03m# Add 0.5 after unnormalizing to [0, 255] to round to nearest integer\u001b[39;00m\n\u001b[1;32m 154\u001b[0m ndarr \u001b[38;5;241m=\u001b[39m grid\u001b[38;5;241m.\u001b[39mmul(\u001b[38;5;241m255\u001b[39m)\u001b[38;5;241m.\u001b[39madd_(\u001b[38;5;241m0.5\u001b[39m)\u001b[38;5;241m.\u001b[39mclamp_(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m255\u001b[39m)\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m, torch\u001b[38;5;241m.\u001b[39muint8)\u001b[38;5;241m.\u001b[39mnumpy()\n",
+ "File \u001b[0;32m/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torch/autograd/grad_mode.py:27\u001b[0m, in \u001b[0;36m_DecoratorContextManager.__call__.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclone():\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\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",
+ "File \u001b[0;32m/raid/projects/akhot2/conda/envs/akhot2/lib/python3.9/site-packages/torchvision/utils.py:60\u001b[0m, in \u001b[0;36mmake_grid\u001b[0;34m(tensor, nrow, padding, normalize, value_range, scale_each, pad_value, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m _log_api_usage_once(make_grid)\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39mis_tensor(tensor) \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(tensor, \u001b[38;5;28mlist\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mall\u001b[39m(torch\u001b[38;5;241m.\u001b[39mis_tensor(t) \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m tensor))):\n\u001b[0;32m---> 60\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtensor or list of tensors expected, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(tensor)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrange\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m 63\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 64\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe parameter \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrange\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is deprecated since 0.12 and will be removed in 0.14. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease use \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue_range\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 66\u001b[0m )\n",
+ "\u001b[0;31mTypeError\u001b[0m: tensor or list of tensors expected, got <class 'PIL.Image.Image'>"
+ ]
+ }
+ ],
"source": [
"export(sim_img_arr, coords_arr)"
]
--
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