diff --git a/crop/SimData.ipynb b/crop/SimData.ipynb
index 25d8be86fc3e1ed7a98de283fb136c3019f73424..c475a8013d724c6a2cd2fb9558d8e144741063a1 100644
--- a/crop/SimData.ipynb
+++ b/crop/SimData.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
@@ -12,16 +12,21 @@
"import pandas as pd\n",
"import numpy as np\n",
"from torchvision.utils import save_image\n",
- "import torchvision.transforms.functional as fn"
+ "import torchvision.transforms.functional as fn\n",
+ "import torchvision.transforms as transforms\n",
+ "import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
- "# TODO: overlap?\n",
+ "# generates new dataset by pasting beetles and non-beetles in the same picture. \n",
+ "#returns array of new images and coordinates\n",
+ "#TODO: make sure that the beetles and non-beetles don't overlap with each other\n",
+ "\n",
"def generate_data(bg: list[Image], beetle_set: list[Image], num_beetles_arr: list[int]):\n",
" set_size = len(beetle_set)\n",
" set_bg_size = len(bg)\n",
@@ -39,14 +44,18 @@
" beetle_width, beetle_height = beetle_img.size\n",
"\n",
" # get random x,y coords to paste beetle\n",
- " x = randint(0, width - beetle_width)\n",
- " y = randint(0, height - beetle_height)\n",
+ " x = np.random.randint(0, width - beetle_width)\n",
+ " y = np.random.randint(0, height - beetle_height)\n",
"\n",
" # get random beetle rotation\n",
- " angle = randint(0, 359)\n",
+ " angle = np.random.randint(0, 360)\n",
" beetle_img = beetle_img.rotate(angle, resample=Image.BICUBIC)\n",
"\n",
" bg_temp.paste(beetle_img, box=(x,y), mask=beetle_img)\n",
+ " \n",
+ " #centers x and y for YOLOv5 PyTorch label\n",
+ " x += beetle_width/2\n",
+ " y += beetle_height/2\n",
" beetle_coords.append((beetle_id, x, y, beetle_width, beetle_height, angle))\n",
" sim_arr.append(bg_temp)\n",
" coords_arr.append(beetle_coords)\n",
@@ -56,10 +65,11 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
+ "#???\n",
"def find_coeffs(source_coords, target_coords):\n",
" matrix = []\n",
" for s, t in zip(source_coords, target_coords):\n",
@@ -73,11 +83,11 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
- "bg = Image.open(\"bg.png\")\n",
+ "bg = Image.open(\"imgs/bg.png\")\n",
"beetles = []\n",
"for file in glob.glob(r\"/raid/projects/akhot2/group-01-phys371-sp2023/crop/beetles/*\"):\n",
" b0 = Image.open(file)\n",
@@ -91,58 +101,1298 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
- "beetle_counts = np.random.randint(0, 6, size=1)\n",
+ "beetle_counts = np.random.randint(0, 6, size=1250)\n",
"sim_img_arr, coords_arr = generate_data([bg_flat], beetles, beetle_counts)"
]
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
+ "#exports images and coordinates in YOLOv5 PyTorch format\n",
"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",
+ " img = img_arr[i]\n",
" for coord in coords_arr[i]:\n",
" c, x, y, w, h, a = coord\n",
- " img = img_arr[i]\n",
- " \n",
" s += f\"{0} {x/img.size[0]} {y/img.size[1]} {w/img.size[0]} {h/img.size[1]}\\n\"\n",
+ " print(i)\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",
- " save_image(img, \"data/\" +image_set+ f\"/images/sim{i}.png\")"
+ " img.save(\"data/\" +image_set+ f\"/images/sim{i}.png\")"
]
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 63,
"metadata": {},
"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'>"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "1200\n",
+ "1201\n",
+ "1202\n",
+ "1203\n",
+ "1204\n",
+ "1205\n",
+ "1206\n",
+ "1207\n",
+ "1208\n",
+ "1209\n",
+ "1210\n",
+ "1211\n",
+ "1212\n",
+ "1213\n",
+ "1214\n",
+ "1215\n",
+ "1216\n",
+ "1217\n",
+ "1218\n",
+ "1219\n",
+ "1220\n",
+ "1221\n",
+ "1222\n",
+ "1223\n",
+ "1224\n",
+ "1225\n",
+ "1226\n",
+ "1227\n",
+ "1228\n",
+ "1229\n",
+ "1230\n",
+ "1231\n",
+ "1232\n",
+ "1233\n",
+ "1234\n",
+ "1235\n",
+ "1236\n",
+ "1237\n",
+ "1238\n",
+ "1239\n",
+ "1240\n",
+ "1241\n",
+ "1242\n",
+ "1243\n",
+ "1244\n",
+ "1245\n",
+ "1246\n",
+ "1247\n",
+ "1248\n",
+ "1249\n"
]
}
],
@@ -164,30 +1414,82 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 49,
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RGBA\n",
+ "(2, 4)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
{
"data": {
+ "image/png": "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\n",
"text/plain": [
- "100"
+ "<Figure size 432x288 with 1 Axes>"
]
},
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "len(beetle_counts)"
+ "from torchvision.utils import draw_bounding_boxes\n",
+ "import torch\n",
+ "import torchvision\n",
+ "transform = transforms.Compose([transforms.ToTensor()])\n",
+ "for i in range(len(sim_img_arr)):\n",
+ " print(sim_img_arr[i].mode)\n",
+ " \n",
+ " tensor = transform(sim_img_arr[i].convert(\"RGB\")).type(torch.uint8)\n",
+ " coords_list = np.zeros((len(coords_arr[i]), 4))\n",
+ " j=0\n",
+ " for k in coords_arr[i]:\n",
+ " c, x, y, w, h, a = k\n",
+ " coords_list[j, 0] = x\n",
+ " coords_list[j, 1] = y\n",
+ " coords_list[j, 2] = int(x+w)\n",
+ " coords_list[j, 3] = int(y+h)\n",
+ " j+=1\n",
+ " print(coords_list.shape)\n",
+ " draw_bounding_boxes(tensor, torch.Tensor(coords_list))\n",
+ " plt.imshow(tensor.permute(1,2,0)[:,:,0:3])\n",
+ " plt.show()\n",
+ " break\n",
+ " \n"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
- "source": []
+ "source": [
+ "First:\n",
+ " - created 1000 images on a clean background variable 1 to 5 of the same beetle, model detected well on test set of 100 images (val/exp or exp2 or exp3) (proof of concept)\n",
+ " - created 1000 images on a clean background variable 0 to 5 beetles of 6 different types of beetles, performing\n",
+ " \n",
+ "TODO:\n",
+ " - created 1000 images on a clean background variable 0 to 5 beetles of 6 different images and 0 to 5 non-beetles of _ different images, performing\n",
+ " - created 1000 images on a clean background variable 0 to 10 beetles of 15 different images and 0 to 10 non-beetles of _ different images\n",
+ " - created 1000 images on a clean background variable 0 to 10 beetles of 15 different images and 0 to 10 non-beetles of _ different images on a dirty background\n",
+ " - perform auto cropping on arduino and include "
+ ]
}
],
"metadata": {