From 7d5ff30b6e9180dbeb1781bf51f7f2b2df9eac83 Mon Sep 17 00:00:00 2001
From: fresleven <khotayush@gmail.com>
Date: Wed, 12 Apr 2023 10:10:21 -0500
Subject: [PATCH] changes to simdata
---
crop/SimData.ipynb | 2025 +++++++++++++++++---------------------------
1 file changed, 775 insertions(+), 1250 deletions(-)
diff --git a/crop/SimData.ipynb b/crop/SimData.ipynb
index 6988fab..6a7e91d 100644
--- a/crop/SimData.ipynb
+++ b/crop/SimData.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 200,
"metadata": {},
"outputs": [],
"source": [
@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 201,
"metadata": {},
"outputs": [],
"source": [
@@ -80,11 +80,16 @@
" # get random beetle image\n",
" beetle_id = np.random.randint(0, set_size)\n",
" beetle_img = beetle_set[beetle_id]\n",
- " \n",
" # get random beetle rotation\n",
" angle = np.random.randint(0, 360)\n",
" beetle_img = beetle_img.rotate(angle, resample=Image.BICUBIC, expand=1)\n",
" \n",
+ " #randomly resize beetle to be smaller as they are much bigger on image\n",
+ " \n",
+ " beetle_width, beetle_height = beetle_img.size\n",
+ " beetle_max = np.max([beetle_width, beetle_height])\n",
+ " factor = np.random.uniform((height/(5*9))/beetle_max, (height/(4*9))/beetle_max)\n",
+ " beetle_img = beetle_img.resize((int(factor * beetle_width), int(factor * beetle_height)), resample=Image.BICUBIC)\n",
" beetle_width, beetle_height = beetle_img.size\n",
" \n",
" # get random x,y coords to paste beetle\n",
@@ -121,7 +126,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 202,
"metadata": {},
"outputs": [],
"source": [
@@ -139,7 +144,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 203,
"metadata": {},
"outputs": [],
"source": [
@@ -154,6 +159,7 @@
" backgrounds.append(bg)\n",
" continue\n",
" bg = Image.open(file)\n",
+ " \n",
" backgrounds.append(bg);\n",
"\n",
"beetles = []\n",
@@ -168,7 +174,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 204,
"metadata": {},
"outputs": [
{
@@ -189,7 +195,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 188,
"metadata": {},
"outputs": [],
"source": [
@@ -201,7 +207,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 189,
"metadata": {},
"outputs": [],
"source": [
@@ -226,7 +232,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 190,
"metadata": {},
"outputs": [
{
@@ -242,1247 +248,7 @@
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]
}
],
@@ -1649,6 +415,765 @@
"len(backgrounds)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using cache found in /home/akhot2/.cache/torch/hub/ultralytics_yolov5_master\n",
+ "YOLOv5 🚀 2023-3-31 Python-3.9.12 torch-1.11.0 CUDA:0 (A100-SXM4-40GB, 40537MiB)\n",
+ "\n",
+ "Fusing layers... \n",
+ "Model summary: 212 layers, 20852934 parameters, 0 gradients, 47.9 GFLOPs\n",
+ "Adding AutoShape... \n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "AutoShape(\n",
+ " (model): DetectMultiBackend(\n",
+ " (model): DetectionModel(\n",
+ " (model): Sequential(\n",
+ " (0): Conv(\n",
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+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (1): Conv(\n",
+ " (conv): Conv2d(48, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
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+ " )\n",
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+ " (act): SiLU(inplace=True)\n",
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+ " )\n",
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+ " (cv1): Conv(\n",
+ " (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv2): Conv(\n",
+ " (conv): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv3): Conv(\n",
+ " (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (m): Sequential(\n",
+ " (0): Bottleneck(\n",
+ " (cv1): Conv(\n",
+ " (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv2): Conv(\n",
+ " (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " (1): Bottleneck(\n",
+ " (cv1): Conv(\n",
+ " (conv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv2): Conv(\n",
+ " (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (18): Conv(\n",
+ " (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (19): Concat()\n",
+ " (20): C3(\n",
+ " (cv1): Conv(\n",
+ " (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv2): Conv(\n",
+ " (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv3): Conv(\n",
+ " (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (m): Sequential(\n",
+ " (0): Bottleneck(\n",
+ " (cv1): Conv(\n",
+ " (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv2): Conv(\n",
+ " (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " (1): Bottleneck(\n",
+ " (cv1): Conv(\n",
+ " (conv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv2): Conv(\n",
+ " (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (21): Conv(\n",
+ " (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (22): Concat()\n",
+ " (23): C3(\n",
+ " (cv1): Conv(\n",
+ " (conv): Conv2d(768, 384, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv2): Conv(\n",
+ " (conv): Conv2d(768, 384, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv3): Conv(\n",
+ " (conv): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (m): Sequential(\n",
+ " (0): Bottleneck(\n",
+ " (cv1): Conv(\n",
+ " (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv2): Conv(\n",
+ " (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " (1): Bottleneck(\n",
+ " (cv1): Conv(\n",
+ " (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " (cv2): Conv(\n",
+ " (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (act): SiLU(inplace=True)\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (24): Detect(\n",
+ " (m): ModuleList(\n",
+ " (0): Conv2d(192, 18, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (1): Conv2d(384, 18, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (2): Conv2d(768, 18, kernel_size=(1, 1), stride=(1, 1))\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "execution_count": 196,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "model = torch.hub.load('ultralytics/yolov5', 'custom', '/raid/projects/akhot2/group-01-phys371-sp2023/yolov5_model/runs/train/20beetle_40-non_20dirt_bkg_overlap/weights/best.pt')\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 199,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "YOLOv5 <class 'models.common.Detections'> instance\n",
+ "image 1/1: 2746x1610 3 beetless\n",
+ "Speed: 125.2ms pre-process, 13.4ms inference, 1.1ms NMS per image at shape (1, 3, 640, 384)"
+ ]
+ },
+ "execution_count": 199,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model('/raid/projects/akhot2/group-01-phys371-sp2023/crop/data/train/images/sim1.png')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real18.jpg\n",
+ "(1744, 2778)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real16.jpg\n",
+ "(1894, 3155)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real7.jpg\n",
+ "(1837, 2978)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real8.jpg\n",
+ "(1680, 2791)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real10.jpg\n",
+ "(1880, 3101)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real13.jpg\n",
+ "(1572, 2582)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real15.jpg\n",
+ "(1486, 2422)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real6.jpg\n",
+ "(1635, 2699)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/F28_3AUGUST2022_cropped.jpg\n",
+ "(1850, 2765)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/bg.png\n",
+ "(2114, 3236)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real17.jpg\n",
+ "(1769, 3095)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real1.jpg\n",
+ "(1751, 2631)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real20.jpg\n",
+ "(1521, 2306)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real4.jpg\n",
+ "(1640, 2289)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real3.jpg\n",
+ "(1610, 2746)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real19.jpg\n",
+ "(1662, 2767)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real9.jpg\n",
+ "(1627, 2155)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real14.jpg\n",
+ "(1548, 2187)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real5.jpg\n",
+ "(1940, 2713)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real12.jpg\n",
+ "(1494, 2695)\n",
+ "/raid/projects/akhot2/group-01-phys371-sp2023/crop/cropped_imgs/real2.jpg\n",
+ "(1940, 2713)\n"
+ ]
+ }
+ ],
+ "source": [
+ "width = []\n",
+ "height = []\n",
+ "for file in glob.glob('/raid/projects/akhot2/group-01-phys371-sp2023/crop/data/train/sim0.png'):\n",
+ " print(file)\n",
+ " n_b0 = Image.open(file)\n",
+ " print(n_b0.size)\n",
+ " w, h = n_b0.size\n",
+ " width.append(w)\n",
+ " height.append(h)\n",
+ " #f = model(file)\n",
+ " #print(f)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "78.57894736842105"
+ ]
+ },
+ "execution_count": 76,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(width)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "92.47368421052632"
+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(height)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1723.5238095238096"
+ ]
+ },
+ "execution_count": 154,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(width)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2704.9523809523807"
+ ]
+ },
+ "execution_count": 155,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.mean(height)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "94.5"
+ ]
+ },
+ "execution_count": 93,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "width[8]*.7"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "54.599999999999994"
+ ]
+ },
+ "execution_count": 94,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "height[8]*.7"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1744,\n",
+ " 1894,\n",
+ " 1837,\n",
+ " 1680,\n",
+ " 1880,\n",
+ " 1572,\n",
+ " 1486,\n",
+ " 1635,\n",
+ " 1850,\n",
+ " 2114,\n",
+ " 1769,\n",
+ " 1751,\n",
+ " 1521,\n",
+ " 1640,\n",
+ " 1610,\n",
+ " 1662,\n",
+ " 1627,\n",
+ " 1548,\n",
+ " 1940,\n",
+ " 1494,\n",
+ " 1940]"
+ ]
+ },
+ "execution_count": 156,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "width"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 166,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "51.44444444444444\n",
+ "41.15555555555556\n",
+ "58.425925925925924\n",
+ "46.74074074074074\n",
+ "55.148148148148145\n",
+ "44.11851851851852\n",
+ "51.68518518518518\n",
+ "41.34814814814815\n",
+ "57.425925925925924\n",
+ "45.94074074074074\n",
+ "47.81481481481482\n",
+ "38.25185185185185\n",
+ "44.851851851851855\n",
+ "35.88148148148148\n",
+ "49.98148148148148\n",
+ "39.98518518518519\n",
+ "51.2037037037037\n",
+ "40.96296296296296\n",
+ "59.925925925925924\n",
+ "47.94074074074074\n",
+ "57.31481481481482\n",
+ "45.851851851851855\n",
+ "48.72222222222222\n",
+ "38.977777777777774\n",
+ "42.7037037037037\n",
+ "34.162962962962965\n",
+ "42.388888888888886\n",
+ "33.91111111111111\n",
+ "50.851851851851855\n",
+ "40.681481481481484\n",
+ "51.24074074074074\n",
+ "40.992592592592594\n",
+ "39.907407407407405\n",
+ "31.925925925925927\n",
+ "40.5\n",
+ "32.4\n",
+ "50.24074074074074\n",
+ "40.19259259259259\n",
+ "49.907407407407405\n",
+ "39.925925925925924\n",
+ "50.24074074074074\n",
+ "40.19259259259259\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in height:\n",
+ " print((i/(6*9)))\n",
+ " print((i/(7.5*9)))"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
--
GitLab