Ethan Wellenreiter
423b511dd9
Moving around the testing notebooks. Autocropping is about done with exception to any new versions or converting the stuff to C code. Signed-off-by: Ethan Wellenreiter <ewellenreiter@gmail.com>
386 lines
13 KiB
Plaintext
386 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 137,
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"metadata": {},
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"outputs": [],
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"source": [
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"version=2.0\n",
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"cachepath=\"../.cache/\"\n",
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"savepath=\"./savespot/\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 138,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch.utils.data import DataLoader\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as fn\n",
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"import torch.optim as optim\n",
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"import torchvision.transforms.functional as tvf\n",
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"import torchvision.transforms.v2 as v2\n",
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"import torchvision.models as models\n",
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"import torchvision.transforms as t\n",
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"\n",
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"\n",
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"from PIL import Image\n",
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"\n",
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"import datasets as ds\n",
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"from tqdm.autonotebook import tqdm\n",
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"\n",
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"import random\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import numpy as np\n",
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"\n",
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"\n",
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"torch.cuda.empty_cache()\n",
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"\n",
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"\n",
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"import os\n",
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"import cv2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 139,
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"metadata": {},
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"outputs": [],
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"source": [
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"# array = np.load(\"./testing_space/outputarray.npy\")\n",
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"# counter = np.load(\"./testing_space/counter.npy\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 140,
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"metadata": {},
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"outputs": [],
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"source": [
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"# print(array)\n",
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"# print(counter)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 141,
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"metadata": {},
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"outputs": [],
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"source": [
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"class RotationDeterminer(nn.Module):\n",
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" def __init__(self, new=False):\n",
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" super(RotationDeterminer,self).__init__()\n",
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" \n",
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" torch.cuda.empty_cache()\n",
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" \n",
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" self.device = torch.device(\"cpu\")\n",
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" if torch.cuda.is_available:\n",
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" self.device = torch.device(\"cuda:0\")\n",
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" \n",
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" \n",
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" self.appliers = [v2.RandomApply(transforms=[v2.RandomPosterize(bits=1)], p=0.25),\n",
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" v2.RandomApply(transforms=[v2.ElasticTransform(alpha=25.0)], p=0.25), # maybe add fill=appliedFill\n",
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" v2.RandomApply(transforms=[v2.GaussianBlur(kernel_size=(5,9), sigma=(0.1,2.))],p=0.25),\n",
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" v2.RandomApply(transforms=[v2.RandomEqualize()],p=0.25)]\n",
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" \n",
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" \n",
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" # self.conv = nn.Sequential(nn.Conv2d(3, 9, kernel_size=11,stride=3), # 1100 x 1100 => 201 x 201\n",
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" # nn.ReLU(inplace=True),\n",
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" # nn.Conv2d(9, 18, kernel_size=5,stride=1),\n",
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" # nn.ReLU(inplace=True),\n",
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" # nn.MaxPool2d(kernel_size=4, stride=2),\n",
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" # nn.Conv2d(18, 36, kernel_size=3,stride=2),\n",
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" # nn.BatchNorm2d(36),\n",
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" # nn.ReLU(inplace=True),\n",
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" # nn.Conv2d(36, 72, kernel_size=3,stride=2),\n",
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" # nn.ReLU(inplace=True),\n",
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" # nn.AvgPool2d(kernel_size=5, stride=3),\n",
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" # nn.Conv2d(72, 144, kernel_size=3,stride=1),\n",
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" # nn.ReLU(inplace=True),\n",
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" # nn.Conv2d(144, 288, kernel_size=5,stride=1),\n",
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" # nn.ReLU(inplace=True),\n",
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" # nn.MaxPool2d(kernel_size=4, stride=1),\n",
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" # nn.Conv2d(288, 192, kernel_size=3,stride=1),\n",
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" # nn.ReLU(inplace=True),\n",
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" # nn.Conv2d(192, 192, kernel_size=3,stride=1), # => 1\n",
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" # nn.ReLU(inplace=True))\n",
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" # print(\"hi\")\n",
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" self.conv = models.resnet18(pretrained=new)\n",
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" \n",
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" self.classifier = nn.Sequential(nn.Linear(1000, 4096),\n",
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" nn.ReLU(inplace=True),\n",
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" nn.Linear(4096,1))\n",
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" \n",
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" self.lossfunc = nn.MSELoss()\n",
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" \n",
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" self.imageprep = v2.Compose([self.SquarePad(),v2.Resize(512),v2.Grayscale(num_output_channels=3),v2.CenterCrop(512),v2.ToImageTensor(), v2.ConvertImageDtype()])\n",
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" \n",
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" \n",
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" class SquarePad:\n",
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" def __call__(self, image):\n",
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" # print(\"hi type:\", type(image))\n",
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" temp = image.size()\n",
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" w = temp[-2]\n",
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" h = temp[-1]\n",
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" max_wh = max([w, h])\n",
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" hp = int((max_wh - w) / 2)\n",
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" vp = int((max_wh - h) / 2)\n",
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" padding = (hp, vp, hp, vp)\n",
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" return tvf.pad(image, padding, 0, 'edge')\n",
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"\n",
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"\n",
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" \n",
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"\n",
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" \n",
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" def forward(self, image):\n",
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"\n",
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" transformedimage = self.imageprep(image)\n",
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" transformedimage = transformedimage.to(self.device)\n",
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"\n",
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" if (len(transformedimage.shape) != 4 and len(transformedimage.shape) != 3):\n",
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" raise Exception(\"Sorry, Dimension of image is incorrect (\", len(transformedimage.shape),\"). Expected a 3D (single image) or 4D (batch of images) tensor\")\n",
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"\n",
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" if (len(transformedimage.shape) == 3):\n",
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" x = transformedimage.unsqueeze(0)\n",
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" else:\n",
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" x = transformedimage\n",
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" \n",
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" x = self.conv(x)\n",
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" # print(x.shape)\n",
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" # x = nn.Flatten(start_dim=-1)(x)\n",
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" # print(x.shape)\n",
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" x = self.classifier(x)\n",
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" # print(x.shape)\n",
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" guessRotation = nn.Flatten(start_dim=0)(x)\n",
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" \n",
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" return guessRotation\n",
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" \n",
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" def loss(self, guess, trueAnswer):\n",
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" return self.lossfunc(guess, trueAnswer)\n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 142,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
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" warnings.warn(\n",
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"/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
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" warnings.warn(msg)\n"
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]
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}
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],
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"source": [
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"model = RotationDeterminer(new=True)\n",
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"device = torch.device(\"cpu\")\n",
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"if torch.cuda.is_available:\n",
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" device = torch.device(\"cuda:0\")\n",
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" model = model.to(device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 143,
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"metadata": {},
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"outputs": [],
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"source": [
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"# def ResizeWithAspectRatio(image, width=None, height=None, inter=cv2.INTER_AREA):\n",
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"# dim = None\n",
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"# (h, w) = image.shape[:2]\n",
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"\n",
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"# if width is None and height is None:\n",
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"# return image\n",
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"# if width is None:\n",
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"# r = height / float(h)\n",
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"# dim = (int(w * r), height)\n",
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"# else:\n",
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"# r = width / float(w)\n",
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"# dim = (width, int(h * r))\n",
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"\n",
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"# return cv2.resize(image, dim, interpolation=inter)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 163,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([1, 4032, 3024])\n",
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"torch.Size([3, 4032, 3024])\n",
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"0.7532281875610352\n"
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]
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}
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],
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"source": [
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"working_dataset = ds.load_from_disk(cachepath + \"datasets/customrotation/\")\n",
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"prepimage = v2.Compose([v2.Grayscale(num_output_channels=3),v2.Resize(512), v2.CenterCrop(512),v2.ToImageTensor(), v2.ConvertImageDtype()])\n",
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"tensorize = v2.Compose([v2.ToImageTensor(), v2.ConvertImageDtype()])\n",
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"grayscaler = v2.Grayscale(num_output_channels=3)\n",
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"working_dataset.set_transform(prepimage)\n",
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"counter = np.load(savepath + \"/v\"+str(version)+\"/counter.npy\")\n",
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"model.load_state_dict(torch.load(savepath + \"/v\"+str(version)+\"/modelsave\" + str(counter) +\"epochs\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 165,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([1, 800, 723])\n",
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"torch.Size([3, 800, 723])\n",
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"-1.3860492706298828\n"
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]
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}
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],
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"source": [
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"filereadimage = cv2.imread(\"./testing_space/cropped.jpg\", 0)\n",
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"# print(type(filereadimage))\n",
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"tensorizedimage = torch.unsqueeze(torch.from_numpy(filereadimage),0)\n",
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"print(tensorizedimage.shape)\n",
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"adjustedtensorizedimage = tensorize(grayscaler(t.ToPILImage()(tensorizedimage)))\n",
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"print(adjustedtensorizedimage.shape)\n",
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"rotation = model(adjustedtensorizedimage).item()\n",
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"print(rotation)\n",
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"rotatedimage = t.Resize(size=1000)(tvf.rotate(adjustedtensorizedimage, rotation))\n",
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"# imS = mf.ResizeWithAspectRatio(filereadimage, 1000)\n",
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"# imS = cv2.resize(filereadimage, (960, 540)) \n",
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"open_cv_image = np.array(t.ToPILImage()(rotatedimage))\n",
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"cv2.imshow(f'image', open_cv_image)\n",
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"key = cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"index = 0\n",
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"active_dataset = working_dataset['test']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# plt.imshow(t.ToPILImage()(working_dataset['test'][3]['image']), cmap='gray', vmin=0, vmax=255)\n",
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"# plt.show()\n",
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"# rotationapplier = model(working_dataset['test'][3]['image']).item()\n",
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"# print(rotationapplier)\n",
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"# img = tvf.rotate(working_dataset['test'][3]['image'], rotationapplier)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# plt.imshow(t.ToPILImage()(img), cmap='gray', vmin=0, vmax=255)\n",
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"# plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# # To call the model on a bunch of the images and rotate them back\n",
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"\n",
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"# while(True):\n",
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"# activeimage = active_dataset[index]['image']\n",
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"# # img = cv2.imread(active_dataset[index]['image'], 0)\n",
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"# activeimage = tvf.rotate(activeimage, model(activeimage).item())\n",
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"# open_cv_image = np.array(t.ToPILImage()(activeimage))\n",
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"# print(index)\n",
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"# cv2.imshow(f'current image', open_cv_image)\n",
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"# key = cv2.waitKey(0)\n",
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"\n",
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"# if key == ord('c'):\n",
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"# print(\"\\tCopying this one\")\n",
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"# elif key == ord('x'):\n",
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"# index -= 1\n",
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"# elif key == ord('v'):\n",
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"# index +=1\n",
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"# elif key == ord('q'):\n",
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"# break\n",
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"\n",
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"# cv2.destroyAllWindows()\n",
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"# cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# # for trying to call the model on the picture repeatedly to see if it will just get more and more straight if it's called multiple times\n",
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"\n",
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"# currentimage = working_dataset['test'][3]['image']\n",
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"# while(True):\n",
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"# rotationapplier = model(currentimage).item()\n",
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"# print(rotationapplier)\n",
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"# img = tvf.rotate(currentimage, rotationapplier)\n",
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"# open_cv_image = np.array(t.ToPILImage()(img))\n",
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"# cv2.imshow(f'current image', open_cv_image)\n",
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"# key = cv2.waitKey(0)\n",
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" \n",
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"# if key == ord('q'):\n",
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"# break\n",
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"# elif key == ord('v'):\n",
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"# currentimage = img\n",
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"# # cv2.destroyAllWindows()\n",
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"# cv2.destroyAllWindows()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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