├── .gitignore ├── COPYING ├── README.md ├── centroidnet ├── __init__.py ├── backbones │ ├── LICENSE │ ├── __init__.py │ └── unet.py ├── centroidnet_core.py └── dataloaders.py ├── config.py ├── dataset ├── LICENSE.txt ├── training │ ├── PotatoPlant0.png │ ├── PotatoPlant0.xml │ ├── PotatoPlant1024.png │ ├── PotatoPlant1024.xml │ ├── PotatoPlant1187.png │ ├── PotatoPlant1187.xml │ ├── PotatoPlant1321.png │ ├── PotatoPlant1321.xml │ ├── PotatoPlant417.png │ └── PotatoPlant417.xml └── validation │ ├── PotatoPlant143.png │ ├── PotatoPlant143.xml │ ├── PotatoPlant297.png │ ├── PotatoPlant297.xml │ ├── PotatoPlant552.png │ └── PotatoPlant552.xml ├── misc └── create_dataset.py ├── predict.py └── train.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Compiled source # 2 | ################### 3 | *.com 4 | *.class 5 | *.dll 6 | *.exe 7 | *.o 8 | *.so 9 | *.pyc 10 | 11 | # Packages # 12 | ############ 13 | *.7z 14 | *.dmg 15 | *.gz 16 | *.iso 17 | *.rar 18 | *.tar 19 | *.zip 20 | 21 | # Logs and databases # 22 | ###################### 23 | *.log 24 | *.sqlite 25 | 26 | # OS generated files # 27 | ###################### 28 | .DS_Store 29 | ehthumbs.db 30 | Icon 31 | Thumbs.db 32 | .tmtags 33 | .idea 34 | **/.idea 35 | tags 36 | vendor.tags 37 | tmtagsHistory 38 | *.sublime-project 39 | *.sublime-workspace 40 | .bundle 41 | 42 | # Package files # 43 | ################# 44 | build 45 | centroidnet.egg.info 46 | dist 47 | centroidnet/**/__pycache__ 48 | 49 | # clion files # 50 | cmake-build-* 51 | 52 | # other stuff # 53 | .RData 54 | data/ -------------------------------------------------------------------------------- /COPYING: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Getting started with OpenCentroidNet 2 | CentroidNet is a hybrid convolutional neural network 3 | 4 | 1) run **create_dataset.py** to generate a synthetic dataset of your liking. 5 | 2) run **train.py** to train ad model using this generated dataset. 6 | 3) run **predict.py** to predict a single image in production. 7 | 4) adjust **config.py** to change hyper parameters. 8 | 9 | # Generated data files and folders 10 | ## The input images and annotation data 11 | **./data/dataset/** contains the image generated by **create_dataset.py** and the annotations in **train.csv** and **validation.csv**.\ 12 | Replace this data with your own set. 13 | 14 | ## Aerial Potato Dataset 15 | The images of the original paper can be found in **./dataset/** 16 | 17 | ## The model 18 | The weights of the CentroidNet model are stored in **./data/CentroidNet.pth** 19 | 20 | ## The input tensors to the model 21 | **./data/validation_result/_inputs.npy** contains the normalized input tensor.\ 22 | **./data/validation_result/_targets.npy** contains the normalized target tensor. 23 | 24 | ## The output tensors of the model 25 | **./data/validation_result/_vectors.npy** contains the normalized 2-d voting vectors (use this to see the quality of the training).\ 26 | **./data/validation_result/_votes.npy** contains the voting space (use this to tune Config.centroid_threshold and Config.nm-size).\ 27 | **./data/validation_result/_centroids.npy** contains a value of one for each centroid (final result).\ 28 | **./data/validation_result/_class_ids.npy** contains the class ids for every pixel.\ 29 | **./data/validation_result/_class_probs.npy** contains the class probability for every pixel.\ 30 | **./data/valitation_result/validation.txt** contains the final centroid coordinates and class info. 31 | 32 | # Citing OpenCentroidNet 33 | 34 | If this code benefits your research please cite: 35 | 36 | @inproceedings{dijkstra2018centroidnet,\ 37 |   title={CentroidNet: A deep neural network for joint object localization and counting},\ 38 |   author={Dijkstra, Klaas and van de Loosdrecht, Jaap and Schomaker, L.R.B. and Wiering, Marco A.},\ 39 |   booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},\ 40 |   pages={585--601},\ 41 |   year={2018},\ 42 |   organization={Springer}\ 43 | } 44 | 45 | # Copyright notice 46 | Copyright (C) 2019 Klaas Dijkstra 47 | 48 | OpenCentroidNet is free software: you can redistribute it and/or modify 49 | it under the terms of the GNU General Public License as published by 50 | the Free Software Foundation, either version 3 of the License, or 51 | (at your option) any later version. 52 | 53 | This program is distributed in the hope that it will be useful, 54 | but WITHOUT ANY WARRANTY; without even the implied warranty of 55 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 56 | GNU General Public License for more details. 57 | 58 | You should have received a copy of the GNU General Public License 59 | along with this program. If not, see . 60 | 61 | The Aerial Potato Dataset is licensed under CC BY-NC-SA 4.0. 62 | 63 | # p.s. 64 | For a numpy viewer go to: https://github.com/ArendJanKramer/Numpyviewer. 65 | 66 | 67 | 68 | 69 | -------------------------------------------------------------------------------- /centroidnet/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (C) 2019 Klaas Dijkstra 2 | # 3 | # This file is part of OpenCentroidNet. 4 | # 5 | # OpenCentroidNet is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | 10 | # OpenCentroidNet is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | 15 | # You should have received a copy of the GNU General Public License 16 | # along with OpenCentroidNet. If not, see . 17 | 18 | from .dataloaders import * 19 | from .backbones import * 20 | from .centroidnet_core import * 21 | -------------------------------------------------------------------------------- /centroidnet/backbones/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Jackson Huang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /centroidnet/backbones/__init__.py: -------------------------------------------------------------------------------- 1 | from .unet import UNet 2 | -------------------------------------------------------------------------------- /centroidnet/backbones/unet.py: -------------------------------------------------------------------------------- 1 | #source: https://github.com/jaxony/unet-pytorch/blob/master/model.py 2 | 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | from torch.nn import init 7 | import numpy as np 8 | 9 | 10 | def conv3x3(in_channels, out_channels, stride=1, 11 | padding=1, bias=True, groups=1): 12 | return nn.Conv2d( 13 | in_channels, 14 | out_channels, 15 | kernel_size=3, 16 | stride=stride, 17 | padding=padding, 18 | bias=bias, 19 | groups=groups) 20 | 21 | 22 | def upconv2x2(in_channels, out_channels, mode='transpose'): 23 | if mode == 'transpose': 24 | return nn.ConvTranspose2d( 25 | in_channels, 26 | out_channels, 27 | kernel_size=2, 28 | stride=2) 29 | else: 30 | # out_channels is always going to be the same 31 | # as in_channels 32 | return nn.Sequential( 33 | nn.Upsample(mode='bilinear', scale_factor=2), 34 | conv1x1(in_channels, out_channels)) 35 | 36 | 37 | def conv1x1(in_channels, out_channels, groups=1): 38 | return nn.Conv2d( 39 | in_channels, 40 | out_channels, 41 | kernel_size=1, 42 | groups=groups, 43 | stride=1) 44 | 45 | 46 | class DownConv(nn.Module): 47 | """ 48 | A helper Module that performs 2 convolutions and 1 MaxPool. 49 | A ReLU activation follows each convolution. 50 | """ 51 | 52 | def __init__(self, in_channels, out_channels, pooling=True): 53 | super(DownConv, self).__init__() 54 | 55 | self.in_channels = in_channels 56 | self.out_channels = out_channels 57 | self.pooling = pooling 58 | 59 | self.conv1 = conv3x3(self.in_channels, self.out_channels) 60 | self.conv2 = conv3x3(self.out_channels, self.out_channels) 61 | 62 | if self.pooling: 63 | self.pool = nn.MaxPool2d(kernel_size=2, stride=2) 64 | 65 | def forward(self, x): 66 | x = F.relu(self.conv1(x)) 67 | x = F.relu(self.conv2(x)) 68 | before_pool = x 69 | if self.pooling: 70 | x = self.pool(x) 71 | return x, before_pool 72 | 73 | 74 | class UpConv(nn.Module): 75 | """ 76 | A helper Module that performs 2 convolutions and 1 UpConvolution. 77 | A ReLU activation follows each convolution. 78 | """ 79 | 80 | def __init__(self, in_channels, out_channels, 81 | merge_mode='concat', up_mode='transpose'): 82 | super(UpConv, self).__init__() 83 | 84 | self.in_channels = in_channels 85 | self.out_channels = out_channels 86 | self.merge_mode = merge_mode 87 | self.up_mode = up_mode 88 | 89 | self.upconv = upconv2x2(self.in_channels, self.out_channels, 90 | mode=self.up_mode) 91 | 92 | if self.merge_mode == 'concat': 93 | self.conv1 = conv3x3( 94 | 2 * self.out_channels, self.out_channels) 95 | else: 96 | # num of input channels to conv2 is same 97 | self.conv1 = conv3x3(self.out_channels, self.out_channels) 98 | self.conv2 = conv3x3(self.out_channels, self.out_channels) 99 | 100 | def forward(self, from_down, from_up): 101 | """ Forward pass 102 | Arguments: 103 | from_down: tensor from the encoder pathway 104 | from_up: upconv'd tensor from the decoder pathway 105 | """ 106 | from_up = self.upconv(from_up) 107 | if not np.array_equal(from_up.data.shape, from_down.data.shape): 108 | from_up = F.upsample(from_up, from_down.data.shape[2:], mode="bilinear") 109 | if self.merge_mode == 'concat': 110 | x = torch.cat((from_up, from_down), 1) 111 | else: 112 | x = from_up + from_down 113 | x = F.relu(self.conv1(x)) 114 | x = F.relu(self.conv2(x)) 115 | return x 116 | 117 | 118 | class UNet(nn.Module): 119 | """ `UNet` class is based on https://arxiv.org/abs/1505.04597 120 | The U-Net is a convolutional encoder-decoder neural network. 121 | Contextual spatial information (from the decoding, 122 | expansive pathway) about an input tensor is merged with 123 | information representing the localization of details 124 | (from the encoding, compressive pathway). 125 | Modifications to the original paper: 126 | (1) padding is used in 3x3 convolutions to prevent loss 127 | of border pixels 128 | (2) merging outputs does not require cropping due to (1) 129 | (3) residual connections can be used by specifying 130 | UNet(merge_mode='add') 131 | (4) if non-parametric upsampling is used in the decoder 132 | pathway (specified by upmode='upsample'), then an 133 | additional 1x1 2d convolution occurs after upsampling 134 | to reduce channel dimensionality by a factor of 2. 135 | This channel halving happens with the convolution in 136 | the tranpose convolution (specified by upmode='transpose') 137 | """ 138 | 139 | def __init__(self, num_classes, in_channels=3, depth=5, 140 | start_filts=64, up_mode='transpose', 141 | merge_mode='concat'): 142 | """ 143 | Arguments: 144 | in_channels: int, number of channels in the input tensor. 145 | Default is 3 for RGB images. 146 | depth: int, number of MaxPools in the U-Net. 147 | start_filts: int, number of convolutional filters for the 148 | first conv. 149 | up_mode: string, type of upconvolution. Choices: 'transpose' 150 | for transpose convolution or 'upsample' for nearest neighbour 151 | upsampling. 152 | """ 153 | super(UNet, self).__init__() 154 | 155 | if up_mode in ('transpose', 'upsample'): 156 | self.up_mode = up_mode 157 | else: 158 | raise ValueError("\"{}\" is not a valid mode for " 159 | "upsampling. Only \"transpose\" and " 160 | "\"upsample\" are allowed.".format(up_mode)) 161 | 162 | if merge_mode in ('concat', 'add'): 163 | self.merge_mode = merge_mode 164 | else: 165 | raise ValueError("\"{}\" is not a valid mode for" 166 | "merging up and down paths. " 167 | "Only \"concat\" and " 168 | "\"add\" are allowed.".format(up_mode)) 169 | 170 | # NOTE: up_mode 'upsample' is incompatible with merge_mode 'add' 171 | if self.up_mode == 'upsample' and self.merge_mode == 'add': 172 | raise ValueError("up_mode \"upsample\" is incompatible " 173 | "with merge_mode \"add\" at the moment " 174 | "because it doesn't make sense to use " 175 | "nearest neighbour to reduce " 176 | "depth channels (by half).") 177 | 178 | self.num_classes = num_classes 179 | self.in_channels = in_channels 180 | self.start_filts = start_filts 181 | self.depth = depth 182 | 183 | self.down_convs = [] 184 | self.up_convs = [] 185 | 186 | # create the encoder pathway and add to a list 187 | for i in range(depth): 188 | ins = self.in_channels if i == 0 else outs 189 | outs = self.start_filts * (2 ** i) 190 | pooling = True if i < depth - 1 else False 191 | 192 | down_conv = DownConv(ins, outs, pooling=pooling) 193 | self.down_convs.append(down_conv) 194 | 195 | # create the decoder pathway and add to a list 196 | # - careful! decoding only requires depth-1 blocks 197 | for i in range(depth - 1): 198 | ins = outs 199 | outs = ins // 2 200 | up_conv = UpConv(ins, outs, up_mode=up_mode, 201 | merge_mode=merge_mode) 202 | self.up_convs.append(up_conv) 203 | 204 | self.conv_final = conv1x1(outs, self.num_classes) 205 | 206 | # add the list of modules to current module 207 | self.down_convs = nn.ModuleList(self.down_convs) 208 | self.up_convs = nn.ModuleList(self.up_convs) 209 | 210 | self.reset_params() 211 | 212 | @staticmethod 213 | def weight_init(m): 214 | if isinstance(m, nn.Conv2d): 215 | init.xavier_normal_(m.weight) 216 | init.constant_(m.bias, 0) 217 | 218 | def reset_params(self): 219 | for i, m in enumerate(self.modules()): 220 | self.weight_init(m) 221 | 222 | def forward(self, x): 223 | encoder_outs = [] 224 | 225 | # encoder pathway, save outputs for merging 226 | for i, module in enumerate(self.down_convs): 227 | x, before_pool = module(x) 228 | encoder_outs.append(before_pool) 229 | 230 | for i, module in enumerate(self.up_convs): 231 | before_pool = encoder_outs[-(i + 2)] 232 | x = module(before_pool, x) 233 | 234 | # No softmax is used. This means you need to use 235 | # nn.CrossEntropyLoss is your training script, 236 | # as this module includes a softmax already. 237 | x = self.conv_final(x) 238 | 239 | #x = F.tanh(x) 240 | return x 241 | -------------------------------------------------------------------------------- /centroidnet/centroidnet_core.py: -------------------------------------------------------------------------------- 1 | # Copyright (C) 2019 Klaas Dijkstra 2 | # 3 | # This file is part of OpenCentroidNet. 4 | # 5 | # OpenCentroidNet is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | 10 | # OpenCentroidNet is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | 15 | # You should have received a copy of the GNU General Public License 16 | # along with OpenCentroidNet. If not, see . 17 | 18 | import torch 19 | import torch.nn 20 | import torch.autograd 21 | import torch.nn.modules.loss 22 | import torch.nn.functional as F 23 | from centroidnet.backbones import UNet 24 | import numpy as np 25 | from typing import List 26 | from skimage.draw import ellipse 27 | from skimage.feature import peak_local_max 28 | 29 | class CentroidNet(torch.nn.Module): 30 | def __init__(self, num_classes, num_channels): 31 | torch.nn.Module.__init__(self) 32 | self.backbone = UNet(num_classes=num_classes+2, in_channels=num_channels, depth=5, start_filts=64) 33 | self.num_classes = num_classes 34 | self.num_channels = num_channels 35 | 36 | def forward(self, x: torch.Tensor) -> torch.Tensor: 37 | return self.backbone(x) 38 | 39 | def __str__(self): 40 | return f"CentroidNet: {self.backbone}" 41 | 42 | 43 | class CentroidLoss(torch.nn.Module): 44 | def __init__(self): 45 | torch.nn.Module.__init__(self) 46 | self.loss = 0 47 | 48 | def forward(self, result, target): 49 | loss = F.mse_loss(result, target, size_average=True, reduce=True) 50 | self.loss = loss.item() 51 | return loss 52 | 53 | def __str__(self): 54 | return f"{self.loss}" 55 | 56 | def encode(coords, image_height: int, image_width: int, max_dist: int, num_classes: int): 57 | y_coords, x_coords, _, _, _, _, _ = np.transpose(coords) 58 | 59 | #Encode vectors 60 | target_vectors = calc_vector_distance(y_coords, x_coords, image_height, image_width, max_dist) 61 | if not max_dist is None: 62 | target_vectors /= max_dist 63 | target_vectors = np.transpose(target_vectors, [2, 0, 1]) 64 | 65 | #Encode logits (bounding box is drawn as ellipses) 66 | target_logits = np.zeros((num_classes, image_height, image_width)) 67 | target_logits[0] = 1 68 | for (y, x, ymin, ymax, xmin, xmax, id) in coords: 69 | ymin, ymax, xmin, xmax = min(ymin, ymax), max(ymin, ymax), min(xmin, xmax), max(xmin, xmax) 70 | rr, cc = ellipse(y, x, (ymax - ymin) // 2, (xmax - xmin) // 2, shape=(image_height, image_width)) 71 | target_logits[id + 1][rr, cc] = 1 72 | target_logits[0][rr, cc] = 0 73 | 74 | target = np.concatenate((target_vectors, target_logits)) 75 | return target 76 | 77 | 78 | def decode(input : np.ndarray, max_dist: int, binning: int, nm_size: int, centroid_threshold: int): 79 | _, image_height, image_width = input.shape 80 | centroid_vectors = input[0:2] * max_dist 81 | logits = input[2:] 82 | 83 | #Calculate class ids and class probabilities 84 | class_ids = np.expand_dims(np.argmax(logits, axis=0), axis=0) 85 | sum_logits = np.expand_dims(np.sum(logits, axis=0), axis=0) 86 | class_probs = np.expand_dims(np.max((logits / sum_logits), axis=0), axis=0) 87 | class_probs = np.clip(class_probs, 0, 1) 88 | 89 | # Calculate the centroid images 90 | votes = calc_vote_image(centroid_vectors, binning) 91 | votes_nm = peak_local_max(votes[0], min_distance=nm_size, threshold_abs=centroid_threshold, indices=False) 92 | votes_nm = np.expand_dims(votes_nm, axis=0) 93 | 94 | # Calculate list of centroid statistics 95 | coords = np.transpose(np.where(votes_nm[0] > 0)) 96 | centroids = [[y * binning, x * binning, class_ids[0, y * binning, x * binning] - 1, class_probs[0, y * binning, x * binning]] for (y, x) in coords] 97 | return centroid_vectors, votes, class_ids, class_probs, votes_nm, centroids 98 | 99 | 100 | def calc_vector_distance(y_coords: List[int], x_coords: List[int], image_height: int, image_width: int, max_dist) -> np.ndarray: 101 | assert (len(y_coords) == len(x_coords)), "list of coordinates should be the same" 102 | assert (len(y_coords) > 0), "No centroids in source image" 103 | 104 | # Prepare datastructures 105 | shape = [image_height, image_width] 106 | image_coords = np.indices(shape) 107 | image_coords_planar = np.transpose(image_coords, [1, 2, 0]) 108 | 109 | dist_cube = np.empty([len(y_coords), image_height, image_width]) 110 | vec_cube = np.empty([len(y_coords), image_height, image_width, 2]) 111 | 112 | # Create multichannel image with distances and vectors 113 | for (i, (y, x)) in enumerate(zip(y_coords, x_coords)): 114 | vec = np.array([y, x]) - image_coords_planar 115 | vec_cube[i] = vec 116 | dist = vec ** 2 117 | dist = np.sum(dist, axis=2) 118 | dist = np.sqrt(dist) 119 | dist_cube[i] = dist 120 | 121 | # Get the smallest centroid distance index 122 | dist_ctr_labels = np.argmin(dist_cube, axis=0) 123 | 124 | # Get the smallest distance vectors [h, w, yx] 125 | vec_ctr = vec_cube[dist_ctr_labels, image_coords[0], image_coords[1]] 126 | 127 | # Clip vectors 128 | if not max_dist is None: 129 | active = np.sqrt(np.sum(vec_ctr ** 2, axis=2)) > max_dist 130 | vec_ctr[active, :] = 0 131 | 132 | return vec_ctr 133 | 134 | 135 | def calc_vote_image(centroid_vectors: np.array, f) -> np.ndarray: 136 | channels, height, width = centroid_vectors.shape 137 | 138 | size = np.array(np.array((height, width), dtype=np.float) * ((1/f), (1/f)), dtype=np.int) 139 | indices = np.indices((height, width), dtype=centroid_vectors.dtype) 140 | 141 | # Calculate absolute vectors 142 | vectors = ((centroid_vectors + indices) * (1/f)).astype(np.int) 143 | nimage = np.zeros((size[0], size[1])) 144 | 145 | # Clip pixels 146 | logic = np.logical_and(np.logical_and(vectors[0] >= 0, vectors[1] >= 0), np.logical_and(vectors[0] < size[0], vectors[1] < size[1])) 147 | coords = vectors[:, logic] 148 | 149 | # Accumulate 150 | np.add.at(nimage, (coords[0], coords[1]), 1) 151 | return np.expand_dims(nimage, axis=0) 152 | -------------------------------------------------------------------------------- /centroidnet/dataloaders.py: -------------------------------------------------------------------------------- 1 | # Copyright (C) 2019 Klaas Dijkstra 2 | # 3 | # This file is part of OpenCentroidNet. 4 | # 5 | # OpenCentroidNet is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | 10 | # OpenCentroidNet is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | 15 | # You should have received a copy of the GNU General Public License 16 | # along with OpenCentroidNet. If not, see . 17 | 18 | import os 19 | from torch.utils.data import Dataset 20 | import numpy as np 21 | import cv2 22 | import random 23 | import centroidnet 24 | 25 | class CentroidNetDataset(Dataset): 26 | """ 27 | CentroidNetDataset Dataset 28 | Load centroids from txt file and apply vector aware data augmentation 29 | 30 | Arguments: 31 | filename: filename of the input data format: (image_file_name,xmin,ymin,xmax,ymax,class_id\lf) 32 | crop (h, w): Random crop size 33 | transpose ((dim2, dim3)): List of random transposes to choose from 34 | stride ((dim2, dim3)): List of random strides to choose from 35 | """ 36 | def convert_path(self, path): 37 | if os.path.isabs(path): 38 | return path 39 | else: 40 | return os.path.join(self.data_path, path) 41 | 42 | def load_and_convert_data(self, filename, max_dist=None): 43 | with open(filename) as f: 44 | lines = f.readlines() 45 | lines = [x.strip().split(",") for x in lines] 46 | self.count = len(lines) 47 | img_ctrs = {} 48 | for line in lines: 49 | fn, xmin, xmax, ymin, ymax, id = line 50 | xmin, xmax, ymin, ymax, id = int(xmin), int(xmax), int(ymin), int(ymax), int(id) 51 | x, y = (xmin + xmax) // 2, (ymin + ymax) // 2 52 | if not fn in img_ctrs: 53 | img_ctrs[fn] = list() 54 | 55 | self.num_classes = id+2 if id+2 > self.num_classes else self.num_classes #including background class 56 | img_ctrs[fn].append(np.array([y, x, ymin, ymax, xmin, xmax, id], dtype=int)) 57 | 58 | for key in img_ctrs.keys(): 59 | img_ctrs[key] = np.stack(img_ctrs[key]) 60 | fn = self.convert_path(key) 61 | img = cv2.imread(fn) 62 | 63 | if img is None: 64 | raise Exception("Could not read {}".format(fn)) 65 | 66 | crop = min(img.shape[0], img.shape[1], self.crop[0], self.crop[1]) 67 | if crop != self.crop[0]: 68 | print(f"Warning: random crop adjusted to {[crop, crop]}") 69 | self.set_crop([crop, crop]) 70 | 71 | target = centroidnet.encode(img_ctrs[key], img.shape[0], img.shape[1], max_dist, self.num_classes) 72 | img = (np.transpose(img, [2, 0, 1]).astype(np.float32) - self.sub) / self.div 73 | target = target.astype(np.float32) 74 | 75 | self.images.append(img) 76 | self.targets.append(target) 77 | 78 | 79 | def __init__(self, filename: str, crop=(256, 256), max_dist=100, repeat=1, sub=127, div=256, transpose=np.array([[0, 1], [1, 0]]), stride=np.array([[1, 1], [-1, -1], [-1, 1], [1, -1]]), data_path=None): 80 | self.count = 0 81 | if data_path is None: 82 | self.data_path = os.path.dirname(filename) 83 | else: 84 | self.data_path = data_path 85 | 86 | self.filename = filename 87 | self.images = list() 88 | self.targets = list() 89 | 90 | self.sub = sub 91 | self.div = div 92 | self.repeat = repeat 93 | self.set_crop(crop) 94 | self.set_repeat(repeat) 95 | self.set_transpose(transpose) 96 | self.set_stride(stride) 97 | self.num_classes = 0 98 | self.train() 99 | self.load_and_convert_data(filename, max_dist=max_dist) 100 | 101 | def eval(self): 102 | self.train_mode = False 103 | 104 | def train(self): 105 | self.train_mode = True 106 | 107 | def set_repeat(self, repeat): 108 | if repeat < 0: 109 | self.repeat = 1 110 | self.repeat = repeat 111 | 112 | def set_crop(self, crop): 113 | self.crop = crop 114 | 115 | def set_transpose(self, transpose): 116 | if np.all(transpose == np.array([[0, 1]])): 117 | self.transpose = None 118 | else: 119 | self.transpose = transpose 120 | 121 | def set_stride(self, stride): 122 | if np.all(stride == np.array([[1, 1]])): 123 | self.stride = None 124 | else: 125 | self.stride = stride 126 | 127 | def adjust_vectors(self, img, transpose, stride): 128 | if not transpose is None: 129 | img2 = img.copy() 130 | img[0] = img2[transpose[0]] 131 | img[1] = img2[transpose[1]] 132 | if not stride is None: 133 | img2 = img.copy() 134 | img[0] = img2[0] * stride[0] 135 | img[1] = img2[1] * stride[1] 136 | return img 137 | 138 | def adjust_image(self, img, transpose, slice, crop, stride): 139 | if not transpose is None: 140 | img = np.transpose(img, (0, transpose[0] + 1, transpose[1] + 1)) 141 | if not slice is None: 142 | img = img[:, slice[0]:slice[0] + crop[0], slice[1]:slice[1] + crop[1]] 143 | if not stride is None: 144 | img = img[:, ::stride[0], ::stride[1]] 145 | return img 146 | 147 | def get_target(self, img: np.array, transpose, slice, crop, stride): 148 | img[0:2] = self.adjust_vectors(img[0:2], transpose, stride) 149 | img = self.adjust_image(img, transpose, slice, crop, stride) 150 | return img 151 | 152 | def get_input(self, img: np.array, transpose, slice, crop, stride): 153 | img = self.adjust_image(img, transpose, slice, crop, stride) 154 | return img 155 | 156 | def __getitem__(self, index): 157 | index = index // self.repeat 158 | input, target = self.images[index], self.targets[index] 159 | 160 | if self.stride is None and self.transpose is None and self.crop is None or not self.train_mode: 161 | return input.astype(np.float32), target.astype(np.float32) 162 | 163 | if not self.transpose is None: 164 | transpose = random.choice(self.transpose) 165 | else: 166 | transpose = None 167 | 168 | if not self.stride is None: 169 | stride = random.choice(self.stride) 170 | else: 171 | stride = None 172 | 173 | if not self.crop is None: 174 | min = np.array([0, 0]) 175 | if not transpose is None: 176 | max = np.array([input.shape[transpose[0] + 1], input.shape[transpose[1] + 1]], dtype=int) - self.crop 177 | else: 178 | max = np.array([input.shape[1] - self.crop[0], input.shape[2] - self.crop[1]]) 179 | slice = [random.randint(mn, mx) for mn, mx in zip(min, max)] 180 | else: 181 | slice = None 182 | 183 | input = self.get_input(input, transpose, slice, self.crop, stride).astype(np.float32) 184 | target = self.get_target(target, transpose, slice, self.crop, stride).astype(np.float32) 185 | 186 | return input, target 187 | 188 | def __len__(self): 189 | return len(self.images) * self.repeat 190 | -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | # Copyright (C) 2019 Klaas Dijkstra 2 | # 3 | # This file is part of OpenCentroidNet. 4 | # 5 | # OpenCentroidNet is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | 10 | # OpenCentroidNet is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | 15 | # You should have received a copy of the GNU General Public License 16 | # along with OpenCentroidNet. If not, see . 17 | 18 | class Config: 19 | dev = "cuda:0" 20 | 21 | # Random crops to take from the image during training and data augmentation. 22 | # This value should be large enough compared to your image size (in this case image size was (200 X 300) pixels. 23 | crop = [100, 100] 24 | 25 | # This should reflect the mean and average in your dataset (or leave default for 8 bit images) 26 | sub = 127 27 | div = 256 28 | 29 | # Maximum allowed voting vector length. All votes are divided by this value during training. 30 | # This value should roughly be twice the max diameter of an object. 31 | max_dist = 30 32 | 33 | # Number of epochs to train. The best model with the best validation loss is kept automatically. 34 | # This value should typically be large. Check vectors.npy if the quality of vectors is ok. 35 | epochs = 500 36 | 37 | # Batch size for training. Choose a batch size which maximizes GPU memory usage. 38 | batch_size = 20 39 | 40 | # Learning rate. Usually this value is sufficient. 41 | learn_rate = 0.001 42 | 43 | # Determines on what interval validation should occur. 44 | validation_interval = 10 45 | 46 | # Number of input channels of the image. Default is RGB. 47 | num_channels = 3 48 | 49 | # Number of classes. This is including the background, so this should be one more then in the training file. 50 | num_classes = 4 51 | 52 | # The amount of spatial binning to use (Values could be: 1, 2, 4, etc.) 53 | # Increase binning for increasing the robustness of the detection. 54 | # Decrease binning to increase spatial accuracy. 55 | binning = 1 56 | 57 | # How far apart should two centroids minimally be. (values could be: 3, 7, 11, 15, 17, etc.) 58 | # Increase value to get less detection close together. 59 | # Decrease value to improve detection which are very close together. 60 | nm_size = 3 61 | 62 | # How many votes constitutes a centroid. 63 | # Increase value to increase precision and decrease recall (less detections) 64 | # Decrease value to increase recall and decrease precision (more detectons) 65 | # Determine a correct value by reviewing votes.npy 66 | centroid_threshold = 10 67 | -------------------------------------------------------------------------------- /dataset/LICENSE.txt: -------------------------------------------------------------------------------- 1 | Aerial Potato Dataset (c) by K. Dijkstra 2 | 3 | Aerial Potato Dataset is licensed under a 4 | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 5 | 6 | You should have received a copy of the license along with this 7 | work. If not, see . 8 | 9 | If this datasets benefits your research please cite: 10 | 11 | @inproceedings{dijkstra2018centroidnet, 12 | title={CentroidNet: A deep neural network for joint object localization and counting}, 13 | author={Dijkstra, Klaas and van de Loosdrecht, Jaap and Schomaker, L.R.B. and Wiering, Marco A.}, 14 | booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases}, 15 | pages={585--601}, 16 | year={2018}, 17 | organization={Springer} 18 | } 19 | 20 | or 21 | 22 | @article{dijkstra2020centroidnetv2, 23 | title={CentroidNetV2: A Hybrid Deep Neural Network for Small-Object Segmentation and Counting.}, 24 | author={Dijkstra, Klaas and van de Loosdrecht, Jaap and Waatze A., Atsma and Schomaker, L.R.B. and Wiering, Marco A.}, 25 | booktitle={Neurocomputing}, 26 | year={2020}, 27 | organization={Elsevier} 28 | DOI={https://doi.org/10.1016/j.neucom.2020.10.075 } 29 | } -------------------------------------------------------------------------------- /dataset/training/PotatoPlant0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kdijkstra13/OpenCentroidNet/a491477351f050f9713072dd1a218314f5580f7a/dataset/training/PotatoPlant0.png -------------------------------------------------------------------------------- /dataset/training/PotatoPlant1024.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kdijkstra13/OpenCentroidNet/a491477351f050f9713072dd1a218314f5580f7a/dataset/training/PotatoPlant1024.png -------------------------------------------------------------------------------- /dataset/training/PotatoPlant1024.xml: -------------------------------------------------------------------------------- 1 | 2 | Kiem Drone 3 | PotatoPlant1024.png 4 | F:\Onedrive\Documenten\Kiem Drone\PotatoPlant1024.png 5 | 6 | Unknown 7 | 8 | 9 | 1800 10 | 1500 11 | 3 12 | 13 | 0 14 | 15 | PotatoPlant 16 | Unspecified 17 | 1 18 | 0 19 | 20 | 108 21 | 1 22 | 140 23 | 24 24 | 25 | 26 | 27 | PotatoPlant 28 | Unspecified 29 | 1 30 | 0 31 | 32 | 1 33 | 1338 34 | 37 35 | 1376 36 | 37 | 38 | 39 | PotatoPlant 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 114 45 | 1314 46 | 171 47 | 1363 48 | 49 | 50 | 51 | PotatoPlant 52 | Unspecified 53 | 1 54 | 0 55 | 56 | 127 57 | 1436 58 | 202 59 | 1500 60 | 61 | 62 | 63 | PotatoPlant 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 127 69 | 1375 70 | 190 71 | 1427 72 | 73 | 74 | 75 | PotatoPlant 76 | Unspecified 77 | 1 78 | 0 79 | 80 | 1 81 | 1413 82 | 35 83 | 1452 84 | 85 | 86 | 87 | PotatoPlant 88 | Unspecified 89 | 1 90 | 0 91 | 92 | 1 93 | 1473 94 | 56 95 | 1496 96 | 97 | 98 | 99 | PotatoPlant 100 | Unspecified 101 | 1 102 | 0 103 | 104 | 1 105 | 1380 106 | 38 107 | 1410 108 | 109 | 110 | 111 | PotatoPlant 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 89 117 | 1135 118 | 144 119 | 1187 120 | 121 | 122 | 123 | PotatoPlant 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 86 129 | 1094 130 | 137 131 | 1133 132 | 133 | 134 | 135 | PotatoPlant 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 83 141 | 1037 142 | 140 143 | 1084 144 | 145 | 146 | 147 | PotatoPlant 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 86 153 | 989 154 | 133 155 | 1025 156 | 157 | 158 | 159 | PotatoPlant 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 214 165 | 962 166 | 265 167 | 1013 168 | 169 | 170 | 171 | PotatoPlant 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 244 177 | 1006 178 | 284 179 | 1048 180 | 181 | 182 | 183 | PotatoPlant 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 216 189 | 1041 190 | 271 191 | 1097 192 | 193 | 194 | 195 | PotatoPlant 196 | Unspecified 197 | 0 198 | 0 199 | 200 | 229 201 | 1090 202 | 292 203 | 1157 204 | 205 | 206 | 207 | PotatoPlant 208 | Unspecified 209 | 0 210 | 0 211 | 212 | 356 213 | 943 214 | 403 215 | 990 216 | 217 | 218 | 219 | PotatoPlant 220 | Unspecified 221 | 0 222 | 0 223 | 224 | 368 225 | 995 226 | 422 227 | 1038 228 | 229 | 230 | 231 | PotatoPlant 232 | Unspecified 233 | 0 234 | 0 235 | 236 | 371 237 | 1033 238 | 430 239 | 1083 240 | 241 | 242 | 243 | PotatoPlant 244 | Unspecified 245 | 0 246 | 0 247 | 248 | 379 249 | 1079 250 | 430 251 | 1129 252 | 253 | 254 | 255 | PotatoPlant 256 | Unspecified 257 | 0 258 | 0 259 | 260 | 505 261 | 938 262 | 551 263 | 984 264 | 265 | 266 | 267 | PotatoPlant 268 | Unspecified 269 | 0 270 | 0 271 | 272 | 524 273 | 1056 274 | 573 275 | 1103 276 | 277 | 278 | 279 | PotatoPlant 280 | Unspecified 281 | 0 282 | 0 283 | 284 | 522 285 | 1010 286 | 583 287 | 1058 288 | 289 | 290 | 291 | PotatoPlant 292 | Unspecified 293 | 0 294 | 0 295 | 296 | 510 297 | 983 298 | 555 299 | 1030 300 | 301 | 302 | 303 | PotatoPlant 304 | Unspecified 305 | 0 306 | 0 307 | 308 | 254 309 | 1288 310 | 302 311 | 1337 312 | 313 | 314 | 315 | PotatoPlant 316 | Unspecified 317 | 0 318 | 0 319 | 320 | 273 321 | 1336 322 | 329 323 | 1378 324 | 325 | 326 | 327 | PotatoPlant 328 | Unspecified 329 | 0 330 | 0 331 | 332 | 276 333 | 1384 334 | 314 335 | 1420 336 | 337 | 338 | 339 | PotatoPlant 340 | Unspecified 341 | 0 342 | 0 343 | 344 | 276 345 | 1417 346 | 341 347 | 1475 348 | 349 | 350 | 351 | PotatoPlant 352 | Unspecified 353 | 0 354 | 0 355 | 356 | 408 357 | 1251 358 | 447 359 | 1287 360 | 361 | 362 | 363 | PotatoPlant 364 | Unspecified 365 | 0 366 | 0 367 | 368 | 563 369 | 1214 370 | 600 371 | 1253 372 | 373 | 374 | 375 | PotatoPlant 376 | Unspecified 377 | 0 378 | 0 379 | 380 | 569 381 | 1277 382 | 600 383 | 1308 384 | 385 | 386 | 387 | PotatoPlant 388 | Unspecified 389 | 0 390 | 0 391 | 392 | 566 393 | 1334 394 | 598 395 | 1361 396 | 397 | 398 | 399 | PotatoPlant 400 | Unspecified 401 | 0 402 | 0 403 | 404 | 587 405 | 1375 406 | 618 407 | 1411 408 | 409 | 410 | 411 | PotatoPlant 412 | Unspecified 413 | 0 414 | 0 415 | 416 | 430 417 | 1402 418 | 486 419 | 1446 420 | 421 | 422 | 423 | PotatoPlant 424 | Unspecified 425 | 0 426 | 0 427 | 428 | 412 429 | 1341 430 | 454 431 | 1375 432 | 433 | 434 | 435 | PotatoPlant 436 | Unspecified 437 | 0 438 | 0 439 | 440 | 402 441 | 1296 442 | 445 443 | 1330 444 | 445 | 446 | 447 | PotatoPlant 448 | Unspecified 449 | 0 450 | 0 451 | 452 | 667 453 | 1145 454 | 730 455 | 1198 456 | 457 | 458 | 459 | PotatoPlant 460 | Unspecified 461 | 0 462 | 0 463 | 464 | 683 465 | 1198 466 | 735 467 | 1239 468 | 469 | 470 | 471 | PotatoPlant 472 | Unspecified 473 | 0 474 | 0 475 | 476 | 692 477 | 1246 478 | 752 479 | 1306 480 | 481 | 482 | 483 | PotatoPlant 484 | Unspecified 485 | 0 486 | 0 487 | 488 | 700 489 | 1306 490 | 761 491 | 1358 492 | 493 | 494 | 495 | PotatoPlant 496 | Unspecified 497 | 0 498 | 0 499 | 500 | 841 501 | 1137 502 | 898 503 | 1188 504 | 505 | 506 | 507 | PotatoPlant 508 | Unspecified 509 | 0 510 | 0 511 | 512 | 822 513 | 1186 514 | 881 515 | 1233 516 | 517 | 518 | 519 | PotatoPlant 520 | Unspecified 521 | 0 522 | 0 523 | 524 | 834 525 | 1241 526 | 882 527 | 1282 528 | 529 | 530 | 531 | PotatoPlant 532 | Unspecified 533 | 0 534 | 0 535 | 536 | 835 537 | 1280 538 | 893 539 | 1330 540 | 541 | 542 | 543 | PotatoPlant 544 | Unspecified 545 | 0 546 | 0 547 | 548 | 796 549 | 881 550 | 854 551 | 939 552 | 553 | 554 | 555 | PotatoPlant 556 | Unspecified 557 | 0 558 | 0 559 | 560 | 793 561 | 946 562 | 827 563 | 976 564 | 565 | 566 | 567 | PotatoPlant 568 | Unspecified 569 | 0 570 | 0 571 | 572 | 812 573 | 969 574 | 859 575 | 1006 576 | 577 | 578 | 579 | PotatoPlant 580 | Unspecified 581 | 0 582 | 0 583 | 584 | 811 585 | 1013 586 | 869 587 | 1072 588 | 589 | 590 | 591 | PotatoPlant 592 | Unspecified 593 | 0 594 | 0 595 | 596 | 934 597 | 840 598 | 981 599 | 887 600 | 601 | 602 | 603 | PotatoPlant 604 | Unspecified 605 | 0 606 | 0 607 | 608 | 936 609 | 914 610 | 976 611 | 957 612 | 613 | 614 | 615 | PotatoPlant 616 | Unspecified 617 | 0 618 | 0 619 | 620 | 948 621 | 953 622 | 993 623 | 990 624 | 625 | 626 | 627 | PotatoPlant 628 | Unspecified 629 | 0 630 | 0 631 | 632 | 939 633 | 1011 634 | 1014 635 | 1069 636 | 637 | 638 | 639 | PotatoPlant 640 | Unspecified 641 | 0 642 | 0 643 | 644 | 982 645 | 1163 646 | 1035 647 | 1210 648 | 649 | 650 | 651 | PotatoPlant 652 | Unspecified 653 | 0 654 | 0 655 | 656 | 990 657 | 1211 658 | 1030 659 | 1257 660 | 661 | 662 | 663 | PotatoPlant 664 | Unspecified 665 | 0 666 | 0 667 | 668 | 1000 669 | 1260 670 | 1049 671 | 1312 672 | 673 | 674 | 675 | PotatoPlant 676 | Unspecified 677 | 0 678 | 0 679 | 680 | 1007 681 | 1317 682 | 1052 683 | 1363 684 | 685 | 686 | 687 | PotatoPlant 688 | Unspecified 689 | 0 690 | 0 691 | 692 | 1006 693 | 1434 694 | 1071 695 | 1495 696 | 697 | 698 | 699 | PotatoPlant 700 | Unspecified 701 | 1 702 | 0 703 | 704 | 1171 705 | 1452 706 | 1220 707 | 1500 708 | 709 | 710 | 711 | PotatoPlant 712 | Unspecified 713 | 0 714 | 0 715 | 716 | 1319 717 | 1412 718 | 1369 719 | 1457 720 | 721 | 722 | 723 | PotatoPlant 724 | Unspecified 725 | 1 726 | 0 727 | 728 | 1471 729 | 1460 730 | 1514 731 | 1500 732 | 733 | 734 | 735 | PotatoPlant 736 | Unspecified 737 | 0 738 | 0 739 | 740 | 1620 741 | 1463 742 | 1655 743 | 1495 744 | 745 | 746 | 747 | PotatoPlant 748 | Unspecified 749 | 0 750 | 0 751 | 752 | 1624 753 | 1396 754 | 1663 755 | 1429 756 | 757 | 758 | 759 | PotatoPlant 760 | Unspecified 761 | 0 762 | 0 763 | 764 | 1602 765 | 1352 766 | 1640 767 | 1383 768 | 769 | 770 | 771 | PotatoPlant 772 | Unspecified 773 | 0 774 | 0 775 | 776 | 1470 777 | 1398 778 | 1505 779 | 1431 780 | 781 | 782 | 783 | PotatoPlant 784 | Unspecified 785 | 0 786 | 0 787 | 788 | 1170 789 | 1389 790 | 1208 791 | 1424 792 | 793 | 794 | 795 | PotatoPlant 796 | Unspecified 797 | 0 798 | 0 799 | 800 | 1300 801 | 1330 802 | 1353 803 | 1375 804 | 805 | 806 | 807 | PotatoPlant 808 | Unspecified 809 | 0 810 | 0 811 | 812 | 1452 813 | 1313 814 | 1493 815 | 1351 816 | 817 | 818 | 819 | PotatoPlant 820 | Unspecified 821 | 0 822 | 0 823 | 824 | 1439 825 | 1269 826 | 1469 827 | 1300 828 | 829 | 830 | 831 | PotatoPlant 832 | Unspecified 833 | 0 834 | 0 835 | 836 | 1600 837 | 1247 838 | 1641 839 | 1294 840 | 841 | 842 | 843 | PotatoPlant 844 | Unspecified 845 | 0 846 | 0 847 | 848 | 1583 849 | 1195 850 | 1613 851 | 1228 852 | 853 | 854 | 855 | PotatoPlant 856 | Unspecified 857 | 0 858 | 0 859 | 860 | 1440 861 | 1163 862 | 1484 863 | 1194 864 | 865 | 866 | 867 | PotatoPlant 868 | Unspecified 869 | 0 870 | 0 871 | 872 | 1575 873 | 1087 874 | 1623 875 | 1136 876 | 877 | 878 | 879 | PotatoPlant 880 | Unspecified 881 | 1 882 | 0 883 | 884 | 1775 885 | 1460 886 | 1797 887 | 1500 888 | 889 | 890 | 891 | PotatoPlant 892 | Unspecified 893 | 0 894 | 0 895 | 896 | 1755 897 | 1417 898 | 1795 899 | 1461 900 | 901 | 902 | 903 | PotatoPlant 904 | Unspecified 905 | 1 906 | 0 907 | 908 | 1770 909 | 1370 910 | 1800 911 | 1398 912 | 913 | 914 | 915 | PotatoPlant 916 | Unspecified 917 | 0 918 | 0 919 | 920 | 1743 921 | 1235 922 | 1783 923 | 1281 924 | 925 | 926 | 927 | PotatoPlant 928 | Unspecified 929 | 0 930 | 0 931 | 932 | 1736 933 | 1168 934 | 1781 935 | 1224 936 | 937 | 938 | 939 | PotatoPlant 940 | Unspecified 941 | 0 942 | 0 943 | 944 | 1721 945 | 1125 946 | 1766 947 | 1163 948 | 949 | 950 | 951 | PotatoPlant 952 | Unspecified 953 | 0 954 | 0 955 | 956 | 1717 957 | 1064 958 | 1762 959 | 1102 960 | 961 | 962 | 963 | PotatoPlant 964 | Unspecified 965 | 0 966 | 0 967 | 968 | 1444 969 | 1221 970 | 1467 971 | 1245 972 | 973 | 974 | 975 | PotatoPlant 976 | Unspecified 977 | 0 978 | 0 979 | 980 | 1311 981 | 1276 982 | 1332 983 | 1300 984 | 985 | 986 | 987 | PotatoPlant 988 | Unspecified 989 | 0 990 | 0 991 | 992 | 1302 993 | 1233 994 | 1330 995 | 1259 996 | 997 | 998 | 999 | PotatoPlant 1000 | Unspecified 1001 | 0 1002 | 0 1003 | 1004 | 1310 1005 | 1209 1006 | 1334 1007 | 1232 1008 | 1009 | 1010 | 1011 | PotatoPlant 1012 | Unspecified 1013 | 0 1014 | 0 1015 | 1016 | 1303 1017 | 1164 1018 | 1325 1019 | 1188 1020 | 1021 | 1022 | 1023 | PotatoPlant 1024 | Unspecified 1025 | 0 1026 | 0 1027 | 1028 | 1168 1029 | 1273 1030 | 1190 1031 | 1299 1032 | 1033 | 1034 | 1035 | PotatoPlant 1036 | Unspecified 1037 | 0 1038 | 0 1039 | 1040 | 1153 1041 | 1214 1042 | 1178 1043 | 1250 1044 | 1045 | 1046 | 1047 | PotatoPlant 1048 | Unspecified 1049 | 0 1050 | 0 1051 | 1052 | 1136 1053 | 1169 1054 | 1172 1055 | 1199 1056 | 1057 | 1058 | 1059 | PotatoPlant 1060 | Unspecified 1061 | 0 1062 | 0 1063 | 1064 | 1291 1065 | 1092 1066 | 1325 1067 | 1126 1068 | 1069 | 1070 | 1071 | PotatoPlant 1072 | Unspecified 1073 | 0 1074 | 0 1075 | 1076 | 1133 1077 | 1118 1078 | 1178 1079 | 1155 1080 | 1081 | 1082 | 1083 | PotatoPlant 1084 | Unspecified 1085 | 0 1086 | 0 1087 | 1088 | 1123 1089 | 1085 1090 | 1147 1091 | 1111 1092 | 1093 | 1094 | 1095 | PotatoPlant 1096 | Unspecified 1097 | 0 1098 | 0 1099 | 1100 | 1110 1101 | 1004 1102 | 1143 1103 | 1040 1104 | 1105 | 1106 | 1107 | PotatoPlant 1108 | Unspecified 1109 | 0 1110 | 0 1111 | 1112 | 1124 1113 | 952 1114 | 1156 1115 | 986 1116 | 1117 | 1118 | 1119 | PotatoPlant 1120 | Unspecified 1121 | 0 1122 | 0 1123 | 1124 | 1116 1125 | 904 1126 | 1151 1127 | 941 1128 | 1129 | 1130 | 1131 | PotatoPlant 1132 | Unspecified 1133 | 0 1134 | 0 1135 | 1136 | 1105 1137 | 822 1138 | 1141 1139 | 851 1140 | 1141 | 1142 | 1143 | PotatoPlant 1144 | Unspecified 1145 | 0 1146 | 0 1147 | 1148 | 1086 1149 | 752 1150 | 1114 1151 | 770 1152 | 1153 | 1154 | 1155 | PotatoPlant 1156 | Unspecified 1157 | 0 1158 | 0 1159 | 1160 | 1096 1161 | 858 1162 | 1117 1163 | 876 1164 | 1165 | 1166 | 1167 | PotatoPlant 1168 | Unspecified 1169 | 0 1170 | 0 1171 | 1172 | 1245 1173 | 840 1174 | 1282 1175 | 874 1176 | 1177 | 1178 | 1179 | PotatoPlant 1180 | Unspecified 1181 | 0 1182 | 0 1183 | 1184 | 1251 1185 | 928 1186 | 1275 1187 | 953 1188 | 1189 | 1190 | 1191 | PotatoPlant 1192 | Unspecified 1193 | 0 1194 | 0 1195 | 1196 | 1266 1197 | 965 1198 | 1300 1199 | 995 1200 | 1201 | 1202 | 1203 | PotatoPlant 1204 | Unspecified 1205 | 0 1206 | 0 1207 | 1208 | 1268 1209 | 999 1210 | 1300 1211 | 1033 1212 | 1213 | 1214 | 1215 | PotatoPlant 1216 | Unspecified 1217 | 0 1218 | 0 1219 | 1220 | 1272 1221 | 1051 1222 | 1297 1223 | 1080 1224 | 1225 | 1226 | 1227 | PotatoPlant 1228 | Unspecified 1229 | 0 1230 | 0 1231 | 1232 | 1401 1233 | 975 1234 | 1449 1235 | 1011 1236 | 1237 | 1238 | 1239 | PotatoPlant 1240 | Unspecified 1241 | 0 1242 | 0 1243 | 1244 | 1420 1245 | 1030 1246 | 1448 1247 | 1055 1248 | 1249 | 1250 | 1251 | PotatoPlant 1252 | Unspecified 1253 | 0 1254 | 0 1255 | 1256 | 1572 1257 | 1013 1258 | 1603 1259 | 1045 1260 | 1261 | 1262 | 1263 | PotatoPlant 1264 | Unspecified 1265 | 0 1266 | 0 1267 | 1268 | 1559 1269 | 959 1270 | 1588 1271 | 986 1272 | 1273 | 1274 | 1275 | PotatoPlant 1276 | Unspecified 1277 | 0 1278 | 0 1279 | 1280 | 1397 1281 | 937 1282 | 1430 1283 | 966 1284 | 1285 | 1286 | 1287 | PotatoPlant 1288 | Unspecified 1289 | 0 1290 | 0 1291 | 1292 | 1396 1293 | 893 1294 | 1421 1295 | 921 1296 | 1297 | 1298 | 1299 | PotatoPlant 1300 | Unspecified 1301 | 0 1302 | 0 1303 | 1304 | 1386 1305 | 802 1306 | 1414 1307 | 840 1308 | 1309 | 1310 | 1311 | PotatoPlant 1312 | Unspecified 1313 | 0 1314 | 0 1315 | 1316 | 1529 1317 | 796 1318 | 1564 1319 | 831 1320 | 1321 | 1322 | 1323 | PotatoPlant 1324 | Unspecified 1325 | 0 1326 | 0 1327 | 1328 | 1537 1329 | 831 1330 | 1570 1331 | 865 1332 | 1333 | 1334 | 1335 | PotatoPlant 1336 | Unspecified 1337 | 0 1338 | 0 1339 | 1340 | 1540 1341 | 873 1342 | 1574 1343 | 907 1344 | 1345 | 1346 | 1347 | PotatoPlant 1348 | Unspecified 1349 | 0 1350 | 0 1351 | 1352 | 1568 1353 | 920 1354 | 1584 1355 | 937 1356 | 1357 | 1358 | 1359 | PotatoPlant 1360 | Unspecified 1361 | 0 1362 | 0 1363 | 1364 | 1694 1365 | 896 1366 | 1737 1367 | 939 1368 | 1369 | 1370 | 1371 | PotatoPlant 1372 | Unspecified 1373 | 0 1374 | 0 1375 | 1376 | 1686 1377 | 835 1378 | 1727 1379 | 876 1380 | 1381 | 1382 | 1383 | PotatoPlant 1384 | Unspecified 1385 | 0 1386 | 0 1387 | 1388 | 1677 1389 | 791 1390 | 1711 1391 | 817 1392 | 1393 | 1394 | 1395 | PotatoPlant 1396 | Unspecified 1397 | 0 1398 | 0 1399 | 1400 | 1662 1401 | 737 1402 | 1707 1403 | 777 1404 | 1405 | 1406 | 1407 | PotatoPlant 1408 | Unspecified 1409 | 0 1410 | 0 1411 | 1412 | 1600 1413 | 1306 1414 | 1632 1415 | 1330 1416 | 1417 | 1418 | 1419 | PotatoPlant 1420 | Unspecified 1421 | 0 1422 | 0 1423 | 1424 | 662 1425 | 928 1426 | 680 1427 | 939 1428 | 1429 | 1430 | 1431 | PotatoPlant 1432 | Unspecified 1433 | 0 1434 | 0 1435 | 1436 | 1383 1437 | 764 1438 | 1414 1439 | 789 1440 | 1441 | 1442 | 1443 | PotatoPlant 1444 | Unspecified 1445 | 0 1446 | 0 1447 | 1448 | 1520 1449 | 736 1450 | 1552 1451 | 769 1452 | 1453 | 1454 | 1455 | PotatoPlant 1456 | Unspecified 1457 | 0 1458 | 0 1459 | 1460 | 1374 1461 | 722 1462 | 1403 1463 | 746 1464 | 1465 | 1466 | 1467 | PotatoPlant 1468 | Unspecified 1469 | 0 1470 | 0 1471 | 1472 | 1524 1473 | 699 1474 | 1557 1475 | 727 1476 | 1477 | 1478 | 1479 | PotatoPlant 1480 | Unspecified 1481 | 0 1482 | 0 1483 | 1484 | 1523 1485 | 653 1486 | 1564 1487 | 692 1488 | 1489 | 1490 | 1491 | PotatoPlant 1492 | Unspecified 1493 | 0 1494 | 0 1495 | 1496 | 1509 1497 | 599 1498 | 1550 1499 | 633 1500 | 1501 | 1502 | 1503 | PotatoPlant 1504 | Unspecified 1505 | 0 1506 | 0 1507 | 1508 | 1496 1509 | 536 1510 | 1531 1511 | 566 1512 | 1513 | 1514 | 1515 | PotatoPlant 1516 | Unspecified 1517 | 0 1518 | 0 1519 | 1520 | 1368 1521 | 680 1522 | 1388 1523 | 702 1524 | 1525 | 1526 | 1527 | PotatoPlant 1528 | Unspecified 1529 | 0 1530 | 0 1531 | 1532 | 1362 1533 | 644 1534 | 1395 1535 | 670 1536 | 1537 | 1538 | 1539 | PotatoPlant 1540 | Unspecified 1541 | 0 1542 | 0 1543 | 1544 | 1353 1545 | 599 1546 | 1395 1547 | 629 1548 | 1549 | 1550 | 1551 | PotatoPlant 1552 | Unspecified 1553 | 0 1554 | 0 1555 | 1556 | 1345 1557 | 526 1558 | 1386 1559 | 580 1560 | 1561 | 1562 | 1563 | PotatoPlant 1564 | Unspecified 1565 | 0 1566 | 0 1567 | 1568 | 1228 1569 | 783 1570 | 1272 1571 | 817 1572 | 1573 | 1574 | 1575 | PotatoPlant 1576 | Unspecified 1577 | 0 1578 | 0 1579 | 1580 | 1231 1581 | 689 1582 | 1266 1583 | 717 1584 | 1585 | 1586 | 1587 | PotatoPlant 1588 | Unspecified 1589 | 0 1590 | 0 1591 | 1592 | 1219 1593 | 742 1594 | 1269 1595 | 776 1596 | 1597 | 1598 | 1599 | PotatoPlant 1600 | Unspecified 1601 | 0 1602 | 0 1603 | 1604 | 1217 1605 | 656 1606 | 1246 1607 | 685 1608 | 1609 | 1610 | 1611 | PotatoPlant 1612 | Unspecified 1613 | 0 1614 | 0 1615 | 1616 | 1213 1617 | 624 1618 | 1246 1619 | 656 1620 | 1621 | 1622 | 1623 | PotatoPlant 1624 | Unspecified 1625 | 0 1626 | 0 1627 | 1628 | 1197 1629 | 581 1630 | 1232 1631 | 607 1632 | 1633 | 1634 | 1635 | PotatoPlant 1636 | Unspecified 1637 | 0 1638 | 0 1639 | 1640 | 1197 1641 | 516 1642 | 1225 1643 | 546 1644 | 1645 | 1646 | 1647 | PotatoPlant 1648 | Unspecified 1649 | 0 1650 | 0 1651 | 1652 | 1338 1653 | 462 1654 | 1371 1655 | 490 1656 | 1657 | 1658 | 1659 | PotatoPlant 1660 | Unspecified 1661 | 0 1662 | 0 1663 | 1664 | 1189 1665 | 470 1666 | 1226 1667 | 504 1668 | 1669 | 1670 | 1671 | PotatoPlant 1672 | Unspecified 1673 | 0 1674 | 0 1675 | 1676 | 1029 1677 | 443 1678 | 1081 1679 | 490 1680 | 1681 | 1682 | 1683 | PotatoPlant 1684 | Unspecified 1685 | 0 1686 | 0 1687 | 1688 | 1042 1689 | 500 1690 | 1091 1691 | 542 1692 | 1693 | 1694 | 1695 | PotatoPlant 1696 | Unspecified 1697 | 0 1698 | 0 1699 | 1700 | 1054 1701 | 563 1702 | 1098 1703 | 597 1704 | 1705 | 1706 | 1707 | PotatoPlant 1708 | Unspecified 1709 | 0 1710 | 0 1711 | 1712 | 1076 1713 | 619 1714 | 1121 1715 | 655 1716 | 1717 | 1718 | 1719 | PotatoPlant 1720 | Unspecified 1721 | 0 1722 | 0 1723 | 1724 | 1069 1725 | 660 1726 | 1116 1727 | 688 1728 | 1729 | 1730 | 1731 | PotatoPlant 1732 | Unspecified 1733 | 0 1734 | 0 1735 | 1736 | 1083 1737 | 694 1738 | 1122 1739 | 729 1740 | 1741 | 1742 | 1743 | PotatoPlant 1744 | Unspecified 1745 | 0 1746 | 0 1747 | 1748 | 1582 1749 | 1148 1750 | 1612 1751 | 1175 1752 | 1753 | 1754 | 1755 | PotatoPlant 1756 | Unspecified 1757 | 0 1758 | 0 1759 | 1760 | 1483 1761 | 435 1762 | 1507 1763 | 459 1764 | 1765 | 1766 | 1767 | PotatoPlant 1768 | Unspecified 1769 | 0 1770 | 0 1771 | 1772 | 1476 1773 | 385 1774 | 1501 1775 | 421 1776 | 1777 | 1778 | 1779 | PotatoPlant 1780 | Unspecified 1781 | 0 1782 | 0 1783 | 1784 | 1460 1785 | 346 1786 | 1510 1787 | 377 1788 | 1789 | 1790 | 1791 | PotatoPlant 1792 | Unspecified 1793 | 0 1794 | 0 1795 | 1796 | 1335 1797 | 413 1798 | 1369 1799 | 440 1800 | 1801 | 1802 | 1803 | PotatoPlant 1804 | Unspecified 1805 | 0 1806 | 0 1807 | 1808 | 1317 1809 | 400 1810 | 1341 1811 | 425 1812 | 1813 | 1814 | 1815 | PotatoPlant 1816 | Unspecified 1817 | 0 1818 | 0 1819 | 1820 | 1317 1821 | 380 1822 | 1336 1823 | 398 1824 | 1825 | 1826 | 1827 | PotatoPlant 1828 | Unspecified 1829 | 0 1830 | 0 1831 | 1832 | 1450 1833 | 287 1834 | 1476 1835 | 318 1836 | 1837 | 1838 | 1839 | PotatoPlant 1840 | Unspecified 1841 | 0 1842 | 0 1843 | 1844 | 1454 1845 | 248 1846 | 1480 1847 | 271 1848 | 1849 | 1850 | 1851 | PotatoPlant 1852 | Unspecified 1853 | 0 1854 | 0 1855 | 1856 | 1437 1857 | 194 1858 | 1478 1859 | 222 1860 | 1861 | 1862 | 1863 | PotatoPlant 1864 | Unspecified 1865 | 0 1866 | 0 1867 | 1868 | 1436 1869 | 121 1870 | 1468 1871 | 149 1872 | 1873 | 1874 | 1875 | PotatoPlant 1876 | Unspecified 1877 | 0 1878 | 0 1879 | 1880 | 1416 1881 | 17 1882 | 1446 1883 | 44 1884 | 1885 | 1886 | 1887 | PotatoPlant 1888 | Unspecified 1889 | 0 1890 | 0 1891 | 1892 | 1431 1893 | 53 1894 | 1458 1895 | 86 1896 | 1897 | 1898 | 1899 | PotatoPlant 1900 | Unspecified 1901 | 0 1902 | 0 1903 | 1904 | 1283 1905 | 255 1906 | 1336 1907 | 284 1908 | 1909 | 1910 | 1911 | PotatoPlant 1912 | Unspecified 1913 | 0 1914 | 0 1915 | 1916 | 1287 1917 | 190 1918 | 1322 1919 | 223 1920 | 1921 | 1922 | 1923 | PotatoPlant 1924 | Unspecified 1925 | 0 1926 | 0 1927 | 1928 | 1291 1929 | 152 1930 | 1329 1931 | 189 1932 | 1933 | 1934 | 1935 | PotatoPlant 1936 | Unspecified 1937 | 0 1938 | 0 1939 | 1940 | 1281 1941 | 103 1942 | 1328 1943 | 145 1944 | 1945 | 1946 | 1947 | PotatoPlant 1948 | Unspecified 1949 | 0 1950 | 0 1951 | 1952 | 1187 1953 | 410 1954 | 1216 1955 | 435 1956 | 1957 | 1958 | 1959 | PotatoPlant 1960 | Unspecified 1961 | 0 1962 | 0 1963 | 1964 | 1184 1965 | 379 1966 | 1211 1967 | 404 1968 | 1969 | 1970 | 1971 | PotatoPlant 1972 | Unspecified 1973 | 0 1974 | 0 1975 | 1976 | 1028 1977 | 417 1978 | 1064 1979 | 440 1980 | 1981 | 1982 | 1983 | PotatoPlant 1984 | Unspecified 1985 | 0 1986 | 0 1987 | 1988 | 1024 1989 | 383 1990 | 1056 1991 | 415 1992 | 1993 | 1994 | 1995 | PotatoPlant 1996 | Unspecified 1997 | 0 1998 | 0 1999 | 2000 | 1167 2001 | 314 2002 | 1206 2003 | 347 2004 | 2005 | 2006 | 2007 | PotatoPlant 2008 | Unspecified 2009 | 0 2010 | 0 2011 | 2012 | 1023 2013 | 326 2014 | 1057 2015 | 353 2016 | 2017 | 2018 | 2019 | PotatoPlant 2020 | Unspecified 2021 | 0 2022 | 0 2023 | 2024 | 1008 2025 | 294 2026 | 1057 2027 | 322 2028 | 2029 | 2030 | 2031 | PotatoPlant 2032 | Unspecified 2033 | 0 2034 | 0 2035 | 2036 | 1003 2037 | 244 2038 | 1042 2039 | 277 2040 | 2041 | 2042 | 2043 | PotatoPlant 2044 | Unspecified 2045 | 0 2046 | 0 2047 | 2048 | 1012 2049 | 218 2050 | 1042 2051 | 239 2052 | 2053 | 2054 | 2055 | PotatoPlant 2056 | Unspecified 2057 | 0 2058 | 0 2059 | 2060 | 1002 2061 | 161 2062 | 1045 2063 | 204 2064 | 2065 | 2066 | 2067 | PotatoPlant 2068 | Unspecified 2069 | 0 2070 | 0 2071 | 2072 | 983 2073 | 89 2074 | 1014 2075 | 117 2076 | 2077 | 2078 | 2079 | PotatoPlant 2080 | Unspecified 2081 | 0 2082 | 0 2083 | 2084 | 983 2085 | 115 2086 | 1010 2087 | 142 2088 | 2089 | 2090 | 2091 | PotatoPlant 2092 | Unspecified 2093 | 0 2094 | 0 2095 | 2096 | 976 2097 | 31 2098 | 1025 2099 | 60 2100 | 2101 | 2102 | 2103 | PotatoPlant 2104 | Unspecified 2105 | 0 2106 | 0 2107 | 2108 | 1353 2109 | 495 2110 | 1370 2111 | 515 2112 | 2113 | 2114 | 2115 | PotatoPlant 2116 | Unspecified 2117 | 0 2118 | 0 2119 | 2120 | 1258 2121 | 24 2122 | 1293 2123 | 76 2124 | 2125 | 2126 | 2127 | PotatoPlant 2128 | Unspecified 2129 | 0 2130 | 0 2131 | 2132 | 1160 2133 | 243 2134 | 1193 2135 | 273 2136 | 2137 | 2138 | 2139 | PotatoPlant 2140 | Unspecified 2141 | 0 2142 | 0 2143 | 2144 | 1152 2145 | 172 2146 | 1191 2147 | 216 2148 | 2149 | 2150 | 2151 | PotatoPlant 2152 | Unspecified 2153 | 0 2154 | 0 2155 | 2156 | 1149 2157 | 125 2158 | 1180 2159 | 148 2160 | 2161 | 2162 | 2163 | PotatoPlant 2164 | Unspecified 2165 | 0 2166 | 0 2167 | 2168 | 1130 2169 | 93 2170 | 1175 2171 | 126 2172 | 2173 | 2174 | 2175 | PotatoPlant 2176 | Unspecified 2177 | 1 2178 | 0 2179 | 2180 | 1119 2181 | 1 2182 | 1160 2183 | 30 2184 | 2185 | 2186 | 2187 | PotatoPlant 2188 | Unspecified 2189 | 1 2190 | 0 2191 | 2192 | 973 2193 | 1 2194 | 1018 2195 | 20 2196 | 2197 | 2198 | 2199 | PotatoPlant 2200 | Unspecified 2201 | 0 2202 | 0 2203 | 2204 | 1167 2205 | 285 2206 | 1185 2207 | 305 2208 | 2209 | 2210 | 2211 | PotatoPlant 2212 | Unspecified 2213 | 0 2214 | 0 2215 | 2216 | 1434 2217 | 153 2218 | 1455 2219 | 173 2220 | 2221 | 2222 | 2223 | PotatoPlant 2224 | Unspecified 2225 | 0 2226 | 0 2227 | 2228 | 1425 2229 | 88 2230 | 1453 2231 | 110 2232 | 2233 | 2234 | 2235 | PotatoPlant 2236 | Unspecified 2237 | 1 2238 | 0 2239 | 2240 | 1254 2241 | 1 2242 | 1281 2243 | 16 2244 | 2245 | 2246 | 2247 | PotatoPlant 2248 | Unspecified 2249 | 0 2250 | 0 2251 | 2252 | 1095 2253 | 777 2254 | 1125 2255 | 799 2256 | 2257 | 2258 | 2259 | PotatoPlant 2260 | Unspecified 2261 | 0 2262 | 0 2263 | 2264 | 3 2265 | 1099 2266 | 16 2267 | 1140 2268 | 2269 | 2270 | 2271 | PotatoPlant 2272 | Unspecified 2273 | 1 2274 | 0 2275 | 2276 | 871 2277 | 1471 2278 | 921 2279 | 1500 2280 | 2281 | 2282 | 2283 | PotatoPlant 2284 | Unspecified 2285 | 0 2286 | 0 2287 | 2288 | 1308 2289 | 275 2290 | 1341 2291 | 310 2292 | 2293 | 2294 | 2295 | -------------------------------------------------------------------------------- /dataset/training/PotatoPlant1187.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kdijkstra13/OpenCentroidNet/a491477351f050f9713072dd1a218314f5580f7a/dataset/training/PotatoPlant1187.png -------------------------------------------------------------------------------- /dataset/training/PotatoPlant1321.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kdijkstra13/OpenCentroidNet/a491477351f050f9713072dd1a218314f5580f7a/dataset/training/PotatoPlant1321.png -------------------------------------------------------------------------------- /dataset/training/PotatoPlant1321.xml: -------------------------------------------------------------------------------- 1 | 2 | Kiem Drone 3 | PotatoPlant1321.png 4 | F:\Onedrive\Documenten\Kiem Drone\PotatoPlant1321.png 5 | 6 | Unknown 7 | 8 | 9 | 1800 10 | 1500 11 | 3 12 | 13 | 0 14 | 15 | PotatoPlant 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 22 21 | 20 22 | 56 23 | 47 24 | 25 | 26 | 27 | PotatoPlant 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 19 33 | 107 34 | 74 35 | 137 36 | 37 | 38 | 39 | PotatoPlant 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 30 45 | 177 46 | 70 47 | 198 48 | 49 | 50 | 51 | PotatoPlant 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 33 57 | 203 58 | 69 59 | 240 60 | 61 | 62 | 63 | PotatoPlant 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 33 69 | 261 70 | 69 71 | 297 72 | 73 | 74 | 75 | PotatoPlant 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 39 81 | 319 82 | 72 83 | 358 84 | 85 | 86 | 87 | PotatoPlant 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 41 93 | 369 94 | 82 95 | 411 96 | 97 | 98 | 99 | PotatoPlant 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 53 105 | 426 106 | 98 107 | 477 108 | 109 | 110 | 111 | PotatoPlant 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 197 117 | 389 118 | 230 119 | 433 120 | 121 | 122 | 123 | PotatoPlant 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 181 129 | 346 130 | 215 131 | 390 132 | 133 | 134 | 135 | PotatoPlant 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 175 141 | 284 142 | 213 143 | 336 144 | 145 | 146 | 147 | PotatoPlant 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 182 153 | 244 154 | 218 155 | 277 156 | 157 | 158 | 159 | PotatoPlant 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 154 165 | 217 166 | 188 167 | 256 168 | 169 | 170 | 171 | PotatoPlant 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 169 177 | 176 178 | 204 179 | 212 180 | 181 | 182 | 183 | PotatoPlant 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 168 189 | 21 190 | 201 191 | 57 192 | 193 | 194 | 195 | PotatoPlant 196 | Unspecified 197 | 0 198 | 0 199 | 200 | 184 201 | 73 202 | 209 203 | 99 204 | 205 | 206 | 207 | PotatoPlant 208 | Unspecified 209 | 0 210 | 0 211 | 212 | 165 213 | 123 214 | 195 215 | 153 216 | 217 | 218 | 219 | PotatoPlant 220 | Unspecified 221 | 0 222 | 0 223 | 224 | 306 225 | 13 226 | 351 227 | 51 228 | 229 | 230 | 231 | PotatoPlant 232 | Unspecified 233 | 0 234 | 0 235 | 236 | 318 237 | 57 238 | 357 239 | 99 240 | 241 | 242 | 243 | PotatoPlant 244 | Unspecified 245 | 0 246 | 0 247 | 248 | 327 249 | 154 250 | 366 251 | 186 252 | 253 | 254 | 255 | PotatoPlant 256 | Unspecified 257 | 0 258 | 0 259 | 260 | 323 261 | 199 262 | 364 263 | 237 264 | 265 | 266 | 267 | PotatoPlant 268 | Unspecified 269 | 0 270 | 0 271 | 272 | 334 273 | 271 274 | 373 275 | 296 276 | 277 | 278 | 279 | PotatoPlant 280 | Unspecified 281 | 0 282 | 0 283 | 284 | 330 285 | 373 286 | 373 287 | 407 288 | 289 | 290 | 291 | PotatoPlant 292 | Unspecified 293 | 0 294 | 0 295 | 296 | 344 297 | 452 298 | 385 299 | 503 300 | 301 | 302 | 303 | PotatoPlant 304 | Unspecified 305 | 0 306 | 0 307 | 308 | 353 309 | 514 310 | 398 311 | 543 312 | 313 | 314 | 315 | PotatoPlant 316 | Unspecified 317 | 0 318 | 0 319 | 320 | 207 321 | 453 322 | 229 323 | 480 324 | 325 | 326 | 327 | PotatoPlant 328 | Unspecified 329 | 0 330 | 0 331 | 332 | 208 333 | 501 334 | 257 335 | 543 336 | 337 | 338 | 339 | PotatoPlant 340 | Unspecified 341 | 0 342 | 0 343 | 344 | 62 345 | 472 346 | 99 347 | 495 348 | 349 | 350 | 351 | PotatoPlant 352 | Unspecified 353 | 0 354 | 0 355 | 356 | 487 357 | 443 358 | 529 359 | 491 360 | 361 | 362 | 363 | PotatoPlant 364 | Unspecified 365 | 0 366 | 0 367 | 368 | 469 369 | 381 370 | 524 371 | 436 372 | 373 | 374 | 375 | PotatoPlant 376 | Unspecified 377 | 0 378 | 0 379 | 380 | 477 381 | 340 382 | 510 383 | 374 384 | 385 | 386 | 387 | PotatoPlant 388 | Unspecified 389 | 0 390 | 0 391 | 392 | 462 393 | 295 394 | 510 395 | 332 396 | 397 | 398 | 399 | PotatoPlant 400 | Unspecified 401 | 0 402 | 0 403 | 404 | 443 405 | 152 406 | 473 407 | 193 408 | 409 | 410 | 411 | PotatoPlant 412 | Unspecified 413 | 0 414 | 0 415 | 416 | 467 417 | 116 418 | 507 419 | 151 420 | 421 | 422 | 423 | PotatoPlant 424 | Unspecified 425 | 0 426 | 0 427 | 428 | 453 429 | 62 430 | 492 431 | 102 432 | 433 | 434 | 435 | PotatoPlant 436 | Unspecified 437 | 0 438 | 0 439 | 440 | 455 441 | 10 442 | 495 443 | 50 444 | 445 | 446 | 447 | PotatoPlant 448 | Unspecified 449 | 0 450 | 0 451 | 452 | 597 453 | 91 454 | 645 455 | 135 456 | 457 | 458 | 459 | PotatoPlant 460 | Unspecified 461 | 0 462 | 0 463 | 464 | 595 465 | 10 466 | 625 467 | 38 468 | 469 | 470 | 471 | PotatoPlant 472 | Unspecified 473 | 0 474 | 0 475 | 476 | 624 477 | 301 478 | 661 479 | 331 480 | 481 | 482 | 483 | PotatoPlant 484 | Unspecified 485 | 0 486 | 0 487 | 488 | 320 489 | 251 490 | 347 491 | 269 492 | 493 | 494 | 495 | PotatoPlant 496 | Unspecified 497 | 0 498 | 0 499 | 500 | 624 501 | 426 502 | 665 503 | 482 504 | 505 | 506 | 507 | PotatoPlant 508 | Unspecified 509 | 0 510 | 0 511 | 512 | 634 513 | 381 514 | 664 515 | 418 516 | 517 | 518 | 519 | PotatoPlant 520 | Unspecified 521 | 0 522 | 0 523 | 524 | 627 525 | 336 526 | 657 527 | 369 528 | 529 | 530 | 531 | PotatoPlant 532 | Unspecified 533 | 0 534 | 0 535 | 536 | 758 537 | 284 538 | 794 539 | 316 540 | 541 | 542 | 543 | PotatoPlant 544 | Unspecified 545 | 0 546 | 0 547 | 548 | 766 549 | 331 550 | 796 551 | 361 552 | 553 | 554 | 555 | PotatoPlant 556 | Unspecified 557 | 0 558 | 0 559 | 560 | 773 561 | 373 562 | 801 563 | 399 564 | 565 | 566 | 567 | PotatoPlant 568 | Unspecified 569 | 0 570 | 0 571 | 572 | 788 573 | 408 574 | 822 575 | 451 576 | 577 | 578 | 579 | PotatoPlant 580 | Unspecified 581 | 0 582 | 0 583 | 584 | 753 585 | 141 586 | 785 587 | 171 588 | 589 | 590 | 591 | PotatoPlant 592 | Unspecified 593 | 0 594 | 0 595 | 596 | 740 597 | 99 598 | 775 599 | 131 600 | 601 | 602 | 603 | PotatoPlant 604 | Unspecified 605 | 0 606 | 0 607 | 608 | 745 609 | 39 610 | 769 611 | 80 612 | 613 | 614 | 615 | PotatoPlant 616 | Unspecified 617 | 1 618 | 0 619 | 620 | 739 621 | 1 622 | 775 623 | 29 624 | 625 | 626 | 627 | PotatoPlant 628 | Unspecified 629 | 0 630 | 0 631 | 632 | 606 633 | 69 634 | 626 635 | 84 636 | 637 | 638 | 639 | PotatoPlant 640 | Unspecified 641 | 1 642 | 0 643 | 644 | 880 645 | 1 646 | 933 647 | 28 648 | 649 | 650 | 651 | PotatoPlant 652 | Unspecified 653 | 0 654 | 0 655 | 656 | 894 657 | 29 658 | 930 659 | 63 660 | 661 | 662 | 663 | PotatoPlant 664 | Unspecified 665 | 0 666 | 0 667 | 668 | 905 669 | 71 670 | 945 671 | 106 672 | 673 | 674 | 675 | PotatoPlant 676 | Unspecified 677 | 0 678 | 0 679 | 680 | 904 681 | 111 682 | 940 683 | 146 684 | 685 | 686 | 687 | PotatoPlant 688 | Unspecified 689 | 0 690 | 0 691 | 692 | 1026 693 | 7 694 | 1057 695 | 35 696 | 697 | 698 | 699 | PotatoPlant 700 | Unspecified 701 | 0 702 | 0 703 | 704 | 1033 705 | 55 706 | 1072 707 | 90 708 | 709 | 710 | 711 | PotatoPlant 712 | Unspecified 713 | 0 714 | 0 715 | 716 | 1027 717 | 97 718 | 1067 719 | 134 720 | 721 | 722 | 723 | PotatoPlant 724 | Unspecified 725 | 0 726 | 0 727 | 728 | 1184 729 | 85 730 | 1229 731 | 120 732 | 733 | 734 | 735 | PotatoPlant 736 | Unspecified 737 | 0 738 | 0 739 | 740 | 1175 741 | 55 742 | 1204 743 | 84 744 | 745 | 746 | 747 | PotatoPlant 748 | Unspecified 749 | 0 750 | 0 751 | 752 | 1059 753 | 253 754 | 1085 755 | 269 756 | 757 | 758 | 759 | PotatoPlant 760 | Unspecified 761 | 0 762 | 0 763 | 764 | 1184 765 | 286 766 | 1213 767 | 307 768 | 769 | 770 | 771 | PotatoPlant 772 | Unspecified 773 | 0 774 | 0 775 | 776 | 1198 777 | 344 778 | 1228 779 | 373 780 | 781 | 782 | 783 | PotatoPlant 784 | Unspecified 785 | 0 786 | 0 787 | 788 | 905 789 | 266 790 | 941 791 | 299 792 | 793 | 794 | 795 | PotatoPlant 796 | Unspecified 797 | 0 798 | 0 799 | 800 | 1060 801 | 321 802 | 1086 803 | 347 804 | 805 | 806 | 807 | PotatoPlant 808 | Unspecified 809 | 0 810 | 0 811 | 812 | 1069 813 | 365 814 | 1085 815 | 381 816 | 817 | 818 | 819 | PotatoPlant 820 | Unspecified 821 | 0 822 | 0 823 | 824 | 907 825 | 318 826 | 957 827 | 363 828 | 829 | 830 | 831 | PotatoPlant 832 | Unspecified 833 | 0 834 | 0 835 | 836 | 920 837 | 358 838 | 958 839 | 402 840 | 841 | 842 | 843 | PotatoPlant 844 | Unspecified 845 | 0 846 | 0 847 | 848 | 925 849 | 402 850 | 974 851 | 455 852 | 853 | 854 | 855 | PotatoPlant 856 | Unspecified 857 | 0 858 | 0 859 | 860 | 46 861 | 529 862 | 89 863 | 570 864 | 865 | 866 | 867 | PotatoPlant 868 | Unspecified 869 | 0 870 | 0 871 | 872 | 66 873 | 581 874 | 93 875 | 608 876 | 877 | 878 | 879 | PotatoPlant 880 | Unspecified 881 | 0 882 | 0 883 | 884 | 72 885 | 638 886 | 105 887 | 682 888 | 889 | 890 | 891 | PotatoPlant 892 | Unspecified 893 | 0 894 | 0 895 | 896 | 215 897 | 651 898 | 241 899 | 679 900 | 901 | 902 | 903 | PotatoPlant 904 | Unspecified 905 | 0 906 | 0 907 | 908 | 207 909 | 576 910 | 243 911 | 624 912 | 913 | 914 | 915 | PotatoPlant 916 | Unspecified 917 | 0 918 | 0 919 | 920 | 207 921 | 545 922 | 237 923 | 577 924 | 925 | 926 | 927 | PotatoPlant 928 | Unspecified 929 | 0 930 | 0 931 | 932 | 354 933 | 543 934 | 396 935 | 573 936 | 937 | 938 | 939 | PotatoPlant 940 | Unspecified 941 | 0 942 | 0 943 | 944 | 358 945 | 583 946 | 392 947 | 615 948 | 949 | 950 | 951 | PotatoPlant 952 | Unspecified 953 | 0 954 | 0 955 | 956 | 355 957 | 634 958 | 398 959 | 675 960 | 961 | 962 | 963 | PotatoPlant 964 | Unspecified 965 | 0 966 | 0 967 | 968 | 371 969 | 736 970 | 405 971 | 780 972 | 973 | 974 | 975 | PotatoPlant 976 | Unspecified 977 | 0 978 | 0 979 | 980 | 503 981 | 642 982 | 536 983 | 682 984 | 985 | 986 | 987 | PotatoPlant 988 | Unspecified 989 | 0 990 | 0 991 | 992 | 509 993 | 682 994 | 541 995 | 711 996 | 997 | 998 | 999 | PotatoPlant 1000 | Unspecified 1001 | 0 1002 | 0 1003 | 1004 | 519 1005 | 737 1006 | 549 1007 | 767 1008 | 1009 | 1010 | 1011 | PotatoPlant 1012 | Unspecified 1013 | 0 1014 | 0 1015 | 1016 | 79 1017 | 721 1018 | 115 1019 | 749 1020 | 1021 | 1022 | 1023 | PotatoPlant 1024 | Unspecified 1025 | 0 1026 | 0 1027 | 1028 | 79 1029 | 757 1030 | 119 1031 | 797 1032 | 1033 | 1034 | 1035 | PotatoPlant 1036 | Unspecified 1037 | 0 1038 | 0 1039 | 1040 | 69 1041 | 827 1042 | 102 1043 | 848 1044 | 1045 | 1046 | 1047 | PotatoPlant 1048 | Unspecified 1049 | 0 1050 | 0 1051 | 1052 | 95 1053 | 848 1054 | 120 1055 | 875 1056 | 1057 | 1058 | 1059 | PotatoPlant 1060 | Unspecified 1061 | 0 1062 | 0 1063 | 1064 | 89 1065 | 972 1066 | 133 1067 | 1024 1068 | 1069 | 1070 | 1071 | PotatoPlant 1072 | Unspecified 1073 | 0 1074 | 0 1075 | 1076 | 96 1077 | 941 1078 | 120 1079 | 965 1080 | 1081 | 1082 | 1083 | PotatoPlant 1084 | Unspecified 1085 | 0 1086 | 0 1087 | 1088 | 99 1089 | 1040 1090 | 135 1091 | 1063 1092 | 1093 | 1094 | 1095 | PotatoPlant 1096 | Unspecified 1097 | 0 1098 | 0 1099 | 1100 | 110 1101 | 1107 1102 | 139 1103 | 1128 1104 | 1105 | 1106 | 1107 | PotatoPlant 1108 | Unspecified 1109 | 0 1110 | 0 1111 | 1112 | 105 1113 | 1135 1114 | 152 1115 | 1168 1116 | 1117 | 1118 | 1119 | PotatoPlant 1120 | Unspecified 1121 | 0 1122 | 0 1123 | 1124 | 105 1125 | 1201 1126 | 135 1127 | 1222 1128 | 1129 | 1130 | 1131 | PotatoPlant 1132 | Unspecified 1133 | 0 1134 | 0 1135 | 1136 | 126 1137 | 1229 1138 | 154 1139 | 1263 1140 | 1141 | 1142 | 1143 | PotatoPlant 1144 | Unspecified 1145 | 0 1146 | 0 1147 | 1148 | 107 1149 | 1266 1150 | 138 1151 | 1305 1152 | 1153 | 1154 | 1155 | PotatoPlant 1156 | Unspecified 1157 | 0 1158 | 0 1159 | 1160 | 124 1161 | 1316 1162 | 153 1163 | 1341 1164 | 1165 | 1166 | 1167 | PotatoPlant 1168 | Unspecified 1169 | 0 1170 | 0 1171 | 1172 | 130 1173 | 1366 1174 | 158 1175 | 1404 1176 | 1177 | 1178 | 1179 | PotatoPlant 1180 | Unspecified 1181 | 0 1182 | 0 1183 | 1184 | 122 1185 | 1428 1186 | 163 1187 | 1467 1188 | 1189 | 1190 | 1191 | PotatoPlant 1192 | Unspecified 1193 | 1 1194 | 0 1195 | 1196 | 6 1197 | 1463 1198 | 48 1199 | 1500 1200 | 1201 | 1202 | 1203 | PotatoPlant 1204 | Unspecified 1205 | 1 1206 | 0 1207 | 1208 | 1 1209 | 1419 1210 | 20 1211 | 1440 1212 | 1213 | 1214 | 1215 | PotatoPlant 1216 | Unspecified 1217 | 1 1218 | 0 1219 | 1220 | 263 1221 | 1468 1222 | 308 1223 | 1500 1224 | 1225 | 1226 | 1227 | PotatoPlant 1228 | Unspecified 1229 | 0 1230 | 0 1231 | 1232 | 251 1233 | 1364 1234 | 292 1235 | 1395 1236 | 1237 | 1238 | 1239 | PotatoPlant 1240 | Unspecified 1241 | 0 1242 | 0 1243 | 1244 | 276 1245 | 1403 1246 | 318 1247 | 1437 1248 | 1249 | 1250 | 1251 | PotatoPlant 1252 | Unspecified 1253 | 0 1254 | 0 1255 | 1256 | 263 1257 | 1315 1258 | 314 1259 | 1354 1260 | 1261 | 1262 | 1263 | PotatoPlant 1264 | Unspecified 1265 | 0 1266 | 0 1267 | 1268 | 264 1269 | 1280 1270 | 292 1271 | 1315 1272 | 1273 | 1274 | 1275 | PotatoPlant 1276 | Unspecified 1277 | 0 1278 | 0 1279 | 1280 | 253 1281 | 1224 1282 | 294 1283 | 1256 1284 | 1285 | 1286 | 1287 | PotatoPlant 1288 | Unspecified 1289 | 0 1290 | 0 1291 | 1292 | 252 1293 | 1159 1294 | 296 1295 | 1187 1296 | 1297 | 1298 | 1299 | PotatoPlant 1300 | Unspecified 1301 | 0 1302 | 0 1303 | 1304 | 250 1305 | 1105 1306 | 276 1307 | 1135 1308 | 1309 | 1310 | 1311 | PotatoPlant 1312 | Unspecified 1313 | 0 1314 | 0 1315 | 1316 | 246 1317 | 1074 1318 | 273 1319 | 1099 1320 | 1321 | 1322 | 1323 | PotatoPlant 1324 | Unspecified 1325 | 0 1326 | 0 1327 | 1328 | 250 1329 | 1046 1330 | 268 1331 | 1065 1332 | 1333 | 1334 | 1335 | PotatoPlant 1336 | Unspecified 1337 | 0 1338 | 0 1339 | 1340 | 231 1341 | 1006 1342 | 272 1343 | 1041 1344 | 1345 | 1346 | 1347 | PotatoPlant 1348 | Unspecified 1349 | 0 1350 | 0 1351 | 1352 | 236 1353 | 855 1354 | 272 1355 | 874 1356 | 1357 | 1358 | 1359 | PotatoPlant 1360 | Unspecified 1361 | 0 1362 | 0 1363 | 1364 | 232 1365 | 883 1366 | 259 1367 | 902 1368 | 1369 | 1370 | 1371 | PotatoPlant 1372 | Unspecified 1373 | 0 1374 | 0 1375 | 1376 | 240 1377 | 788 1378 | 274 1379 | 822 1380 | 1381 | 1382 | 1383 | PotatoPlant 1384 | Unspecified 1385 | 0 1386 | 0 1387 | 1388 | 219 1389 | 751 1390 | 255 1391 | 784 1392 | 1393 | 1394 | 1395 | PotatoPlant 1396 | Unspecified 1397 | 0 1398 | 0 1399 | 1400 | 213 1401 | 698 1402 | 253 1403 | 724 1404 | 1405 | 1406 | 1407 | PotatoPlant 1408 | Unspecified 1409 | 0 1410 | 0 1411 | 1412 | 383 1413 | 845 1414 | 417 1415 | 872 1416 | 1417 | 1418 | 1419 | PotatoPlant 1420 | Unspecified 1421 | 0 1422 | 0 1423 | 1424 | 382 1425 | 947 1426 | 414 1427 | 988 1428 | 1429 | 1430 | 1431 | PotatoPlant 1432 | Unspecified 1433 | 0 1434 | 0 1435 | 1436 | 397 1437 | 1017 1438 | 426 1439 | 1044 1440 | 1441 | 1442 | 1443 | PotatoPlant 1444 | Unspecified 1445 | 0 1446 | 0 1447 | 1448 | 401 1449 | 1055 1450 | 427 1451 | 1075 1452 | 1453 | 1454 | 1455 | PotatoPlant 1456 | Unspecified 1457 | 0 1458 | 0 1459 | 1460 | 383 1461 | 1121 1462 | 417 1463 | 1167 1464 | 1465 | 1466 | 1467 | PotatoPlant 1468 | Unspecified 1469 | 0 1470 | 0 1471 | 1472 | 406 1473 | 1169 1474 | 441 1475 | 1205 1476 | 1477 | 1478 | 1479 | PotatoPlant 1480 | Unspecified 1481 | 0 1482 | 0 1483 | 1484 | 406 1485 | 1211 1486 | 450 1487 | 1246 1488 | 1489 | 1490 | 1491 | PotatoPlant 1492 | Unspecified 1493 | 0 1494 | 0 1495 | 1496 | 412 1497 | 1278 1498 | 444 1499 | 1309 1500 | 1501 | 1502 | 1503 | PotatoPlant 1504 | Unspecified 1505 | 0 1506 | 0 1507 | 1508 | 403 1509 | 1324 1510 | 435 1511 | 1357 1512 | 1513 | 1514 | 1515 | PotatoPlant 1516 | Unspecified 1517 | 0 1518 | 0 1519 | 1520 | 422 1521 | 1398 1522 | 466 1523 | 1452 1524 | 1525 | 1526 | 1527 | PotatoPlant 1528 | Unspecified 1529 | 0 1530 | 0 1531 | 1532 | 560 1533 | 1338 1534 | 632 1535 | 1401 1536 | 1537 | 1538 | 1539 | PotatoPlant 1540 | Unspecified 1541 | 0 1542 | 0 1543 | 1544 | 562 1545 | 1290 1546 | 602 1547 | 1323 1548 | 1549 | 1550 | 1551 | PotatoPlant 1552 | Unspecified 1553 | 0 1554 | 0 1555 | 1556 | 537 1557 | 1199 1558 | 603 1559 | 1276 1560 | 1561 | 1562 | 1563 | PotatoPlant 1564 | Unspecified 1565 | 0 1566 | 0 1567 | 1568 | 542 1569 | 1033 1570 | 587 1571 | 1076 1572 | 1573 | 1574 | 1575 | PotatoPlant 1576 | Unspecified 1577 | 0 1578 | 0 1579 | 1580 | 542 1581 | 975 1582 | 588 1583 | 1006 1584 | 1585 | 1586 | 1587 | PotatoPlant 1588 | Unspecified 1589 | 0 1590 | 0 1591 | 1592 | 536 1593 | 948 1594 | 574 1595 | 971 1596 | 1597 | 1598 | 1599 | PotatoPlant 1600 | Unspecified 1601 | 0 1602 | 0 1603 | 1604 | 520 1605 | 899 1606 | 555 1607 | 945 1608 | 1609 | 1610 | 1611 | PotatoPlant 1612 | Unspecified 1613 | 0 1614 | 0 1615 | 1616 | 571 1617 | 887 1618 | 595 1619 | 917 1620 | 1621 | 1622 | 1623 | PotatoPlant 1624 | Unspecified 1625 | 0 1626 | 0 1627 | 1628 | 680 1629 | 890 1630 | 714 1631 | 917 1632 | 1633 | 1634 | 1635 | PotatoPlant 1636 | Unspecified 1637 | 0 1638 | 0 1639 | 1640 | 686 1641 | 923 1642 | 727 1643 | 959 1644 | 1645 | 1646 | 1647 | PotatoPlant 1648 | Unspecified 1649 | 0 1650 | 0 1651 | 1652 | 694 1653 | 975 1654 | 718 1655 | 1003 1656 | 1657 | 1658 | 1659 | PotatoPlant 1660 | Unspecified 1661 | 0 1662 | 0 1663 | 1664 | 692 1665 | 1018 1666 | 702 1667 | 1052 1668 | 1669 | 1670 | 1671 | PotatoPlant 1672 | Unspecified 1673 | 0 1674 | 0 1675 | 1676 | 712 1677 | 1178 1678 | 746 1679 | 1212 1680 | 1681 | 1682 | 1683 | PotatoPlant 1684 | Unspecified 1685 | 0 1686 | 0 1687 | 1688 | 712 1689 | 1245 1690 | 746 1691 | 1275 1692 | 1693 | 1694 | 1695 | PotatoPlant 1696 | Unspecified 1697 | 0 1698 | 0 1699 | 1700 | 724 1701 | 1298 1702 | 755 1703 | 1314 1704 | 1705 | 1706 | 1707 | PotatoPlant 1708 | Unspecified 1709 | 0 1710 | 0 1711 | 1712 | 729 1713 | 1326 1714 | 772 1715 | 1366 1716 | 1717 | 1718 | 1719 | PotatoPlant 1720 | Unspecified 1721 | 0 1722 | 0 1723 | 1724 | 860 1725 | 1307 1726 | 883 1727 | 1330 1728 | 1729 | 1730 | 1731 | PotatoPlant 1732 | Unspecified 1733 | 0 1734 | 0 1735 | 1736 | 874 1737 | 1176 1738 | 891 1739 | 1194 1740 | 1741 | 1742 | 1743 | PotatoPlant 1744 | Unspecified 1745 | 0 1746 | 0 1747 | 1748 | 835 1749 | 1011 1750 | 874 1751 | 1053 1752 | 1753 | 1754 | 1755 | PotatoPlant 1756 | Unspecified 1757 | 0 1758 | 0 1759 | 1760 | 824 1761 | 965 1762 | 869 1763 | 998 1764 | 1765 | 1766 | 1767 | PotatoPlant 1768 | Unspecified 1769 | 0 1770 | 0 1771 | 1772 | 797 1773 | 862 1774 | 859 1775 | 898 1776 | 1777 | 1778 | 1779 | PotatoPlant 1780 | Unspecified 1781 | 0 1782 | 0 1783 | 1784 | 808 1785 | 710 1786 | 851 1787 | 758 1788 | 1789 | 1790 | 1791 | PotatoPlant 1792 | Unspecified 1793 | 0 1794 | 0 1795 | 1796 | 664 1797 | 728 1798 | 708 1799 | 783 1800 | 1801 | 1802 | 1803 | PotatoPlant 1804 | Unspecified 1805 | 0 1806 | 0 1807 | 1808 | 641 1809 | 680 1810 | 689 1811 | 706 1812 | 1813 | 1814 | 1815 | PotatoPlant 1816 | Unspecified 1817 | 0 1818 | 0 1819 | 1820 | 651 1821 | 619 1822 | 699 1823 | 657 1824 | 1825 | 1826 | 1827 | PotatoPlant 1828 | Unspecified 1829 | 0 1830 | 0 1831 | 1832 | 653 1833 | 560 1834 | 684 1835 | 598 1836 | 1837 | 1838 | 1839 | PotatoPlant 1840 | Unspecified 1841 | 0 1842 | 0 1843 | 1844 | 794 1845 | 562 1846 | 830 1847 | 594 1848 | 1849 | 1850 | 1851 | PotatoPlant 1852 | Unspecified 1853 | 0 1854 | 0 1855 | 1856 | 802 1857 | 609 1858 | 828 1859 | 649 1860 | 1861 | 1862 | 1863 | PotatoPlant 1864 | Unspecified 1865 | 0 1866 | 0 1867 | 1868 | 808 1869 | 673 1870 | 834 1871 | 691 1872 | 1873 | 1874 | 1875 | PotatoPlant 1876 | Unspecified 1877 | 0 1878 | 0 1879 | 1880 | 942 1881 | 550 1882 | 983 1883 | 594 1884 | 1885 | 1886 | 1887 | PotatoPlant 1888 | Unspecified 1889 | 0 1890 | 0 1891 | 1892 | 954 1893 | 600 1894 | 995 1895 | 637 1896 | 1897 | 1898 | 1899 | PotatoPlant 1900 | Unspecified 1901 | 0 1902 | 0 1903 | 1904 | 949 1905 | 657 1906 | 969 1907 | 685 1908 | 1909 | 1910 | 1911 | PotatoPlant 1912 | Unspecified 1913 | 0 1914 | 0 1915 | 1916 | 946 1917 | 695 1918 | 993 1919 | 731 1920 | 1921 | 1922 | 1923 | PotatoPlant 1924 | Unspecified 1925 | 0 1926 | 0 1927 | 1928 | 968 1929 | 831 1930 | 1014 1931 | 882 1932 | 1933 | 1934 | 1935 | PotatoPlant 1936 | Unspecified 1937 | 0 1938 | 0 1939 | 1940 | 966 1941 | 891 1942 | 1015 1943 | 929 1944 | 1945 | 1946 | 1947 | PotatoPlant 1948 | Unspecified 1949 | 0 1950 | 0 1951 | 1952 | 983 1953 | 957 1954 | 1013 1955 | 981 1956 | 1957 | 1958 | 1959 | PotatoPlant 1960 | Unspecified 1961 | 0 1962 | 0 1963 | 1964 | 984 1965 | 997 1966 | 1032 1967 | 1031 1968 | 1969 | 1970 | 1971 | PotatoPlant 1972 | Unspecified 1973 | 0 1974 | 0 1975 | 1976 | 1109 1977 | 698 1978 | 1143 1979 | 740 1980 | 1981 | 1982 | 1983 | PotatoPlant 1984 | Unspecified 1985 | 0 1986 | 0 1987 | 1988 | 1095 1989 | 621 1990 | 1137 1991 | 677 1992 | 1993 | 1994 | 1995 | PotatoPlant 1996 | Unspecified 1997 | 0 1998 | 0 1999 | 2000 | 1232 2001 | 620 2002 | 1267 2003 | 652 2004 | 2005 | 2006 | 2007 | PotatoPlant 2008 | Unspecified 2009 | 0 2010 | 0 2011 | 2012 | 1247 2013 | 659 2014 | 1267 2015 | 686 2016 | 2017 | 2018 | 2019 | PotatoPlant 2020 | Unspecified 2021 | 0 2022 | 0 2023 | 2024 | 1272 2025 | 921 2026 | 1294 2027 | 946 2028 | 2029 | 2030 | 2031 | PotatoPlant 2032 | Unspecified 2033 | 0 2034 | 0 2035 | 2036 | 1296 2037 | 1104 2038 | 1337 2039 | 1138 2040 | 2041 | 2042 | 2043 | PotatoPlant 2044 | Unspecified 2045 | 0 2046 | 0 2047 | 2048 | 1246 2049 | 583 2050 | 1280 2051 | 613 2052 | 2053 | 2054 | 2055 | PotatoPlant 2056 | Unspecified 2057 | 0 2058 | 0 2059 | 2060 | 1086 2061 | 579 2062 | 1113 2063 | 597 2064 | 2065 | 2066 | 2067 | PotatoPlant 2068 | Unspecified 2069 | 0 2070 | 0 2071 | 2072 | 1235 2073 | 499 2074 | 1264 2075 | 526 2076 | 2077 | 2078 | 2079 | PotatoPlant 2080 | Unspecified 2081 | 0 2082 | 0 2083 | 2084 | 1214 2085 | 242 2086 | 1234 2087 | 265 2088 | 2089 | 2090 | 2091 | PotatoPlant 2092 | Unspecified 2093 | 0 2094 | 0 2095 | 2096 | 329 2097 | 296 2098 | 373 2099 | 328 2100 | 2101 | 2102 | 2103 | PotatoPlant 2104 | Unspecified 2105 | 0 2106 | 0 2107 | 2108 | 1314 2109 | 71 2110 | 1338 2111 | 91 2112 | 2113 | 2114 | 2115 | PotatoPlant 2116 | Unspecified 2117 | 0 2118 | 0 2119 | 2120 | 1327 2121 | 106 2122 | 1350 2123 | 131 2124 | 2125 | 2126 | 2127 | PotatoPlant 2128 | Unspecified 2129 | 0 2130 | 0 2131 | 2132 | 1337 2133 | 235 2134 | 1370 2135 | 271 2136 | 2137 | 2138 | 2139 | PotatoPlant 2140 | Unspecified 2141 | 0 2142 | 0 2143 | 2144 | 1355 2145 | 285 2146 | 1403 2147 | 327 2148 | 2149 | 2150 | 2151 | PotatoPlant 2152 | Unspecified 2153 | 0 2154 | 0 2155 | 2156 | 1345 2157 | 320 2158 | 1362 2159 | 346 2160 | 2161 | 2162 | 2163 | PotatoPlant 2164 | Unspecified 2165 | 0 2166 | 0 2167 | 2168 | 1373 2169 | 393 2170 | 1393 2171 | 419 2172 | 2173 | 2174 | 2175 | PotatoPlant 2176 | Unspecified 2177 | 0 2178 | 0 2179 | 2180 | 1384 2181 | 575 2182 | 1411 2183 | 606 2184 | 2185 | 2186 | 2187 | PotatoPlant 2188 | Unspecified 2189 | 0 2190 | 0 2191 | 2192 | 1394 2193 | 610 2194 | 1417 2195 | 642 2196 | 2197 | 2198 | 2199 | PotatoPlant 2200 | Unspecified 2201 | 0 2202 | 0 2203 | 2204 | 1384 2205 | 653 2206 | 1419 2207 | 683 2208 | 2209 | 2210 | 2211 | PotatoPlant 2212 | Unspecified 2213 | 0 2214 | 0 2215 | 2216 | 1276 2217 | 835 2218 | 1298 2219 | 851 2220 | 2221 | 2222 | 2223 | PotatoPlant 2224 | Unspecified 2225 | 0 2226 | 0 2227 | 2228 | 1420 2229 | 815 2230 | 1452 2231 | 851 2232 | 2233 | 2234 | 2235 | PotatoPlant 2236 | Unspecified 2237 | 0 2238 | 0 2239 | 2240 | 1431 2241 | 856 2242 | 1463 2243 | 896 2244 | 2245 | 2246 | 2247 | PotatoPlant 2248 | Unspecified 2249 | 0 2250 | 0 2251 | 2252 | 1414 2253 | 901 2254 | 1434 2255 | 923 2256 | 2257 | 2258 | 2259 | PotatoPlant 2260 | Unspecified 2261 | 0 2262 | 0 2263 | 2264 | 1434 2265 | 961 2266 | 1450 2267 | 989 2268 | 2269 | 2270 | 2271 | PotatoPlant 2272 | Unspecified 2273 | 0 2274 | 0 2275 | 2276 | 1290 2277 | 982 2278 | 1308 2279 | 993 2280 | 2281 | 2282 | 2283 | PotatoPlant 2284 | Unspecified 2285 | 0 2286 | 0 2287 | 2288 | 386 2289 | 877 2290 | 414 2291 | 892 2292 | 2293 | 2294 | 2295 | PotatoPlant 2296 | Unspecified 2297 | 0 2298 | 0 2299 | 2300 | 1296 2301 | 1190 2302 | 1334 2303 | 1227 2304 | 2305 | 2306 | 2307 | PotatoPlant 2308 | Unspecified 2309 | 0 2310 | 0 2311 | 2312 | 1309 2313 | 1239 2314 | 1330 2315 | 1262 2316 | 2317 | 2318 | 2319 | PotatoPlant 2320 | Unspecified 2321 | 0 2322 | 0 2323 | 2324 | 1144 2325 | 1157 2326 | 1171 2327 | 1183 2328 | 2329 | 2330 | 2331 | PotatoPlant 2332 | Unspecified 2333 | 0 2334 | 0 2335 | 2336 | 1153 2337 | 1195 2338 | 1172 2339 | 1215 2340 | 2341 | 2342 | 2343 | PotatoPlant 2344 | Unspecified 2345 | 0 2346 | 0 2347 | 2348 | 1163 2349 | 1243 2350 | 1192 2351 | 1278 2352 | 2353 | 2354 | 2355 | PotatoPlant 2356 | Unspecified 2357 | 0 2358 | 0 2359 | 2360 | 1169 2361 | 1315 2362 | 1197 2363 | 1337 2364 | 2365 | 2366 | 2367 | PotatoPlant 2368 | Unspecified 2369 | 0 2370 | 0 2371 | 2372 | 986 2373 | 1149 2374 | 1046 2375 | 1187 2376 | 2377 | 2378 | 2379 | PotatoPlant 2380 | Unspecified 2381 | 0 2382 | 0 2383 | 2384 | 1008 2385 | 1223 2386 | 1044 2387 | 1256 2388 | 2389 | 2390 | 2391 | PotatoPlant 2392 | Unspecified 2393 | 0 2394 | 0 2395 | 2396 | 1012 2397 | 1264 2398 | 1059 2399 | 1306 2400 | 2401 | 2402 | 2403 | PotatoPlant 2404 | Unspecified 2405 | 0 2406 | 0 2407 | 2408 | 1010 2409 | 1317 2410 | 1063 2411 | 1345 2412 | 2413 | 2414 | 2415 | PotatoPlant 2416 | Unspecified 2417 | 1 2418 | 0 2419 | 2420 | 878 2421 | 1474 2422 | 899 2423 | 1500 2424 | 2425 | 2426 | 2427 | PotatoPlant 2428 | Unspecified 2429 | 1 2430 | 0 2431 | 2432 | 576 2433 | 1472 2434 | 619 2435 | 1500 2436 | 2437 | 2438 | 2439 | PotatoPlant 2440 | Unspecified 2441 | 0 2442 | 0 2443 | 2444 | 224 2445 | 967 2446 | 266 2447 | 1000 2448 | 2449 | 2450 | 2451 | PotatoPlant 2452 | Unspecified 2453 | 0 2454 | 0 2455 | 2456 | 228 2457 | 916 2458 | 285 2459 | 953 2460 | 2461 | 2462 | 2463 | PotatoPlant 2464 | Unspecified 2465 | 0 2466 | 0 2467 | 2468 | 1765 2469 | 56 2470 | 1787 2471 | 83 2472 | 2473 | 2474 | 2475 | PotatoPlant 2476 | Unspecified 2477 | 0 2478 | 0 2479 | 2480 | 1620 2481 | 143 2482 | 1657 2483 | 176 2484 | 2485 | 2486 | 2487 | PotatoPlant 2488 | Unspecified 2489 | 0 2490 | 0 2491 | 2492 | 1633 2493 | 206 2494 | 1668 2495 | 250 2496 | 2497 | 2498 | 2499 | PotatoPlant 2500 | Unspecified 2501 | 1 2502 | 0 2503 | 2504 | 1779 2505 | 233 2506 | 1800 2507 | 254 2508 | 2509 | 2510 | 2511 | PotatoPlant 2512 | Unspecified 2513 | 0 2514 | 0 2515 | 2516 | 1759 2517 | 176 2518 | 1798 2519 | 213 2520 | 2521 | 2522 | 2523 | PotatoPlant 2524 | Unspecified 2525 | 0 2526 | 0 2527 | 2528 | 1639 2529 | 276 2530 | 1671 2531 | 299 2532 | 2533 | 2534 | 2535 | PotatoPlant 2536 | Unspecified 2537 | 0 2538 | 0 2539 | 2540 | 1656 2541 | 320 2542 | 1683 2543 | 350 2544 | 2545 | 2546 | 2547 | PotatoPlant 2548 | Unspecified 2549 | 0 2550 | 0 2551 | 2552 | 1507 2553 | 286 2554 | 1525 2555 | 312 2556 | 2557 | 2558 | 2559 | PotatoPlant 2560 | Unspecified 2561 | 0 2562 | 0 2563 | 2564 | 1497 2565 | 378 2566 | 1523 2567 | 403 2568 | 2569 | 2570 | 2571 | PotatoPlant 2572 | Unspecified 2573 | 0 2574 | 0 2575 | 2576 | 1676 2577 | 456 2578 | 1698 2579 | 490 2580 | 2581 | 2582 | 2583 | PotatoPlant 2584 | Unspecified 2585 | 0 2586 | 0 2587 | 2588 | 1680 2589 | 500 2590 | 1696 2591 | 526 2592 | 2593 | 2594 | 2595 | PotatoPlant 2596 | Unspecified 2597 | 0 2598 | 0 2599 | 2600 | 1674 2601 | 545 2602 | 1718 2603 | 576 2604 | 2605 | 2606 | 2607 | PotatoPlant 2608 | Unspecified 2609 | 0 2610 | 0 2611 | 2612 | 1647 2613 | 574 2614 | 1682 2615 | 603 2616 | 2617 | 2618 | 2619 | PotatoPlant 2620 | Unspecified 2621 | 0 2622 | 0 2623 | 2624 | 1521 2625 | 504 2626 | 1557 2627 | 549 2628 | 2629 | 2630 | 2631 | PotatoPlant 2632 | Unspecified 2633 | 0 2634 | 0 2635 | 2636 | 1221 2637 | 382 2638 | 1242 2639 | 406 2640 | 2641 | 2642 | 2643 | PotatoPlant 2644 | Unspecified 2645 | 0 2646 | 0 2647 | 2648 | 1488 2649 | 536 2650 | 1513 2651 | 563 2652 | 2653 | 2654 | 2655 | PotatoPlant 2656 | Unspecified 2657 | 0 2658 | 0 2659 | 2660 | 1538 2661 | 572 2662 | 1569 2663 | 595 2664 | 2665 | 2666 | 2667 | PotatoPlant 2668 | Unspecified 2669 | 0 2670 | 0 2671 | 2672 | 1489 2673 | 574 2674 | 1518 2675 | 598 2676 | 2677 | 2678 | 2679 | PotatoPlant 2680 | Unspecified 2681 | 0 2682 | 0 2683 | 2684 | 1517 2685 | 601 2686 | 1532 2687 | 627 2688 | 2689 | 2690 | 2691 | PotatoPlant 2692 | Unspecified 2693 | 0 2694 | 0 2695 | 2696 | 1522 2697 | 686 2698 | 1543 2699 | 705 2700 | 2701 | 2702 | 2703 | PotatoPlant 2704 | Unspecified 2705 | 0 2706 | 0 2707 | 2708 | 1541 2709 | 744 2710 | 1577 2711 | 768 2712 | 2713 | 2714 | 2715 | PotatoPlant 2716 | Unspecified 2717 | 0 2718 | 0 2719 | 2720 | 1551 2721 | 809 2722 | 1571 2723 | 838 2724 | 2725 | 2726 | 2727 | PotatoPlant 2728 | Unspecified 2729 | 0 2730 | 0 2731 | 2732 | 1692 2733 | 615 2734 | 1720 2735 | 639 2736 | 2737 | 2738 | 2739 | PotatoPlant 2740 | Unspecified 2741 | 0 2742 | 0 2743 | 2744 | 1679 2745 | 645 2746 | 1710 2747 | 666 2748 | 2749 | 2750 | 2751 | PotatoPlant 2752 | Unspecified 2753 | 0 2754 | 0 2755 | 2756 | 1686 2757 | 691 2758 | 1727 2759 | 720 2760 | 2761 | 2762 | 2763 | PotatoPlant 2764 | Unspecified 2765 | 0 2766 | 0 2767 | 2768 | 1689 2769 | 761 2770 | 1717 2771 | 791 2772 | 2773 | 2774 | 2775 | PotatoPlant 2776 | Unspecified 2777 | 0 2778 | 0 2779 | 2780 | 1712 2781 | 805 2782 | 1748 2783 | 836 2784 | 2785 | 2786 | 2787 | PotatoPlant 2788 | Unspecified 2789 | 0 2790 | 0 2791 | 2792 | 1722 2793 | 878 2794 | 1750 2795 | 895 2796 | 2797 | 2798 | 2799 | PotatoPlant 2800 | Unspecified 2801 | 0 2802 | 0 2803 | 2804 | 1576 2805 | 875 2806 | 1595 2807 | 892 2808 | 2809 | 2810 | 2811 | PotatoPlant 2812 | Unspecified 2813 | 0 2814 | 0 2815 | 2816 | 1738 2817 | 916 2818 | 1773 2819 | 948 2820 | 2821 | 2822 | 2823 | PotatoPlant 2824 | Unspecified 2825 | 0 2826 | 0 2827 | 2828 | 1717 2829 | 941 2830 | 1754 2831 | 979 2832 | 2833 | 2834 | 2835 | PotatoPlant 2836 | Unspecified 2837 | 0 2838 | 0 2839 | 2840 | 1727 2841 | 999 2842 | 1753 2843 | 1023 2844 | 2845 | 2846 | 2847 | PotatoPlant 2848 | Unspecified 2849 | 0 2850 | 0 2851 | 2852 | 1718 2853 | 1050 2854 | 1755 2855 | 1075 2856 | 2857 | 2858 | 2859 | PotatoPlant 2860 | Unspecified 2861 | 0 2862 | 0 2863 | 2864 | 1740 2865 | 1143 2866 | 1769 2867 | 1195 2868 | 2869 | 2870 | 2871 | PotatoPlant 2872 | Unspecified 2873 | 0 2874 | 0 2875 | 2876 | 1737 2877 | 1243 2878 | 1767 2879 | 1261 2880 | 2881 | 2882 | 2883 | PotatoPlant 2884 | Unspecified 2885 | 0 2886 | 0 2887 | 2888 | 1746 2889 | 1271 2890 | 1776 2891 | 1307 2892 | 2893 | 2894 | 2895 | PotatoPlant 2896 | Unspecified 2897 | 0 2898 | 0 2899 | 2900 | 1754 2901 | 1311 2902 | 1781 2903 | 1337 2904 | 2905 | 2906 | 2907 | PotatoPlant 2908 | Unspecified 2909 | 0 2910 | 0 2911 | 2912 | 1755 2913 | 1341 2914 | 1772 2915 | 1361 2916 | 2917 | 2918 | 2919 | PotatoPlant 2920 | Unspecified 2921 | 0 2922 | 0 2923 | 2924 | 1767 2925 | 1374 2926 | 1794 2927 | 1392 2928 | 2929 | 2930 | 2931 | PotatoPlant 2932 | Unspecified 2933 | 0 2934 | 0 2935 | 2936 | 1757 2937 | 1443 2938 | 1788 2939 | 1483 2940 | 2941 | 2942 | 2943 | PotatoPlant 2944 | Unspecified 2945 | 0 2946 | 0 2947 | 2948 | 1606 2949 | 1423 2950 | 1660 2951 | 1458 2952 | 2953 | 2954 | 2955 | PotatoPlant 2956 | Unspecified 2957 | 1 2958 | 0 2959 | 2960 | 1612 2961 | 1472 2962 | 1644 2963 | 1500 2964 | 2965 | 2966 | 2967 | PotatoPlant 2968 | Unspecified 2969 | 0 2970 | 0 2971 | 2972 | 1458 2973 | 1436 2974 | 1498 2975 | 1470 2976 | 2977 | 2978 | 2979 | PotatoPlant 2980 | Unspecified 2981 | 1 2982 | 0 2983 | 2984 | 1462 2985 | 1483 2986 | 1493 2987 | 1500 2988 | 2989 | 2990 | 2991 | PotatoPlant 2992 | Unspecified 2993 | 0 2994 | 0 2995 | 2996 | 1453 2997 | 1265 2998 | 1483 2999 | 1305 3000 | 3001 | 3002 | 3003 | PotatoPlant 3004 | Unspecified 3005 | 0 3006 | 0 3007 | 3008 | 1435 3009 | 1183 3010 | 1490 3011 | 1219 3012 | 3013 | 3014 | 3015 | PotatoPlant 3016 | Unspecified 3017 | 0 3018 | 0 3019 | 3020 | 1603 3021 | 1179 3022 | 1633 3023 | 1216 3024 | 3025 | 3026 | 3027 | PotatoPlant 3028 | Unspecified 3029 | 0 3030 | 0 3031 | 3032 | 1603 3033 | 1251 3034 | 1627 3035 | 1281 3036 | 3037 | 3038 | 3039 | PotatoPlant 3040 | Unspecified 3041 | 0 3042 | 0 3043 | 3044 | 1427 3045 | 1122 3046 | 1478 3047 | 1168 3048 | 3049 | 3050 | 3051 | PotatoPlant 3052 | Unspecified 3053 | 0 3054 | 0 3055 | 3056 | 1446 3057 | 1225 3058 | 1479 3059 | 1256 3060 | 3061 | 3062 | 3063 | PotatoPlant 3064 | Unspecified 3065 | 1 3066 | 0 3067 | 3068 | 727 3069 | 1487 3070 | 762 3071 | 1500 3072 | 3073 | 3074 | 3075 | PotatoPlant 3076 | Unspecified 3077 | 0 3078 | 0 3079 | 3080 | 817 3081 | 895 3082 | 861 3083 | 936 3084 | 3085 | 3086 | 3087 | PotatoPlant 3088 | Unspecified 3089 | 1 3090 | 0 3091 | 3092 | 1328 3093 | 1481 3094 | 1350 3095 | 1500 3096 | 3097 | 3098 | 3099 | -------------------------------------------------------------------------------- /dataset/training/PotatoPlant417.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kdijkstra13/OpenCentroidNet/a491477351f050f9713072dd1a218314f5580f7a/dataset/training/PotatoPlant417.png -------------------------------------------------------------------------------- /dataset/validation/PotatoPlant143.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kdijkstra13/OpenCentroidNet/a491477351f050f9713072dd1a218314f5580f7a/dataset/validation/PotatoPlant143.png -------------------------------------------------------------------------------- /dataset/validation/PotatoPlant297.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kdijkstra13/OpenCentroidNet/a491477351f050f9713072dd1a218314f5580f7a/dataset/validation/PotatoPlant297.png -------------------------------------------------------------------------------- /dataset/validation/PotatoPlant552.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kdijkstra13/OpenCentroidNet/a491477351f050f9713072dd1a218314f5580f7a/dataset/validation/PotatoPlant552.png -------------------------------------------------------------------------------- /misc/create_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (C) 2019 Klaas Dijkstra 2 | # 3 | # This file is part of OpenCentroidNet. 4 | # 5 | # OpenCentroidNet is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | 10 | # OpenCentroidNet is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | 15 | # You should have received a copy of the GNU General Public License 16 | # along with OpenCentroidNet. If not, see . 17 | 18 | import numpy as np 19 | from skimage.draw import circle 20 | import os 21 | import cv2 22 | import random 23 | 24 | def create_dataset(folder, file: str = "training.csv", n: int = 50, height: int = 200, width: int = 300, n_circles: int = 5, min_r: int = 15, max_r: int = 20, prefix="train", n_classes=3): 25 | boxes = [] 26 | for i in range(n): 27 | out_fn = f"{prefix}_{i}.png" 28 | img = np.random.randint(10, 30, (3, height, width)) 29 | for a in range(n_circles): 30 | if min_r < max_r: 31 | r = np.random.randint(min_r, max_r) 32 | else: 33 | r = min_r 34 | y = np.random.randint(r, height-r) 35 | x = np.random.randint(r, width-r) 36 | rr, cc = circle(y, x, r) 37 | d = (r - ((((rr - y) ** 2) + ((cc - x) ** 2)) ** 0.5)) * (150 / r) + 50 38 | id = a % min(n_classes, 3) 39 | img[id, rr, cc] = d 40 | xmin,xmax,ymin,ymax = x-r,x+r,y-r,y+r 41 | boxes.append(f"{out_fn},{xmin},{xmax},{ymin},{ymax},{id}\n") 42 | n_points = round(height * width * 0.2) 43 | y_rand = np.random.randint(0, height, n_points) 44 | x_rand = np.random.randint(0, width, n_points) 45 | img[:, y_rand, x_rand] = np.random.randint(0, 64, size=(3, n_points)) 46 | img = np.transpose(img.astype(np.uint8), (1, 2, 0)) 47 | cv2.imwrite(os.path.join(folder, out_fn), img) 48 | f = open(os.path.join(folder, file), "w") 49 | f.writelines(boxes) 50 | 51 | 52 | random.seed(42) 53 | folder = os.path.join("..", "data", "dataset") 54 | train_folder = os.path.join(folder, "training") 55 | validation_folder = os.path.join(folder, "validation") 56 | os.makedirs(folder, exist_ok=True) 57 | create_dataset(folder, file="training.csv", prefix="train", n=50) 58 | create_dataset(folder, file="validation.csv", prefix="valid", n=10) 59 | 60 | 61 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | # Copyright (C) 2019 Klaas Dijkstra 2 | # 3 | # This file is part of OpenCentroidNet. 4 | # 5 | # OpenCentroidNet is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | 10 | # OpenCentroidNet is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | 15 | # You should have received a copy of the GNU General Public License 16 | # along with OpenCentroidNet. If not, see . 17 | 18 | import torch.utils.data 19 | from config import Config 20 | 21 | from centroidnet import * 22 | 23 | dev = "cuda:1" 24 | 25 | def create_centroidnet(num_channels, num_classes): 26 | model = centroidnet.CentroidNet(num_classes, num_channels) 27 | return model 28 | 29 | 30 | def load_model(filename, model): 31 | print(f"Load snapshot from: {os.path.abspath(filename)}") 32 | with open(filename, "rb") as f: 33 | state_dict = torch.load(f) 34 | model.load_state_dict(state_dict) 35 | return model 36 | 37 | 38 | def predict(image, model, max_dist, binning, nm_size, centroid_threshold, sub, div): 39 | # Prepare network input 40 | inputs = np.expand_dims(np.transpose(image, (2, 0, 1)), axis=0).astype(np.float32) 41 | inputs = torch.Tensor((inputs - sub) / div) 42 | 43 | # Upload to device 44 | inputs = inputs.to(Config.dev) 45 | model.to(Config.dev) 46 | 47 | # Do inference and decoding 48 | outputs = model(inputs)[0].cpu().detach().numpy() 49 | centroid_vectors, votes, class_ids, class_probs, votes_nm, centroids = centroidnet.decode(outputs, max_dist, binning, nm_size, centroid_threshold) 50 | 51 | # Only return the list of centroids 52 | return centroids 53 | 54 | 55 | def main(): 56 | file = "data/dataset/valid_0.png" 57 | 58 | print(f"Load image: {file}") 59 | image = cv2.imread(file) 60 | assert image is not None, f"Image {os.path.abspath(file)} not found" 61 | 62 | print(f"Predicting.") 63 | model = create_centroidnet(num_channels=Config.num_channels, num_classes=Config.num_classes) 64 | model = load_model(os.path.join("data", "CentroidNet.pth"), model) 65 | centroids = predict(image, model, Config.max_dist, Config.binning, Config.nm_size, Config.centroid_threshold, Config.sub, Config.div) 66 | centroids = np.stack(centroids, axis=0) 67 | print(f"Found {centroids.shape[0]} centroids (y, x, class_id, probability):\n {centroids}") 68 | 69 | 70 | if __name__ == '__main__': 71 | main() 72 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | # Copyright (C) 2019 Klaas Dijkstra 2 | # 3 | # This file is part of OpenCentroidNet. 4 | # 5 | # OpenCentroidNet is free software: you can redistribute it and/or modify 6 | # it under the terms of the GNU General Public License as published by 7 | # the Free Software Foundation, either version 3 of the License, or 8 | # (at your option) any later version. 9 | 10 | # OpenCentroidNet is distributed in the hope that it will be useful, 11 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | # GNU General Public License for more details. 14 | 15 | # You should have received a copy of the GNU General Public License 16 | # along with OpenCentroidNet. If not, see . 17 | 18 | import torch.utils.data 19 | import torch.optim as optim 20 | from tqdm import tqdm 21 | import copy 22 | from config import Config 23 | from centroidnet import * 24 | 25 | 26 | def create_data_loader(training_file, validation_file, crop, max_dist, repeat, sub, div): 27 | training_set = centroidnet.CentroidNetDataset(training_file, crop=crop, max_dist=max_dist, repeat=repeat, sub=sub, div=div) 28 | validation_set = centroidnet.CentroidNetDataset(validation_file, crop=crop, max_dist=max_dist, sub=sub, div=div) 29 | return training_set, validation_set 30 | 31 | 32 | def create_centroidnet(num_channels, num_classes): 33 | model = centroidnet.CentroidNet(num_classes, num_channels) 34 | return model 35 | 36 | 37 | def create_centroidnet_loss(): 38 | loss = centroidnet.CentroidLoss() 39 | return loss 40 | 41 | 42 | def validate(validation_loss, epoch, validation_set_loader, model, loss, validation_interval=10): 43 | if epoch % validation_interval == 0: 44 | with torch.no_grad(): 45 | # Validate using validation data loader 46 | model.eval() # put in evaluation mode 47 | validation_loss = 0 48 | idx = 0 49 | for inputs, targets in validation_set_loader: 50 | inputs = inputs.to(Config.dev) 51 | targets = targets.to(Config.dev) 52 | outputs = model(inputs) 53 | mse = loss(outputs, targets) 54 | validation_loss += mse.item() 55 | idx += 1 56 | model.train() # put back in training mode 57 | return validation_loss / idx 58 | else: 59 | return validation_loss 60 | 61 | 62 | def save_model(filename, model): 63 | print(f"Save snapshot to: {os.path.abspath(filename)}") 64 | with open(filename, "wb") as f: 65 | torch.save(model.state_dict(), f) 66 | 67 | 68 | def load_model(filename, model): 69 | print(f"Load snapshot from: {os.path.abspath(filename)}") 70 | with open(filename, "rb") as f: 71 | state_dict = torch.load(f) 72 | model.load_state_dict(state_dict) 73 | return model 74 | 75 | 76 | def train(training_set, validation_set, model, loss, epochs, batch_size, learn_rate, validation_interval): 77 | print(f"Training {len(training_set)} images for {epochs} epochs with a batch size of {batch_size}.\n" 78 | f"Validate {len(validation_set)} images each {validation_interval} epochs and learning rate {learn_rate}.\n") 79 | 80 | #training_set.eval() 81 | 82 | best_model = copy.deepcopy(model) 83 | model.to(Config.dev) 84 | 85 | optimizer = optim.Adam(model.parameters(), lr=learn_rate) 86 | scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1) 87 | 88 | training_set_loader = torch.utils.data.DataLoader(training_set, batch_size=batch_size, shuffle=True, num_workers=10, drop_last=True) 89 | validation_set_loader = torch.utils.data.DataLoader(validation_set, batch_size=min(len(validation_set), batch_size), shuffle=True, num_workers=10, drop_last=True) 90 | 91 | if len(training_set_loader) == 0: 92 | raise Exception("The training dataset does no contain any samples. Is the minibatch larger than the amount of samples?") 93 | if len(training_set_loader) == 0: 94 | raise Exception("The validation dataset does no contain any samples. Is the minibatch larger than the amount of samples?") 95 | 96 | bar = tqdm(range(1, epochs)) 97 | validation_loss = 9999 98 | best_loss = 9999 99 | for epoch in bar: 100 | training_loss = 0 101 | idx = 0 102 | # Train one minibatch 103 | for (inputs, targets) in training_set_loader: 104 | inputs = inputs.to(Config.dev) 105 | targets = targets.to(Config.dev) 106 | optimizer.zero_grad() 107 | outputs = model(inputs) 108 | ls = loss(outputs, targets) 109 | ls.backward() 110 | optimizer.step() 111 | training_loss += ls.item() 112 | idx += 1 113 | 114 | scheduler.step(epoch) 115 | 116 | # Update progress bar 117 | bar.set_description("Epoch {}/{} Loss(T): {:5f} and Loss(V): {:.5f}".format(epoch, epochs, training_loss / idx, validation_loss)) 118 | bar.refresh() 119 | 120 | # Validate and save 121 | validation_loss = validate(validation_loss, epoch, validation_set_loader, model, loss, validation_interval) 122 | if validation_loss < best_loss: 123 | print(f"Update model with loss {validation_loss}") 124 | best_loss = validation_loss 125 | best_model.load_state_dict(model.state_dict()) 126 | 127 | return best_model 128 | 129 | 130 | def predict(data_set, model, loss, max_dist, binning, nm_size, centroid_threshold): 131 | print(f"Predicting {len(data_set)} files with loss {type(loss)}") 132 | with torch.no_grad(): 133 | data_set.eval() 134 | model.eval() 135 | model.to(Config.dev) 136 | loss_value = 0 137 | idx = 0 138 | set_loader = torch.utils.data.DataLoader(data_set, batch_size=5, shuffle=False, num_workers=1, drop_last=False) 139 | result_images = [] 140 | result_centroids = [] 141 | for inputs, targets in tqdm(set_loader): 142 | inputs = inputs.to(Config.dev) 143 | targets = targets.to(Config.dev) 144 | outputs = model(inputs) 145 | ls = loss(outputs, targets) 146 | loss_value += ls.item() 147 | decoded = [centroidnet.decode(img, max_dist, binning, nm_size, centroid_threshold) for img in outputs.cpu().numpy()] 148 | 149 | # Add all numpy arrays to a list 150 | result_images.extend([{"inputs": i.cpu().numpy(), 151 | "targets": t.cpu().numpy(), 152 | "vectors": d[0], 153 | "votes": d[1], 154 | "class_ids": d[2], 155 | "class_probs": d[3], 156 | "centroids": d[4]} for i, t, o, d in zip(inputs, targets, outputs, decoded)]) 157 | 158 | # Add image_id to centroid locations and add to list 159 | result_centroids.extend([np.stack(ctr for ctr in d[5]) for d in decoded]) 160 | idx = idx + 1 161 | print("Aggregated loss is {:.5f}".format(loss_value / idx)) 162 | return result_images, result_centroids 163 | 164 | 165 | def output(folder, result_images, result_centroids): 166 | os.makedirs(folder, exist_ok=True) 167 | print(f"Created output folder {os.path.abspath(folder)}") 168 | for i, sample in enumerate(result_images): 169 | for name, arr in sample.items(): 170 | np.save(os.path.join(folder, f"{i}_{name}.npy"), arr) 171 | 172 | lines = ["image_nr centroid_y centroid_x class_id probability \r\n"] 173 | with open(os.path.join(folder, "validation.txt"), "w") as f: 174 | for i, image in enumerate(result_centroids): 175 | for line in image: 176 | line_str = [str(i), *[str(elm) for elm in line]] 177 | lines.append(" ".join(line_str) + "\r\n") 178 | f.writelines(lines) 179 | 180 | 181 | def main(): 182 | # Perform retraining. 183 | do_train = True 184 | # Perform loading of the model. 185 | do_load = True 186 | # Perform final prediction and export data. 187 | do_predict = True 188 | 189 | # Start script 190 | assert (do_train or do_load), "Enable do_train and/or do_load" 191 | 192 | # Load datasets 193 | training_set, validation_set = create_data_loader(os.path.join("data", "dataset", "training.csv"), 194 | os.path.join("data", "dataset", "validation.csv"), 195 | crop=Config.crop, max_dist=Config.max_dist, repeat=1, sub=Config.sub, div=Config.div) 196 | 197 | assert training_set.num_classes == Config.num_classes, f"Number of classes on config.py is incorrect. Should be {training_set.num_classes}" 198 | 199 | # Create loss function 200 | loss = create_centroidnet_loss() 201 | model = None 202 | 203 | # Train network 204 | if do_train: 205 | # Create network and load snapshots 206 | model = create_centroidnet(num_channels=Config.num_channels, num_classes=Config.num_classes) 207 | model = train(training_set, validation_set, model, loss, epochs=Config.epochs, batch_size=Config.batch_size, learn_rate=Config.learn_rate, validation_interval=Config.validation_interval) 208 | save_model(os.path.join("data", "CentroidNet.pth"), model) 209 | 210 | # Load model 211 | if do_load: 212 | model = create_centroidnet(num_channels=3, num_classes=training_set.num_classes) 213 | model = load_model(os.path.join("data", "CentroidNet.pth"), model) 214 | 215 | # Predict 216 | if do_predict: 217 | result_images, result_centroids = predict(validation_set, model, loss, max_dist=Config.max_dist, binning=Config.binning, nm_size=Config.nm_size, centroid_threshold=Config.centroid_threshold) 218 | output(os.path.join("data", "validation_result"), result_images, result_centroids) 219 | 220 | 221 | if __name__ == '__main__': 222 | main() 223 | --------------------------------------------------------------------------------