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Buildings Classification
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prepare.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Building Shape Classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we try to classify images of individual buildings based on their shapes. Shape \"classes\" are extracted from the ground-truth annotations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initializations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Imports."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"import os\n",
"import pickle\n",
"import random\n",
"from typing import Dict, List, Tuple\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"plt.rcParams['figure.figsize'] = (4,4)\n",
"%config InlineBackend.figure_format = 'svg'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"General utility functions."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def compose(*fns):\n",
" return reduce(lambda f, g: lambda x: f(g(x)), fns, lambda x: x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The save directory for this run of the notebook."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"dir_name = 'run+{}'.format(datetime.now().strftime('%H-%M-%S'))\n",
"save_dir = os.path.join(os.getcwd(), 'output', dir_name)\n",
"os.makedirs(save_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load a part of the crowdAI dataset. This consists of 8366 images totaling 71871 annotations. Out of all annotations, we sample ~20,000 for computational reasons. This can be later scaled to the total number of milions of annotations in the full dataset."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from pycocotools.coco import COCO as make_coco\n",
"import skimage.io as io\n",
"\n",
"Annotation = Dict # The type of an annotation\n",
"\n",
"# The number of annotations used in this experiment.\n",
"N_ANNOTATIONS = 30000\n",
"\n",
" \n",
"def _sample_annotations(coco: object, n_samples: int) -> List[Annotation]:\n",
" all_ids = coco.getAnnIds()\n",
" if n_samples > len(all_ids):\n",
" raise ValueError('Cannot sample more annotations that all that were loaded.')\n",
" elif n_samples == len(all_ids):\n",
" return coco.loadAnns(all_ids)\n",
"\n",
" samples = []\n",
" for i, ann_id in enumerate(all_ids):\n",
" if len(samples) < n_samples:\n",
" samples.append(coco.loadAnns(ann_id)[0])\n",
" elif (j := int(random.random() * i)) < n_samples:\n",
" samples[j] = coco.loadAnns(ann_id)[0]\n",
" assert len(samples) == n_samples\n",
"\n",
" return samples\n",
"\n",
"def _basic_filter(anns: List[Annotation]) -> List[Annotation]:\n",
" n_anns = len(anns)\n",
" print() # new line\n",
"\n",
" # 1. Remove those that have multiple components: by my understanding, this should not happen\n",
" anns = [ann for ann in anns if len(ann['segmentation']) == 1]\n",
" print('Removed {} annotations because they have '\n",
" 'multiple components.'.format(n_anns - len(anns)))\n",
"\n",
" return anns\n",
"\n",
"def _index_by_id(anns: List[Annotation]) -> Dict[int, Annotation]:\n",
" return {ann['id'] : ann for ann in anns}\n",
"\n",
"class Dataset:\n",
"\n",
" def __init__(self, spec: str, image_dir: str):\n",
" self.coco = make_coco(spec)\n",
" self.image_dir = image_dir\n",
" self.anns = _index_by_id(_basic_filter(_sample_annotations(self.coco, N_ANNOTATIONS)))\n",
" # FIXME(ccruceru): Do not hardcode them.\n",
" self.w = 300\n",
" self.h = 300\n",
" \n",
" def read_image(self, image_id: int) -> np.ndarray:\n",
" image_dict = self.coco.loadImgs(image_id)[0]\n",
" image_path = os.path.join(self.image_dir, image_dict['file_name'])\n",
" return io.imread(image_path)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loading annotations into memory...\n",
"Done (t=0.73s)\n",
"creating index...\n",
"index created!\n",
"\n",
"Removed 0 annotations because they have multiple components.\n"
]
}
],
"source": [
"ds = Dataset(spec=\"data/train/annotation-small.json\", image_dir=\"data/train/images\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute Pairwise Similarities"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import multiprocessing\n",
"from pycocotools import mask as cocomask\n",
"\n",
"\n",
"Poly = List[float] # The type of the polygon representation of an annotation\n",
"Rle = Dict # The type of the RLE representation of an annotation\n",
"Mask = np.ndarray # The type of the mask representation of an annotation\n",
"\n",
"\n",
"# The number of \"steps\" in the angle domain to take when approximating\n",
"# the rotation-invariant IOU.\n",
"N_ANGLES = 15\n",
"\n",
"# The number of CPUs to use when computing the pairwise IOUs in parallel.\n",
"N_CPUS = 15\n",
"\n",
"\n",
"def _extract_poly(ann: Annotation) -> Poly:\n",
" return ann['segmentation'][0]\n",
"\n",
"def _poly_to_x(poly: Poly) -> np.ndarray:\n",
" n = (len(poly) - 2) // 2\n",
" X = np.empty(shape=(n, 2))\n",
" for i in range(n):\n",
" X[i, 0] = poly[2 * i]\n",
" X[i, 1] = poly[2 * i + 1]\n",
"\n",
" return X\n",
"\n",
"def _x_to_poly(X: np.ndarray) -> Poly:\n",
" poly = []\n",
" for i in range(X.shape[0]):\n",
" poly.extend([X[i, 0], X[i, 1]])\n",
" poly.extend([poly[0], poly[1]])\n",
"\n",
" return poly\n",
"\n",
"def _compute_centroid_and_area(\n",
" poly: Poly, min_area: int) -> Tuple[Tuple[float, float], float]:\n",
" # Reference: https://en.wikipedia.org/wiki/Centroid#Of_a_polygon\n",
" c_x = 0\n",
" c_y = 0\n",
" area = 0\n",
" n = (len(poly) - 2) // 2\n",
" for i in range(n):\n",
" x_i = poly[2 * i]\n",
" y_i = poly[2 * i + 1]\n",
" x_ip1 = poly[(2 * i + 2) % (2 * n)]\n",
" y_ip1 = poly[(2 * i + 3) % (2 * n)]\n",
"\n",
" area_term = x_i * y_ip1 - x_ip1 * y_i\n",
" c_x += (x_i + x_ip1) * area_term\n",
" c_y += (y_i + y_ip1) * area_term\n",
" area += area_term\n",
"\n",
" # NOTE: Exact comparison to 0 should be fine because the polygon is given\n",
" # by integer grid coordinates.\n",
" if np.abs(area) < min_area:\n",
" raise ValueError('The area is less than the minimum desired one.')\n",
"\n",
" return (c_x / area / 3, c_y / area / 3), np.abs(area) / 2\n",
"\n",
"def _scale_and_center(poly: Poly, min_area: int) -> Poly:\n",
" # Convert to numpy format\n",
" X = _poly_to_x(poly)\n",
" n = X.shape[0]\n",
"\n",
" # Center the points.\n",
" (c_x, c_y), _ = _compute_centroid_and_area(poly, min_area)\n",
" X_centered = X - [c_x, c_y]\n",
"\n",
" # Apply uniform scaling.\n",
" scale = np.sqrt(np.sum(X_centered**2) / n)\n",
" X_scaled = X_centered / scale\n",
"\n",
" # Convert back to 'poly' format\n",
" return _x_to_poly(X_scaled)\n",
"\n",
"def _centered_poly_to_rle(poly: Poly, \n",
" width: int = 100, \n",
" height: int = 100, \n",
" scale: float = 30) -> Rle:\n",
" # Center the polygon within the mask.\n",
" cpoly = []\n",
" for i in range(len(poly)):\n",
" if i % 2 == 0:\n",
" offset = width / 2\n",
" else:\n",
" offset = height / 2\n",
"\n",
" # Also apply a uniform rescaling of the whole object.\n",
" cpoly.append(scale * poly[i] + offset)\n",
"\n",
" # Transform it to RLE.\n",
" return cocomask.frPyObjects([cpoly], width, height)\n",
"\n",
"def _get_rotation_matrices():\n",
" thetas = np.linspace(-np.pi, np.pi, N_ANGLES)\n",
" sins = np.sin(thetas)\n",
" coss = np.cos(thetas)\n",
" Rs = np.stack([coss, -sins, sins, coss], axis=-1).reshape(-1, 2, 2)\n",
"\n",
" return Rs\n",
"\n",
"def _iou(lhs: Rle, rhs: Rle) -> float:\n",
" return cocomask.iou(lhs, rhs, [False])[0][0]\n",
"\n",
"\n",
"class IouWithPos:\n",
" def __init__(self, rles: List[List[Rle]]):\n",
" self.rles = rles\n",
"\n",
" def __call__(self, pos: Tuple[int, int]) -> Tuple[int, int, float]:\n",
" i, j = pos\n",
" rle_i = self.rles[i][0]\n",
" return i, j, max(_iou(rle_i, rle_j) for rle_j in self.rles[j])\n",
"\n",
"\n",
"def _compute_ious(rles: List[List[Rle]]) -> np.ndarray:\n",
" # The function used to lazily generate the cell positions\n",
" def _gen(n: int):\n",
" for i in range(n):\n",
" for j in range(i + 1, n):\n",
" yield i, j\n",
" \n",
" n = len(rles)\n",
" pool = multiprocessing.Pool(N_CPUS)\n",
" ret = pool.map(IouWithPos(rles), _gen(n))\n",
" \n",
" # Put them into a numpy array.\n",
" ious = np.zeros(shape=(n, n))\n",
" for i, j, iou in ret:\n",
" ious[i, j] = iou\n",
" ious += ious.T\n",
"\n",
" return ious\n",
"\n",
"def _ious_to_dists(ious: np.ndarray, eps: float = 1e-5) -> np.ndarray:\n",
" n = ious.shape[0]\n",
" pdists = np.zeros_like(ious)\n",
" for i in range(n):\n",
" for j in range(i + 1, n):\n",
" pdists[i, j] = 1 / (ious[i, j] + eps) - 1\n",
" pdists += pdists.T\n",
"\n",
" return pdists\n",
"\n",
"\n",
"class PairwiseDistances:\n",
" \n",
" def __init__(self, ds: Dataset, *, min_poly_area: int = 1000):\n",
" # Center and re-scale the annotations.\n",
" ann_ids: List[int] = []\n",
" polys: List[Poly] = []\n",
" for ann_id, ann in ds.anns.items():\n",
" try:\n",
" poly = _scale_and_center(_extract_poly(ann), min_poly_area)\n",
" polys.append(poly)\n",
" ann_ids.append(ann_id)\n",
" except ValueError as e:\n",
" continue\n",
" print('{}/{} annotations are left'.format(len(ann_ids), len(ds.anns)))\n",
" \n",
" # Save the annotations we will be working with from here on.\n",
" self.valid_ann_ids = ann_ids\n",
" self.valid_polys = {ann_id : poly for ann_id, poly in zip(ann_ids, polys)}\n",
"\n",
" # Transform them to numpy format\n",
" Xs: List[np.ndarray] = [_poly_to_x(poly) for poly in polys]\n",
" \n",
" # Compute the rotations corresponding to the discretized domain.\n",
" Rs: np.ndarray = _get_rotation_matrices()\n",
" Xs_rots: List[np.ndarray] = [X @ Rs for X in Xs]\n",
" \n",
" # Transform to RLE for fast IOU computation.\n",
" rles: List[List[Rle]] = []\n",
" for X_rots in Xs_rots:\n",
" rle_rots = []\n",
" for X_rot in X_rots:\n",
" rle = _centered_poly_to_rle(_x_to_poly(X_rot))\n",
" rle_rots.append(rle) \n",
" rles.append(rle_rots)\n",
" \n",
" # Compute the pairwise IOUs.\n",
" self.ious = _compute_ious(rles)\n",
" \n",
" # Compute the corresponding distances.\n",
" self.dists = _ious_to_dists(self.ious)\n",
" \n",
" def get_ann_poly_from_index(self, i: int) -> Poly:\n",
" return self.valid_polys[self.valid_ann_ids[i]]\n",
" \n",
" \n",
" def get_ann_mask_from_poly(self, poly: Poly) -> Mask:\n",
" rle = _centered_poly_to_rle(poly)\n",
" return cocomask.decode(rle).squeeze().astype(bool)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**WARNING**: This takes a long time (about ~3 hours)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22117/30000 annotations are left\n",
"CPU times: user 10min 34s, sys: 2min 1s, total: 12min 36s\n",
"Wall time: 3h 26min 58s\n"
]
}
],
"source": [
"%%time\n",
"pd = PairwiseDistances(ds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Serialize it."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"with open(os.path.join(save_dir, 'pd.pjk'), 'wb') as f:\n",
" pickle.dump(pd, f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# RUN THIS TO SYNC THE `Dataset' instance with the annotation ids\n",
"# for which we computed the pairwies distances here.\n",
"#\n",
"# This was useful during development: I added some new fields to the\n",
"# `Dataset' class which resulted in new samples being picked. It can\n",
"# be removed once everything works.\n",
"\n",
"anns = {}\n",
"for ann_id in pd.valid_ann_ids:\n",
" anns[ann_id] = ds.coco.loadAnns(ann_id)[0]\n",
"# THIS IS COMMENTED FOR SAFETY (although it should be no-op)\n",
"# ds.anns = anns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizations of pairwise similarities"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualize the resulting (dis)similarities."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
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"source": [
"f, axes = plt.subplots(1, 2, figsize=(8,4))\n",
"\n",
"axes[0].hist(pd.ious.flatten(), bins=20, ec='k', lw=1.2)\n",
"axes[0].set_xlabel('IOU')\n",
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Remove outlier shapes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now need to filter out some of the \"outlier\" shapes."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"class CuratedDataset:\n",
" \n",
" def __init__(self, ds: Dataset, pd: PairwiseDistances, criterion):\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Later Note**: This is not necessary because hierarchical clustering should be \"equivariant\" with respect to the outliers in this sense: increasing the number of clusters that we \"stop at\", we should be able to filter out those outliers and the remaining clusters should match the ones we would get if we filtered them out in the first place (and decreased the number of clusters we extract)."
]
},
{
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"metadata": {},
"source": [
"## Direct Clustering"
]
},
{
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"metadata": {},
"source": [
"We cluster the shapes based on the pairwise (dis)similarities alone."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import AgglomerativeClustering\n",
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"N_CLUSTERS = 200\n",
"\n",
"shape_clustering = AgglomerativeClustering(\n",
" n_clusters=N_CLUSTERS, affinity='precomputed', linkage='complete').fit(pd.dists)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# The number of elements per cluster\n",
"cluster_labels, cluster_counts = np.unique(shape_clustering.labels_, return_counts=True)\n",
"assert np.all(cluster_labels == np.arange(N_CLUSTERS))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Analyze the results."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
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"metadata": {
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"output_type": "display_data"
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],
"source": [
"f, axes = plt.subplots(1, 2, figsize=(8,4))\n",
"axes[0].bar(cluster_labels, cluster_counts, align='center')\n",
"axes[0].set_ylabel('#annotations')\n",
"axes[0].set_xlabel('cluster id')\n",
"axes[0].set_title('Number of shapes per cluster')\n",
"\n",
"cumsum_counts = np.cumsum(np.sort(cluster_counts))\n",
"axes[1].plot(np.arange(N_CLUSTERS), cumsum_counts)\n",
"axes[1].set_yscale('log')\n",
"axes[1].set_ylabel('cumsum of sorted cluster counts')\n",
"axes[1].set_xlabel('ordered cluster id')\n",
"axes[1].set_title('Cumulative sum of cluster counts')\n",
"\n",
"f.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To note in the second plot: the first ~100 clusters represent mostly outliers (1 element per cluster) while from there on we have more balanced clusters, ending with a few clusters that contain a lot of elements (the sharp increase at the end)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize Clusters"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"scrolled": true
},
"outputs": [
{
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],
"source": [
"n_clusters_to_show = 25\n",
"n_samples_to_show = 12\n",
"figsize = (n_samples_to_show // 1.2, n_clusters_to_show // 1.2)\n",
"f, axes = plt.subplots(n_clusters_to_show, n_samples_to_show, figsize=figsize)\n",
"\n",
"sorted_indices = np.flip(np.argsort(cluster_counts))\n",
"for i in range(n_clusters_to_show):\n",
" cluster = cluster_labels[sorted_indices[i]]\n",
" indices = np.where(shape_clustering.labels_ == cluster)[0]\n",
" num_samples = min(n_samples_to_show, len(indices))\n",
" cluster_shape_indices = np.random.choice(indices, size=num_samples, replace=False)\n",
" for j in range(len(cluster_shape_indices)):\n",
" ann_poly = pd.get_ann_poly_from_index(cluster_shape_indices[j])\n",
" ann_mask = pd.get_ann_mask_from_poly(ann_poly)\n",
" axes[i, j].imshow(ann_mask)\n",
" for j in range(n_samples_to_show):\n",
" axes[i, j].set_axis_off()\n",
"f.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building Classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The idea is to define several \"shapes classes\" based on the *direct clustering* from above. Then, split the dataset into train and test and train/test as usual. Rather than looking at accuracy numbers, it is worth looking at confusion matrices, as some of the clusters are more similar than others. It would be interesting to see if we can recover a \"block structure\" in the confusion matrix that resembles the similarity between different clusters in the direct clustering plot from earlier. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Curated Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Build a dataset based on the clustering above. We filter out the buildings which are part of very small clusters."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"# The minimum number of elements in a cluster for a building image\n",
"# to be added to the dataset.\n",
"MIN_CLUSTER_SIZE = 100\n",
"\n",
"\n",
"class DatasetBuilder:\n",
"\n",
" def __init__(self, ds: Dataset, width: int, height: int, bbox_margin: int):\n",
" self.ds = ds\n",
" self.w = width\n",
" self.h = height\n",
" self.bbox_margin = bbox_margin\n",
" \n",
" self.image_cache: Dict[int, np.ndarray] = {}\n",
" \n",
" def _get_image_for_annotation(self, ann: Annotation) -> np.ndarray:\n",
" r\"\"\"Read the image corresponding to the annotation with id `ann_id'.\n",
" Cache the images so we avoid reloading the same image multiple times.\n",
" \"\"\"\n",
" image_id = ann['image_id']\n",
" if image_id in self.image_cache:\n",
" return self.image_cache[image_id]\n",
" \n",
" image = self.ds.read_image(image_id)\n",
" self.image_cache[image_id] = image\n",
" return image\n",
" \n",
" def _get_bbox_for_annotation(self, ann: Annotation) -> Tuple[int, int, int, int]:\n",
" rle = cocomask.frPyObjects(ann['segmentation'], self.ds.w, self.ds.h)\n",
" return tuple(int(e) for e in cocomask.toBbox(rle)[0])\n",
" \n",
" def _extract_building_image(self, ann_id: int) -> np.ndarray:\n",
" ann = self.ds.anns[ann_id]\n",
" img = self._get_image_for_annotation(ann)\n",
" \n",
" # Get the bounding box of the annotation.\n",
" x, y, w, h = self._get_bbox_for_annotation(ann)\n",
" \n",
" # Extend it by a few pixels, if possible.\n",
" x_new: int = max(0, x - self.bbox_margin)\n",
" y_new: int = max(0, y - self.bbox_margin)\n",
" w_new: int = min(self.ds.w - x_new, w + (x - x_new) + self.bbox_margin)\n",
" h_new: int = min(self.ds.h - y_new, h + (y - y_new) + self.bbox_margin)\n",
" \n",
" # Extract it.\n",
" img_bbox = img[y_new:(y_new + h_new), x_new:(x_new + w_new)]\n",
" \n",
" # The shape-preserving scaling factor.\n",
" scale = min(self.w / w_new, self.h / h_new)\n",
" w_rescaled = round(w_new * scale)\n",
" h_rescaled = round(h_new * scale)\n",
" assert w_rescaled == self.w or h_rescaled == self.h\n",
" \n",
" # Resize the image.\n",
" img_resized = cv2.resize(\n",
" img_bbox, (w_rescaled, h_rescaled), interpolation=cv2.INTER_LINEAR)\n",
" \n",
" # Pad with zeros to get the desired shape.\n",
" w_padding = self.w - w_rescaled; left = w_padding // 2; right = w_padding - left\n",
" h_padding = self.h - h_rescaled; top = h_padding // 2; bottom = h_padding - top\n",
" \n",
" return cv2.copyMakeBorder(\n",
" img_resized, \n",
" top=top, \n",
" bottom=bottom, \n",
" left=left, \n",
" right=right,\n",
" borderType=cv2.BORDER_CONSTANT,\n",
" value=0)\n",
" \n",
" def __call__(self, ann_ids: List[int], cluster_labels: np.ndarray):\n",
" Xs = []\n",
" ys = []\n",
" pd_indices = []\n",
" _, counts = np.unique(cluster_labels, return_counts=True)\n",
" for i, ann_id in enumerate(ann_ids):\n",
" ann_label = cluster_labels[i]\n",
" if counts[ann_label] < MIN_CLUSTER_SIZE:\n",
" continue\n",
"\n",
" # Extract the building image corresponding to this annotation.\n",
" building_image = self._extract_building_image(ann_id)\n",
"\n",
" Xs.append(building_image)\n",
" ys.append(ann_label)\n",
" pd_indices.append(i)\n",
" \n",
" # Relabel the classes.\n",
" ys = LabelEncoder().fit_transform(ys)\n",
" \n",
" Xs = np.stack(Xs)\n",
" ys = np.array(ys)\n",
" indices = np.array(pd_indices)\n",
"\n",
" # NOTE: We keep track of the annotation indices as used in `PairwiseDistances'\n",
" # as well as by the \"direct clustering\" method from above, so that we can\n",
" # compare the clustering results later on.\n",
"\n",
" return Xs, ys, indices"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"builder = DatasetBuilder(ds, width=112, height=112, bbox_margin=10)\n",
"Xs, ys, indices = builder(pd.valid_ann_ids, shape_clustering.labels_)O"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"n_classes = np.unique(ys).shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The full dataset contains 19856/22117 individual building images.\n",
"The number of classes it 25 (out of a total of 200 clusters).\n"
]
}
],
"source": [
"print('The full dataset contains {}/{} individual '\n",
" 'building images.'.format(len(Xs), len(pd.valid_ann_ids)))\n",
"print('The number of classes it {} (out of a total of {} clusters).'.format(n_classes, N_CLUSTERS))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualize a few of them to make sure we extract the images correctly."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
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\" y=\"-6.24\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"axes_2\">\n",
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\" y=\"-6.24\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"axes_3\">\n",
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\" y=\"-6.24\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"axes_4\">\n",
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\" y=\"-6.24\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"axes_5\">\n",
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y=\"-6.24\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"p6903da5b79\">\n",
" <rect height=\"131.04\" width=\"131.04\" x=\"7.2\" y=\"7.2\"/>\n",
" </clipPath>\n",
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" </clipPath>\n",
" </defs>\n",
"</svg>\n"
],
"text/plain": [
"<Figure size 720x288 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"n_samples = 5\n",
"sample_indices = np.random.choice(len(Xs), size=n_samples)\n",
"\n",
"f, axes = plt.subplots(1, n_samples, figsize=(n_samples * 2, 4))\n",
"for i, idx in enumerate(sample_indices):\n",
" axes[i].imshow(Xs[idx])\n",
" axes[i].set_axis_off()\n",
"f.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Serialize them."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"np.save(os.path.join(save_dir, 'Xs.npy'), Xs)\n",
"np.save(os.path.join(save_dir, 'ys.npy'), ys)\n",
"np.save(os.path.join(save_dir, 'indices.npy'), indices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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