forked from 170010011/fr
296 lines
12 KiB
Python
296 lines
12 KiB
Python
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import numpy as np
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from . import _hoghistogram
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def _hog_normalize_block(block, method, eps=1e-5):
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if method == 'L1':
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out = block / (np.sum(np.abs(block)) + eps)
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elif method == 'L1-sqrt':
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out = np.sqrt(block / (np.sum(np.abs(block)) + eps))
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elif method == 'L2':
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out = block / np.sqrt(np.sum(block ** 2) + eps ** 2)
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elif method == 'L2-Hys':
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out = block / np.sqrt(np.sum(block ** 2) + eps ** 2)
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out = np.minimum(out, 0.2)
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out = out / np.sqrt(np.sum(out ** 2) + eps ** 2)
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else:
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raise ValueError('Selected block normalization method is invalid.')
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return out
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def _hog_channel_gradient(channel):
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"""Compute unnormalized gradient image along `row` and `col` axes.
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Parameters
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----------
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channel : (M, N) ndarray
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Grayscale image or one of image channel.
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Returns
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-------
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g_row, g_col : channel gradient along `row` and `col` axes correspondingly.
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"""
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g_row = np.empty(channel.shape, dtype=np.double)
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g_row[0, :] = 0
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g_row[-1, :] = 0
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g_row[1:-1, :] = channel[2:, :] - channel[:-2, :]
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g_col = np.empty(channel.shape, dtype=np.double)
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g_col[:, 0] = 0
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g_col[:, -1] = 0
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g_col[:, 1:-1] = channel[:, 2:] - channel[:, :-2]
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return g_row, g_col
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def hog(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3),
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block_norm='L2-Hys', visualize=False, transform_sqrt=False,
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feature_vector=True, multichannel=None):
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"""Extract Histogram of Oriented Gradients (HOG) for a given image.
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Compute a Histogram of Oriented Gradients (HOG) by
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1. (optional) global image normalization
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2. computing the gradient image in `row` and `col`
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3. computing gradient histograms
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4. normalizing across blocks
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5. flattening into a feature vector
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Parameters
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----------
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image : (M, N[, C]) ndarray
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Input image.
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orientations : int, optional
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Number of orientation bins.
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pixels_per_cell : 2-tuple (int, int), optional
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Size (in pixels) of a cell.
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cells_per_block : 2-tuple (int, int), optional
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Number of cells in each block.
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block_norm : str {'L1', 'L1-sqrt', 'L2', 'L2-Hys'}, optional
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Block normalization method:
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``L1``
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Normalization using L1-norm.
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``L1-sqrt``
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Normalization using L1-norm, followed by square root.
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``L2``
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Normalization using L2-norm.
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``L2-Hys``
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Normalization using L2-norm, followed by limiting the
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maximum values to 0.2 (`Hys` stands for `hysteresis`) and
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renormalization using L2-norm. (default)
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For details, see [3]_, [4]_.
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visualize : bool, optional
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Also return an image of the HOG. For each cell and orientation bin,
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the image contains a line segment that is centered at the cell center,
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is perpendicular to the midpoint of the range of angles spanned by the
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orientation bin, and has intensity proportional to the corresponding
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histogram value.
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transform_sqrt : bool, optional
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Apply power law compression to normalize the image before
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processing. DO NOT use this if the image contains negative
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values. Also see `notes` section below.
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feature_vector : bool, optional
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Return the data as a feature vector by calling .ravel() on the result
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just before returning.
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multichannel : boolean, optional
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If True, the last `image` dimension is considered as a color channel,
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otherwise as spatial.
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Returns
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-------
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out : (n_blocks_row, n_blocks_col, n_cells_row, n_cells_col, n_orient) ndarray
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HOG descriptor for the image. If `feature_vector` is True, a 1D
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(flattened) array is returned.
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hog_image : (M, N) ndarray, optional
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A visualisation of the HOG image. Only provided if `visualize` is True.
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References
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----------
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.. [1] https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
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.. [2] Dalal, N and Triggs, B, Histograms of Oriented Gradients for
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Human Detection, IEEE Computer Society Conference on Computer
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Vision and Pattern Recognition 2005 San Diego, CA, USA,
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https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf,
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:DOI:`10.1109/CVPR.2005.177`
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.. [3] Lowe, D.G., Distinctive image features from scale-invatiant
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keypoints, International Journal of Computer Vision (2004) 60: 91,
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http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf,
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:DOI:`10.1023/B:VISI.0000029664.99615.94`
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.. [4] Dalal, N, Finding People in Images and Videos,
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Human-Computer Interaction [cs.HC], Institut National Polytechnique
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de Grenoble - INPG, 2006,
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https://tel.archives-ouvertes.fr/tel-00390303/file/NavneetDalalThesis.pdf
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Notes
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-----
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The presented code implements the HOG extraction method from [2]_ with
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the following changes: (I) blocks of (3, 3) cells are used ((2, 2) in the
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paper); (II) no smoothing within cells (Gaussian spatial window with sigma=8pix
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in the paper); (III) L1 block normalization is used (L2-Hys in the paper).
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Power law compression, also known as Gamma correction, is used to reduce
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the effects of shadowing and illumination variations. The compression makes
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the dark regions lighter. When the kwarg `transform_sqrt` is set to
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``True``, the function computes the square root of each color channel
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and then applies the hog algorithm to the image.
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"""
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image = np.atleast_2d(image)
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if multichannel is None:
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multichannel = (image.ndim == 3)
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ndim_spatial = image.ndim - 1 if multichannel else image.ndim
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if ndim_spatial != 2:
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raise ValueError('Only images with 2 spatial dimensions are '
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'supported. If using with color/multichannel '
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'images, specify `multichannel=True`.')
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"""
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The first stage applies an optional global image normalization
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equalisation that is designed to reduce the influence of illumination
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effects. In practice we use gamma (power law) compression, either
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computing the square root or the log of each color channel.
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Image texture strength is typically proportional to the local surface
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illumination so this compression helps to reduce the effects of local
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shadowing and illumination variations.
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"""
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if transform_sqrt:
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image = np.sqrt(image)
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"""
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The second stage computes first order image gradients. These capture
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contour, silhouette and some texture information, while providing
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further resistance to illumination variations. The locally dominant
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color channel is used, which provides color invariance to a large
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extent. Variant methods may also include second order image derivatives,
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which act as primitive bar detectors - a useful feature for capturing,
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e.g. bar like structures in bicycles and limbs in humans.
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"""
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if image.dtype.kind == 'u':
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# convert uint image to float
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# to avoid problems with subtracting unsigned numbers
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image = image.astype('float')
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if multichannel:
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g_row_by_ch = np.empty_like(image, dtype=np.double)
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g_col_by_ch = np.empty_like(image, dtype=np.double)
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g_magn = np.empty_like(image, dtype=np.double)
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for idx_ch in range(image.shape[2]):
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g_row_by_ch[:, :, idx_ch], g_col_by_ch[:, :, idx_ch] = \
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_hog_channel_gradient(image[:, :, idx_ch])
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g_magn[:, :, idx_ch] = np.hypot(g_row_by_ch[:, :, idx_ch],
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g_col_by_ch[:, :, idx_ch])
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# For each pixel select the channel with the highest gradient magnitude
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idcs_max = g_magn.argmax(axis=2)
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rr, cc = np.meshgrid(np.arange(image.shape[0]),
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np.arange(image.shape[1]),
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indexing='ij',
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sparse=True)
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g_row = g_row_by_ch[rr, cc, idcs_max]
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g_col = g_col_by_ch[rr, cc, idcs_max]
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else:
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g_row, g_col = _hog_channel_gradient(image)
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"""
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The third stage aims to produce an encoding that is sensitive to
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local image content while remaining resistant to small changes in
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pose or appearance. The adopted method pools gradient orientation
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information locally in the same way as the SIFT [Lowe 2004]
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feature. The image window is divided into small spatial regions,
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called "cells". For each cell we accumulate a local 1-D histogram
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of gradient or edge orientations over all the pixels in the
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cell. This combined cell-level 1-D histogram forms the basic
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"orientation histogram" representation. Each orientation histogram
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divides the gradient angle range into a fixed number of
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predetermined bins. The gradient magnitudes of the pixels in the
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cell are used to vote into the orientation histogram.
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"""
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s_row, s_col = image.shape[:2]
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c_row, c_col = pixels_per_cell
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b_row, b_col = cells_per_block
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n_cells_row = int(s_row // c_row) # number of cells along row-axis
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n_cells_col = int(s_col // c_col) # number of cells along col-axis
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# compute orientations integral images
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orientation_histogram = np.zeros((n_cells_row, n_cells_col, orientations))
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_hoghistogram.hog_histograms(g_col, g_row, c_col, c_row, s_col, s_row,
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n_cells_col, n_cells_row,
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orientations, orientation_histogram)
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# now compute the histogram for each cell
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hog_image = None
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if visualize:
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from .. import draw
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radius = min(c_row, c_col) // 2 - 1
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orientations_arr = np.arange(orientations)
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# set dr_arr, dc_arr to correspond to midpoints of orientation bins
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orientation_bin_midpoints = (
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np.pi * (orientations_arr + .5) / orientations)
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dr_arr = radius * np.sin(orientation_bin_midpoints)
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dc_arr = radius * np.cos(orientation_bin_midpoints)
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hog_image = np.zeros((s_row, s_col), dtype=float)
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for r in range(n_cells_row):
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for c in range(n_cells_col):
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for o, dr, dc in zip(orientations_arr, dr_arr, dc_arr):
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centre = tuple([r * c_row + c_row // 2,
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c * c_col + c_col // 2])
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rr, cc = draw.line(int(centre[0] - dc),
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int(centre[1] + dr),
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int(centre[0] + dc),
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int(centre[1] - dr))
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hog_image[rr, cc] += orientation_histogram[r, c, o]
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"""
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The fourth stage computes normalization, which takes local groups of
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cells and contrast normalizes their overall responses before passing
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to next stage. Normalization introduces better invariance to illumination,
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shadowing, and edge contrast. It is performed by accumulating a measure
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of local histogram "energy" over local groups of cells that we call
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"blocks". The result is used to normalize each cell in the block.
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Typically each individual cell is shared between several blocks, but
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its normalizations are block dependent and thus different. The cell
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thus appears several times in the final output vector with different
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normalizations. This may seem redundant but it improves the performance.
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We refer to the normalized block descriptors as Histogram of Oriented
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Gradient (HOG) descriptors.
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"""
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n_blocks_row = (n_cells_row - b_row) + 1
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n_blocks_col = (n_cells_col - b_col) + 1
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normalized_blocks = np.zeros((n_blocks_row, n_blocks_col,
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b_row, b_col, orientations))
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for r in range(n_blocks_row):
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for c in range(n_blocks_col):
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block = orientation_histogram[r:r + b_row, c:c + b_col, :]
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normalized_blocks[r, c, :] = \
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_hog_normalize_block(block, method=block_norm)
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"""
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The final step collects the HOG descriptors from all blocks of a dense
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overlapping grid of blocks covering the detection window into a combined
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feature vector for use in the window classifier.
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"""
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if feature_vector:
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normalized_blocks = normalized_blocks.ravel()
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if visualize:
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return normalized_blocks, hog_image
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else:
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return normalized_blocks
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