1-Ph3~ddlZddlmZddlmZddZdZejd  ddddZ dS)N) _hoghistogram)utilsh㈵>c|dkr.|tjtj||zz }n |dkr?tj|tjtj||zz }n|dkr3|tjtj|dz|dzzz }n|dkrz|tjtj|dz|dzzz }tj|d}|tjtj|dz|dzzz }nt d|S)NL1zL1-sqrtL2rL2-Hysg?z/Selected block normalization method is invalid.)npsumabssqrtminimum ValueError)blockmethodepsouts T/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/feature/_hog.py_hog_normalize_blockrs" ~~rvbfUmm,,s23 9  gervbfUmm44s:;<< 4bgbfUAX..a7888 8  bgbfUAX..a7888jc""BGBF36NNS!V3444JKKK Jcjtj|j|j}d|dddf<d|dddf<|ddddf|ddddfz |ddddf<tj|j|j}d|dddf<d|dddf<|ddddf|ddddfz |ddddf<||fS)aCompute unnormalized gradient image along `row` and `col` axes. Parameters ---------- channel : (M, N) ndarray Grayscale image or one of image channel. Returns ------- g_row, g_col : channel gradient along `row` and `col` axes correspondingly. dtyperNrr)r emptyshaper)channelg_rowg_cols r_hog_channel_gradientr#s HW]'- 8 8 8EE!QQQ$KE"aaa%LQRRU^gcrc111fo5E!B$'N HW]'- 8 8 8EE!!!Q$KE!!!R%LQQQU^gaaa"fo5E!!!QrT'N %<rF)multichannel_output r'r)r T) channel_axisc  tj|}tj|j} || d}|du} | r |jdz n|j} | dkrtd |rtj|} | rMtj || } tj || } tj || }t|j dD]n}t|dddd|f\| dddd|f<| dddd|f<tj | dddd|f| dddd|f|dddd|f<o|d}tjtj|j d tj|j dd d \}}| |||f}| |||f}nt|\}} |j dd\}}|\}}|\}}t#||z}t#||z}tj|||ft&}|t&d}|t&d}t)j|||||||||| d}|rxdd lm}t1||dzdz } tj|}!tj|!dzz|z }"| tj|"z}#| tj|"z}$tj||f| }t|D]}%t|D]}&t9|!|#|$D]\}'}(})t;|%|z|dzz|&|z|dzzg}*|t#|*d |)z t#|*d|(zt#|*d |)zt#|*d|(z \}}|||fxx||%|&|'fz cc<̌ ||z dz}+||z dz},|,d ks|+d kr ||z}-||z}.td|-d|.dtj|+|,|||f| }/t|+D]D}%t|,D]2}&||%|%|z|&|&|zddf}0t?|0||/|%|&ddf<3E |r|/ }/|r|/|fS|/S)aExtract Histogram of Oriented Gradients (HOG) for a given image. Compute a Histogram of Oriented Gradients (HOG) by 1. (optional) global image normalization 2. computing the gradient image in `row` and `col` 3. computing gradient histograms 4. normalizing across blocks 5. flattening into a feature vector Parameters ---------- image : (M, N[, C]) ndarray Input image. orientations : int, optional Number of orientation bins. pixels_per_cell : 2-tuple (int, int), optional Size (in pixels) of a cell. cells_per_block : 2-tuple (int, int), optional Number of cells in each block. block_norm : str {'L1', 'L1-sqrt', 'L2', 'L2-Hys'}, optional Block normalization method: ``L1`` Normalization using L1-norm. ``L1-sqrt`` Normalization using L1-norm, followed by square root. ``L2`` Normalization using L2-norm. ``L2-Hys`` Normalization using L2-norm, followed by limiting the maximum values to 0.2 (`Hys` stands for `hysteresis`) and renormalization using L2-norm. (default) For details, see [3]_, [4]_. visualize : bool, optional Also return an image of the HOG. For each cell and orientation bin, the image contains a line segment that is centered at the cell center, is perpendicular to the midpoint of the range of angles spanned by the orientation bin, and has intensity proportional to the corresponding histogram value. transform_sqrt : bool, optional Apply power law compression to normalize the image before processing. DO NOT use this if the image contains negative values. Also see `notes` section below. feature_vector : bool, optional Return the data as a feature vector by calling .ravel() on the result just before returning. channel_axis : int or None, optional If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels. .. versionadded:: 0.19 `channel_axis` was added in 0.19. Returns ------- out : (n_blocks_row, n_blocks_col, n_cells_row, n_cells_col, n_orient) ndarray HOG descriptor for the image. If `feature_vector` is True, a 1D (flattened) array is returned. hog_image : (M, N) ndarray, optional A visualisation of the HOG image. Only provided if `visualize` is True. Raises ------ ValueError If the image is too small given the values of pixels_per_cell and cells_per_block. References ---------- .. [1] https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients .. [2] Dalal, N and Triggs, B, Histograms of Oriented Gradients for Human Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005 San Diego, CA, USA, https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf, :DOI:`10.1109/CVPR.2005.177` .. [3] Lowe, D.G., Distinctive image features from scale-invatiant keypoints, International Journal of Computer Vision (2004) 60: 91, http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf, :DOI:`10.1023/B:VISI.0000029664.99615.94` .. [4] Dalal, N, Finding People in Images and Videos, Human-Computer Interaction [cs.HC], Institut National Polytechnique de Grenoble - INPG, 2006, https://tel.archives-ouvertes.fr/tel-00390303/file/NavneetDalalThesis.pdf Notes ----- The presented code implements the HOG extraction method from [2]_ with the following changes: (I) blocks of (3, 3) cells are used ((2, 2) in the paper); (II) no smoothing within cells (Gaussian spatial window with sigma=8pix in the paper); (III) L1 block normalization is used (L2-Hys in the paper). Power law compression, also known as Gamma correction, is used to reduce the effects of shadowing and illumination variations. The compression makes the dark regions lighter. When the kwarg `transform_sqrt` is set to ``True``, the function computes the square root of each color channel and then applies the hog algorithm to the image. F)copyNrrzwOnly images with two spatial dimensions are supported. If using with color/multichannel images, specify `channel_axis`.r)axisrijT)indexingsparse)drawg?zoThe input image is too small given the values of pixels_per_cell and cells_per_block. 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