U fb@sddlZddlZddlZddlmZddlmZddl m Z ddl Z ddl mmZddlZddlZddlZdddZdddZd d Zdd d Zdd dZddZdS)N)Imagec Cst|D]D}|\}}}t|ddd}t|ddd}t||||d}q|rltd||d|nt |t ddS)aPlot annotations on images for debugging purposes Args: ds: a deepforest pytorch dataset, see deepforest.dataset or deepforest.load_dataset() to start from a csv file savedir: optional path to save figures. If none (default) images will be interactively plotted color: color of the bounding box as a tuple of BGR color, e.g. orange annotations is (0, 165, 255) thickness: thickness of the rectangle border line in px rFscores)color thickness{}/{}N) iter format_boxesnpmoveaxisnumpyplot_predictionscv2imwriteformatimshowwaitKey) dssavedirrri image_pathimagetargetsdfrj/home/ec2-user/SageMaker/vegetation-management-remars2022/remars2022-workshop/libs/deepforest/visualize.py view_datasets   rTcCs^tj|dddddgd}|d|d<|rZ|d |d <|S) aXFormat a retinanet prediction into a pandas dataframe for a single image Args: prediction: a dictionary with keys 'boxes' and 'labels' coming from a retinanet scores: Whether boxes come with scores, during prediction, or without scores, as in during training. Returns: df: a pandas dataframe boxesxminyminxmaxymax)columnslabelslabelrscore)pd DataFramecpudetachr) predictionrrrrrr "s  r cCsjt|dddddddf}t|}t||}t|dd}t||}d||}t|||S)zPlot an image, its predictions, and its ground truth targets for debugging Args: image: torch tensor, RGB color order targets: torch tensor Returns: figure_path: path on disk with saved figure NFr {}/{}.png)r arraycopyr rrrr)r predictionsr image_namerZ prediction_dfZ target_dfZ figure_pathrrrplot_prediction_and_targets5s&     r3c Csg}|dD]\}}ttd||dddddddf}t||}|dk rt||j|k}t||}|rd|t j |d} | | t | |q|S)aFor each row in dataframe, call plot predictions. For multi-class labels, boxes will be colored by labels. Ground truth boxes will all be same color, regardless of class. Args: df: a pandas dataframe with image_path, xmin, xmax, ymin, ymax and label columns. The image_path column should be the relative path from root_dir, not the full path. root_dir: relative dir to look for image names from df.image_path ground_truth: an optional pandas dataframe in same format as df holding ground_truth boxes savedir: save the plot to an optional directory path. Returns: written_figures: list of filenames written rr Nr-r.r)groupbyr r/ropenrr0rrospathsplitextappendrr) rroot_dir ground_truthrZwritten_figuresnamegroupr annotationsZ figure_namerrrplot_prediction_dataframeGs 4   r?c Cs|jddkrtd|jdkr*|d}|}|sPt|jsPt dd}| D]X\}}|spt |d}t j |t|d t|d ft|d t|d f||t jd qX|S)aBPlot a set of boxes on an image By default this function does not show, but only plots an axis Label column must be numeric! Image must be BGR color order! Args: image: a numpy array in *BGR* color order! Channel order is channels first df: a pandas dataframe with xmin, xmax, ymin, ymax and label column color: color of the bounding box as a tuple of BGR color, e.g. orange annotations is (0, 165, 255) thickness: thickness of the rectangle border line in px Returns: image: a numpy array with drawn annotations rzxInput images must be channels last format [h, w, 3] not channels first [3, h, w], use np.rollaxis(image, 0, 3) to invertfloat32uint8zXNo color was provided and the label column is not numeric. Using a single default color.)rr&r r!r"r#)rrZlineType)shape ValueErrordtypeastyper0ptypesis_numeric_dtyper&warningswarniterrowslabel_to_colorr rectangleintLINE_AA)rrrrindexrowrrrras      BrcCsi}tdddtdddD}dd|D}t|t|D]\}}|||<qFd|d<d|d<d |d <d |d <d |d<d|d<d|d<d|d<d|d<||S)NrcSs,g|]$}ttj|ddgdtqS)g?rD)list matplotlibcolors hsv_to_rgbrHrP.0xrrr sz"label_to_color..rg?cSsg|]}tdd|DqS)cSsg|] }t|qSr)rP)rYyrrrr[sz-label_to_color...)tuplerXrrrr[s)rDrDr)GcrD)rDrrr)2r`r@)prb)<)?ra)rD)rrD)randomseedr arangeshuffle enumerate)r& color_dictrVrRrrrrrNs"   rN)NNr)T)NN)Nr)r6pandasr(rUmatplotlib.pyplotpyplotpltmatplotlib.patchespatchesPILrrr Zpandas.api.typesapitypesrIrrqrKrr r3r?rrNrrrrs