U gb@s|dZddlZddlZddlZddlZddlZddl m Z ddl m Z ddl mZddl mZdddZd d Zdd d ZdS)z Evaluation module N)Image)IoU) check_file) visualizecs d}t|dkr(td|n|d}jddddd<tjdd jd dddd<tjdd t}|j fd d|d <|j fd d|d<|rt t d||dddddddf}tj|d}tj|dd}td||||S)a Compute intersection-over-union matching among prediction and ground truth boxes for one image Args: df: a pandas dataframe with columns name, xmin, xmax, ymin, ymax, label. The 'name' column should be the path relative to the location of the file. summarize: Whether to group statistics by plot and overall score image_coordinates: Whether the current boxes are in coordinate system of the image, e.g. origin (0,0) upper left. root_dir: Where to search for image names in df savedir: optional directory to save image with overlaid predictions and annotations Returns: result: pandas dataframe with crown ids of prediciton and ground truth and the IoU score. image_pathz,More than one plot passed to image crown: {}rcSstj|j|j|j|jSNshapelygeometryboxxminyminxmaxymaxxri/home/ec2-user/SageMaker/vegetation-management-remars2022/remars2022-workshop/libs/deepforest/evaluate.py"z evaluate_image..)axisr )r cSstj|j|j|j|jSrr rrrrr&rcst|rjj|S|Sr)pdnotnulllabellocr) predictionsrrr.rpredicted_labelcs jj|Sr)rrr) ground_dfrrr/r true_labelz{}/{}N)df)r)r!color)uniquelen ValueErrorformatapplygpd GeoDataFramerZ compute_IoU prediction_idtruth_idnparrayropenrplot_predictionscv2imwrite)rrroot_dirsavedir plot_names plot_nameresultimager)rrrevaluate_images.     0r:c Csi}i}i}||j}|jr0tdd}|S|dD]r\}}t|j|jk|jd||<||j|kjd}|dkrd||<nt|j|jk|||<|jd||<q:t | t |t |t |dj dd}|S)zGiven a set of evaluations, what proportion of predicted boxes match. True boxes which are not matched to predictions do not count against accuracy.zNo predictions madeNrr)rrecall precisionsizeTdrop) rnotnaemptyprintgroupbysumrshaper DataFramekeysSeries reset_index) resultsZclass_recall_dictZclass_precision_dictZ class_sizeZ box_results class_recallnamegroupZnumber_of_predictionsrrrcompute_class_recall9s" 2rN皙?c Cs2t|t|g}g}g}|dD]\}} ||d|kjdd} | jrt| jjddddd| jd} | d| | q&n| jdd} t | | ||d} || d<| j |k| d<t | d} | | j d} | | j d}| | | || | q&t|}t|}t|}t|}||||d S) auImage annotated crown evaluation routine submission can be submitted as a .shp, existing pandas dataframe or .csv path Args: predictions: a pandas dataframe, if supplied a root dir is needed to give the relative path of files in df.name. The labels in ground truth and predictions must match. If one is numeric, the other must be numeric. ground_df: a pandas dataframe, if supplied a root dir is needed to give the relative path of files in df.name root_dir: location of files in the dataframe 'name' column. Returns: results: a dataframe of match bounding boxes box_recall: proportion of true positives of box position, regardless of class box_precision: proportion of predictions that are true positive, regardless of class class_recall: a pandas dataframe of class level recall and precision with class sizes rTr>Nr)r-r,rrscorematchr)rrr4r5rQ)rJ box_precision box_recallrK)rrCrIrArrFindexvaluesrappendr:rrDrEconcatr.meanrN)rrr4 iou_thresholdr5rJZ box_recallsZbox_precisionsrrMZimage_predictionsr8 true_positiver;r<rRrSrKrrrevaluateSs>           r[)N)rON)__doc__pandasr geopandasr*r numpyr.r2PILrlibs.deepforestrZlibs.deepforest.utilitiesrrr:rNr[rrrrs     *