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APP.py
Source:APP.py 
1import os2import flask3import dash4import dash_daq as daq5import dash_core_components as dcc6import dash_html_components as html7import pandas as pd89#lecture et initialisation des données10df = pd.read_csv('data.csv', delimiter=',', names=['year','temperature','moisture','humidity'])1112def render_gauge(name, parameter, unit):13 return daq.Gauge(14 className= 'gauge',15 label = name,16 value = df.loc[len(df)-1, parameter],17 max = 100,18 min = 0,19 showCurrentValue = True,20 units = unit,21 color = {"gradient":True,"ranges":{"green":[0,60],"yellow":[60,80],"red":[80,100]}},22 scale = {'start':0, 'interval': 5},23 )24config = {'displayModeBar': False,'displaylogo': False, 'scrollZoom': True}25def render_graph(yvalue, label):26 return dcc.Graph( 27 config= config,28 figure =dict(29 data=[30 dict(31 x=df['year'], y=df[yvalue], type='bar',32 )33 ],34 layout=dict(35 title = label + ' VS Temps',36 xaxis = dict(37 title= 'Temps',38 titlefont=dict(39 family = 'Arial',40 size = 20,41 color = '#7FDBFF'42 )43 ),44 yaxis = dict(45 title= label,46 titlefont=dict(47 family = 'Arial',48 size = 20,49 color = '#7FDBFF'50 )51 ),52 plot_bgcolor= '#1d242e',53 paper_bgcolor= '#1d242e',54 font= {'color': '#7FDBFF'}, 55 )56 ),57 style={'overflowX':'scroll'},58 id= "graph"+label,59 )6061#initialisation de la page dash62app = dash.Dash(__name__, title='UI pour IRESEN',63 meta_tags=[{"name":"description","content":"my UI app"},{"name": "viewport", "content": "width=device-width, initial-scale=1"}])64server = app.server 6566#visualisation de la page67app.layout =html.Div(68 [ 69 html.Header(className="flex-display row", children=[70 html.Img(src=app.get_asset_url('domaines.webp'), className='icon', alt='icone des domaines'),71 html.Div(className='seven columns', children=[72 html.H1('Interface graphique pour serre intelligente', style={'textAlign': 'center', 'margin-top':'30px'}),73 html.Br(),74 html.H6('Visualisation des paramètres avec un serveur local', style={'textAlign': 'center', 'padding': 5}),75 ]),76 html.Img(src=app.get_asset_url("Iresen.webp"), className='icon_iresen', alt='icone IRESEN'),77 ]),78 html.Main([79 dcc.Tabs(id='tabs', value='tab_1', className='tabs-container', parent_className='custom-tabs', children=[80 dcc.Tab(label='Vue générale', value='tab_1', className='custom-tab', selected_className='custom-tab--selected', children=[81 html.Div(className='six columns', children=[82 html.H2(children='Image de la serre', className='twelve columns', style={'textAlign':'center'}),83 html.Img(src=app.get_asset_url('serre.webp'), className='serre', alt='image de la serre'),84 ]),85 html.Div(className='six columns', children=[86 html.H2(children='Valeur des capteurs', className='twelve columns', style={'textAlign':'center'}),87 html.Div([88 render_gauge('Humidité du sol', 'moisture', '%'),89 render_gauge('Température du sol', 'temperature', '°C'),90 render_gauge('Température ambiante', 'temperature', '°C'),91 render_gauge('Ensoleillement','moisture', '%'),92 render_gauge('CO2', 'humidity','mg/L'),93 render_gauge('Humidité relative', 'humidity', '%'),94 ])95 ]),96 ]),97 dcc.Tab(label='Humidité du sol', className='custom-tab', selected_className='custom-tab--selected', children=[98 html.Div(className= 'three columns', children=[99 html.H4(children='Emplacement des capteurs',className='twelve columns', style={'margin-left':20,'textAlign': 'center'}),100 html.Img(src=app.get_asset_url('serre moist.webp'), className='img_sensor', alt='image de la serre'),101 ]),102 html.Div(className='six columns', children=[103 html.H4(children='Humidité du sol', style={'textAlign': 'center'}),104 html.Div(className='offset-by-four columns',children=[105 render_gauge('', 'moisture', '%')106 ])107 ]),108 html.Div(className='three columns', children=[109 html.H4(children='Image du capteur'),110 html.Img(src=app.get_asset_url('moisture.webp'), className='img_capt', alt='image du capteur'),111 ]),112 html.Div(className='twelve columns', children=[113 render_graph('moisture','Humidité du sol')114 ])115 ]),116 dcc.Tab(label='Température du sol', className='custom-tab', selected_className='custom-tab--selected', children=[117 html.Div(className= 'three columns', children=[118 html.H4(children='Emplacement des capteurs', style={'textAlign': 'center', 'margin-left':20}),119 html.Img(src=app.get_asset_url('serre temp.webp'), className='img_sensor', alt='image de la serre'),120 ]),121 html.Div(className='six columns', children=[122 html.H4(children='Température du sol', style={'textAlign': 'center'}),123 html.Div(className='offset-by-four columns',children=[124 render_gauge('','temperature','°C')125 ])126 ]),127 html.Div(className='three columns', children=[128 html.H4(children='Image du capteur'),129 html.Img(src=app.get_asset_url('temp.webp'), className='img_capt', alt='image du capteur'),130 ]),131 html.Div(className='twelve columns', children=[132 render_graph('temperature','Température du sol') 133 ])134 ]),135 dcc.Tab(label='Température ambiante', className='custom-tab', selected_className='custom-tab--selected', children=[136 html.Div(className= 'three columns', children=[137 html.H4(children='Emplacement des capteurs', style={'textAlign': 'center', 'margin-left':20}),138 html.Img(src=app.get_asset_url('serre both.webp'), className='img_sensor', alt='image de la serre'),139 ]),140 html.Div(className='six columns', children=[141 html.H4(children='Température ambiante', style={'textAlign': 'center'}),142 html.Div(className='offset-by-four columns',children=[143 render_gauge('','temperature','°C')144 ])145 ]),146 html.Div(className='three columns', children=[147 html.H4(children='Image du capteur'),148 html.Img(src=app.get_asset_url('temp and humidity.webp'), className='img_capt', alt='image du capteur'),149 ]),150 html.Div(className='twelve columns', children=[151 render_graph('temperature','Température ambiante')152 ])153 ]),154 dcc.Tab(label='Ensoleillement', className='custom-tab', selected_className='custom-tab--selected', children=[155 html.Div(className= 'three columns', children=[156 html.H4(children='Emplacement des capteurs', style={'textAlign': 'center', 'margin-left':20}),157 html.Img(src=app.get_asset_url('serre enso.webp'), className='img_sensor', alt='image de la serre'),158 ]),159 html.Div(className='six columns', children=[160 html.H4(children='Ensoleillement', style={'textAlign': 'center'}),161 html.Div(className='offset-by-four columns',children=[162 render_gauge('','temperature','%')163 ])164 ]),165 html.Div(className='three columns', children=[166 html.H4(children='Image du capteur'),167 html.Img(src=app.get_asset_url('ensoleillement.webp'), className='img_capt', alt='image du capteur'),168 ]),169 html.Div(className='twelve columns', children=[170 render_graph('humidity','Ensoleillement')171 ])172 ]),173 dcc.Tab(label='CO2', className='custom-tab', selected_className='custom-tab--selected', children=[174 html.Div(className= 'three columns', children=[175 html.H4(children='Emplacement des capteurs', style={'textAlign': 'center', 'margin-left':20}),176 html.Img(src=app.get_asset_url('serre co.webp'), className='img_sensor', alt='image de la serre'),177 ]),178 html.Div(className='six columns', children=[179 html.H4(children='CO2', style={'textAlign': 'center'}),180 html.Div(className='offset-by-four columns',children=[181 render_gauge('','moisture','%')182 ])183 ]),184 html.Div(className='three columns', children=[185 html.H4(children='Image du capteur'),186 html.Img(src=app.get_asset_url('CO2.webp'), className='img_capt', alt='image du capteur'),187 ]),188 html.Div(className='twelve columns', children=[189 render_graph('moisture','CO2')190 ])191 ]), 192 dcc.Tab(label='Humidité relative', className='custom-tab', selected_className='custom-tab--selected', children=[193 html.Div(className= 'three columns', children=[194 html.H4(children='Emplacement des capteurs', style={'textAlign': 'center', 'margin-left':20}),195 html.Img(src=app.get_asset_url('serre both.webp'), className='img_sensor', alt='image de la serre'),196 ]),197 html.Div(className='six columns', children=[198 html.H4(children='Humidité relative', style={'textAlign': 'center'}),199 html.Div(className='offset-by-four columns',children=[200 render_gauge('','humidity','%')201 ])202 ]),203 html.Div(className='three columns', children=[204 html.H4(children='Image du capteur'),205 html.Img(src=app.get_asset_url('temp and humidity.webp'), className='img_capt', alt='image du capteur'),206 ]),207 html.Div(className='twelve columns', children=[208 render_graph('humidity','Humidité relative')209 ])210 ]), 211 ])212 ])213])214215216class MyFlask(flask.Flask):217 def get_send_file_max_age(self, name):218 if name.lower().endswith('.webp'):219 return 60220 if name.lower().endswith('.css?m=1596413279.0'):221 return 60222 return flask.Flask.get_send_file_max_age(self, name)223224@server.route('/assets/<path:path>')225def serve_static(path):226 root_dir = os.getcwd()227 return flask.send_from_directory(os.path.joint(root_dir, 'assets'),path)228229if __name__ == '__main__':
...
modelPredict2.py
Source:modelPredict2.py
1import dash_core_components as dcc2import dash_html_components as html3def renderModelPredict() :4 return html.Div([5 html.H1('Test Saham', className='h1'),6 html.Div(children=[7 html.Div([8 html.P('Tanggal mulai prediksi : ')9 ],className='col-2'),10 html.Div([11 dcc.Input(id='datein', type='number', value='12')12 ],className='col-4'),13 html.Div([14 html.P('Tanggal akhir prediksi : ')15 ],className='col-2'),16 html.Div([17 dcc.Input(id='dateout', type='number', value='12')18 ],className='col-4'),19 html.Div([20 html.P('Bulan mulai prediksi : ')21 ],className='col-2'),22 html.Div([23 dcc.Input(id='monthin', type='number', value='12')24 ],className='col-4'),25 html.Div([26 html.P('Bulan akhir prediksi : ')27 ],className='col-2'),28 html.Div([29 dcc.Input(id='monthout', type='number', value='12')30 ],className='col-4'),31 html.Div([32 html.P('Tahun mulai prediksi : ')33 ],className='col-2'),34 html.Div([35 dcc.Input(id='yearin', type='number', value='2000')36 ],className='col-4'),37 html.Div([38 html.P('Tahun akhir prediksi : ')39 ],className='col-2'),40 html.Div([41 dcc.Input(id='yearout', type='number', value='2100')42 ],className='col-4'),43 html.Div([44 html.P('Data Train : ')45 ],className='col-2'),46 html.Div([47 dcc.Input(id='train', type='number', value='1000')48 ],className='col-4'),49 html.Div([50 html.P('Data Test : ')51 ],className='col-2'),52 html.Div([53 dcc.Input(id='test', type='number', value='1000')54 ],className='col-4'),55 html.Div([56 html.P('Symbol saham : ')57 ],className='col-2'),58 html.Div([59 dcc.Input(id='symbol', type='text')60 ],className='col-4'),61 html.Div([62 html.P('Jenis Machine Learning : ')63 ],className='col-2'),64 html.Div([65 dcc.Dropdown(id='ML', options=[66 {'label' : 'Moving Averange', 'value' : 'MA'},67 {'label' : 'Linear Regresression', 'value' : 'LR'},68 {'label' : 'On Proggress', 'value' : 'KNN'},69 {'label' : 'On Proggress', 'value' : 'AA'},70 {'label' : 'On Proggress', 'value' : 'LSTM'}71 ], value='None.1' )72 ],className='col-4'),73 html.Div([74 html.A("Daftar Kode Saham", href='https://www.idx.co.id/data-pasar/data-saham/daftar-saham/', target="_blank")75 ],className='col-2'),76 html.Div([77 html.Button('Predict', type='submit', id='buttonPredict', className='btn btn-primary')78 # html.A(79 # html.Button('Predict', id='buttonPredict', className='btn btn-primary'),80 # href = 'http://127.0.0.1:2019/'81 # )82 ],className='mx-auto', style={ 'paddingTop': '20px', 'paddingBottom': '20px' })83 ],className='row'),84 html.Div([85 html.H2('', id='outputPredict', className='mx-auto')86 ], className='row')87 ])88 89def renderModelPredict1() :90 return html.Div([91 html.H1('Test Saham 2', className='h1'),92 html.Div(children=[93 html.Div([94 html.P('Tanggal mulai prediksi : ')95 ],className='col-2'),96 html.Div([97 dcc.Input(id='datein1', type='number', value='12')98 ],className='col-4'),99 html.Div([100 html.P('Tanggal akhir prediksi : ')101 ],className='col-2'),102 html.Div([103 dcc.Input(id='dateout1', type='number', value='12')104 ],className='col-4'),105 html.Div([106 html.P('Bulan mulai prediksi : ')107 ],className='col-2'),108 html.Div([109 dcc.Input(id='monthin1', type='number', value='12')110 ],className='col-4'),111 112 html.Div([113 html.P('Bulan akhir prediksi : ')114 ],className='col-2'),115 html.Div([116 dcc.Input(id='monthout1', type='number', value='12')117 ],className='col-4'),118 html.Div([119 html.P('Tahun mulai prediksi : ')120 ],className='col-2'),121 html.Div([122 dcc.Input(id='yearin1', type='number', value='2000')123 ],className='col-4'),124 html.Div([125 html.P('Tahun akhir prediksi : ')126 ],className='col-2'),127 html.Div([128 dcc.Input(id='yearout1', type='number', value='2100')129 ],className='col-4'),130 html.Div([131 html.P('Data Train : ')132 ],className='col-2'),133 html.Div([134 dcc.Input(id='train1', type='number', value='1000')135 ],className='col-4'),136 html.Div([137 html.P('Data Test : ')138 ],className='col-2'),139 html.Div([140 dcc.Input(id='test1', type='number', value='1000')141 ],className='col-4'),142 html.Div([143 html.P('Symbol saham : ')144 ],className='col-2'),145 html.Div([146 dcc.Input(id='symbol1', type='text')147 ],className='col-4'),148 html.Div([149 html.P('Jenis Machine Learning : ')150 ],className='col-2'),151 html.Div([152 dcc.Dropdown(id='ML1', options=[153 {'label' : 'Moving Averange', 'value' : 'MA'},154 {'label' : 'Linear Regresression', 'value' : 'LR'},155 {'label' : 'On Proggress', 'value' : 'KNN'},156 {'label' : 'On Proggress', 'value' : 'AA'},157 {'label' : 'On Proggress', 'value' : 'LSTM'}158 ], value='None.1' )159 ],className='col-4'),160 html.Div([161 html.Button('Predict', type='submit', id='buttonPredict1', className='btn btn-primary')162 # html.A(163 # html.Button('Predict', id='buttonPredict', className='btn btn-primary'),164 # href = 'http://127.0.0.1:2019/'165 # )166 ],className='mx-auto', style={ 'paddingTop': '20px', 'paddingBottom': '20px' })167 ],className='row'),168 html.Div([169 html.H2('', id='outputPredict1', className='mx-auto')170 ], className='row')171 ])172 173def renderModelPredict2() :174 return html.Div([175 html.H1('Test Saham Trading Signal', className='h1'),176 html.Div(children=[177 html.Div([178 html.P('Tanggal mulai prediksi : ')179 ],className='col-2'),180 html.Div([181 dcc.Input(id='datein2', type='number', value='12')182 ],className='col-4'),183 html.Div([184 html.P('Tanggal akhir prediksi : ')185 ],className='col-2'),186 html.Div([187 dcc.Input(id='dateout2', type='number', value='12')188 ],className='col-4'),189 html.Div([190 html.P('Bulan mulai prediksi : ')191 ],className='col-2'),192 html.Div([193 dcc.Input(id='monthin2', type='number', value='12')194 ],className='col-4'),195 196 html.Div([197 html.P('Bulan akhir prediksi : ')198 ],className='col-2'),199 html.Div([200 dcc.Input(id='monthout2', type='number', value='12')201 ],className='col-4'),202 html.Div([203 html.P('Tahun mulai prediksi : ')204 ],className='col-2'),205 html.Div([206 dcc.Input(id='yearin2', type='number', value='2000')207 ],className='col-4'),208 html.Div([209 html.P('Tahun akhir prediksi : ')210 ],className='col-2'),211 html.Div([212 dcc.Input(id='yearout2', type='number', value='2100')213 ],className='col-4'),214 html.Div([215 html.P('Symbol saham : ')216 ],className='col-2'),217 html.Div([218 dcc.Input(id='symbol2', type='text')219 ],className='col-4'),220 html.Div([221 html.P('Cash (0-100000): ')222 ],className='col-2'),223 html.Div([224 dcc.Input(id='cash', type='number', value='100000')225 ],className='col-4'),226 html.Div([227 html.P('Stoploss (0.1-0.9) : ')228 ],className='col-2'),229 html.Div([230 dcc.Input(id='stoploss', type='number', value='1000')231 ],className='col-4'),232 html.Div([233 html.P('batch (0-10000): ')234 ],className='col-2'),235 html.Div([236 dcc.Input(id='batch', type='text')237 ],className='col-4'),238 html.Div([239 html.P('Port Value (0.1-0.9): ')240 ],className='col-2'),241 html.Div([242 dcc.Input(id='portvalue', type='text')243 ],className='col-4'),244 html.Div([245 html.Button('Predict', type='submit', id='buttonPredict2', className='btn btn-primary')246 # html.A(247 # html.Button('Predict', id='buttonPredict', className='btn btn-primary'),248 # href = 'http://127.0.0.1:2019/'249 # )250 ],className='mx-auto', style={ 'paddingTop': '20px', 'paddingBottom': '20px' })251 ],className='row'),252 html.Div([253 html.H2('', id='outputPredict2', className='mx-auto')254 ], className='row')255 ])...
eval_det.py
Source:eval_det.py
1""" 2 Generic Code for Object Detection Evaluation3 From: https://github.com/facebookresearch/votenet/blob/master/utils/eval_det.py4 Input:5 For each class:6 For each image:7 Predictions: box, score8 Groundtruths: box9 10 Output:11 For each class:12 precision-recal and average precision13 14 Author: Charles R. Qi15 16 Ref: https://raw.githubusercontent.com/rbgirshick/py-faster-rcnn/master/lib/datasets/voc_eval.py17"""18import numpy as np19def voc_ap(rec, prec, use_07_metric=False):20 """ ap = voc_ap(rec, prec, [use_07_metric])21 Compute VOC AP given precision and recall.22 If use_07_metric is true, uses the23 VOC 07 11 point method (default:False).24 """25 if use_07_metric:26 # 11 point metric27 ap = 0.28 for t in np.arange(0., 1.1, 0.1):29 if np.sum(rec >= t) == 0:30 p = 031 else:32 p = np.max(prec[rec >= t])33 ap = ap + p / 11.34 else:35 # correct AP calculation36 # first append sentinel values at the end37 mrec = np.concatenate(([0.], rec, [1.]))38 mpre = np.concatenate(([0.], prec, [0.]))39 # compute the precision envelope40 for i in range(mpre.size - 1, 0, -1):41 mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])42 # to calculate area under PR curve, look for points43 # where X axis (recall) changes value44 i = np.where(mrec[1:] != mrec[:-1])[0]45 # and sum (\Delta recall) * prec46 ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])47 return ap48import os49import sys50BASE_DIR = os.path.dirname(os.path.abspath(__file__))51from utils.metric_util import calc_iou # axis-aligned 3D box IoU52def get_iou(bb1, bb2):53 """ Compute IoU of two bounding boxes.54 ** Define your bod IoU function HERE **55 """56 #pass57 iou3d = calc_iou(bb1, bb2)58 return iou3d59from utils.box_util import box3d_iou60def get_iou_obb(bb1,bb2):61 iou3d = box3d_iou(bb1,bb2)62 return iou3d63def get_iou_main(get_iou_func, args):64 return get_iou_func(*args)65def eval_det_cls(pred, gt, ovthresh=0.25, use_07_metric=False, get_iou_func=get_iou):66 """ Generic functions to compute precision/recall for object detection67 for a single class.68 Input:69 pred: map of {img_id: [(bbox, score)]} where bbox is numpy array70 gt: map of {img_id: [bbox]}71 ovthresh: scalar, iou threshold72 use_07_metric: bool, if True use VOC07 11 point method73 Output:74 rec: numpy array of length nd75 prec: numpy array of length nd76 ap: scalar, average precision77 """78 # construct gt objects79 class_recs = {} # {img_id: {'bbox': bbox list, 'det': matched list}}80 npos = 081 for img_id in gt.keys():82 bbox = np.array(gt[img_id])83 det = [False] * len(bbox)84 npos += len(bbox)85 class_recs[img_id] = {'bbox': bbox, 'det': det}86 # pad empty list to all other imgids87 for img_id in pred.keys():88 if img_id not in gt:89 class_recs[img_id] = {'bbox': np.array([]), 'det': []}90 # construct dets91 image_ids = []92 confidence = []93 BB = []94 for img_id in pred.keys():95 for box,score in pred[img_id]:96 image_ids.append(img_id)97 confidence.append(score)98 BB.append(box)99 confidence = np.array(confidence)100 BB = np.array(BB) # (nd,4 or 8,3 or 6)101 # sort by confidence102 sorted_ind = np.argsort(-confidence)103 sorted_scores = np.sort(-confidence)104 BB = BB[sorted_ind, ...]105 image_ids = [image_ids[x] for x in sorted_ind]106 # go down dets and mark TPs and FPs107 nd = len(image_ids)108 tp = np.zeros(nd)109 fp = np.zeros(nd)110 for d in range(nd):111 #if d%100==0: print(d)112 R = class_recs[image_ids[d]]113 bb = BB[d,...].astype(float)114 ovmax = -np.inf115 BBGT = R['bbox'].astype(float)116 if BBGT.size > 0:117 # compute overlaps118 for j in range(BBGT.shape[0]):119 iou = get_iou_main(get_iou_func, (bb, BBGT[j,...]))120 if iou > ovmax:121 ovmax = iou122 jmax = j123 #print d, ovmax124 if ovmax > ovthresh:125 if not R['det'][jmax]:126 tp[d] = 1.127 R['det'][jmax] = 1128 else:129 fp[d] = 1.130 else:131 fp[d] = 1.132 # compute precision recall133 fp = np.cumsum(fp)134 tp = np.cumsum(tp)135 rec = tp / float(npos + 1e-8)136 #print('NPOS: ', npos)137 # avoid divide by zero in case the first detection matches a difficult138 # ground truth139 prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)140 ap = voc_ap(rec, prec, use_07_metric)141 return rec, prec, ap142def eval_det_cls_wrapper(arguments):143 pred, gt, ovthresh, use_07_metric, get_iou_func = arguments144 rec, prec, ap = eval_det_cls(pred, gt, ovthresh, use_07_metric, get_iou_func)145 return (rec, prec, ap)146def eval_det(pred_all, gt_all, ovthresh=0.25, use_07_metric=False, get_iou_func=get_iou):147 """ Generic functions to compute precision/recall for object detection148 for multiple classes.149 Input:150 pred_all: map of {img_id: [(classname, bbox, score)]}151 gt_all: map of {img_id: [(classname, bbox)]}152 ovthresh: scalar, iou threshold153 use_07_metric: bool, if true use VOC07 11 point method154 Output:155 rec: {classname: rec}156 prec: {classname: prec_all}157 ap: {classname: scalar}158 """159 pred = {} # map {classname: pred}160 gt = {} # map {classname: gt}161 for img_id in pred_all.keys():162 for classname, bbox, score in pred_all[img_id]:163 if classname not in pred: pred[classname] = {}164 if img_id not in pred[classname]:165 pred[classname][img_id] = []166 if classname not in gt: gt[classname] = {}167 if img_id not in gt[classname]:168 gt[classname][img_id] = []169 pred[classname][img_id].append((bbox,score))170 for img_id in gt_all.keys():171 for classname, bbox in gt_all[img_id]:172 if classname not in gt: gt[classname] = {}173 if img_id not in gt[classname]:174 gt[classname][img_id] = []175 gt[classname][img_id].append(bbox)176 rec = {}177 prec = {}178 ap = {}179 for classname in gt.keys():180 print('Computing AP for class: ', classname)181 rec[classname], prec[classname], ap[classname] = eval_det_cls(pred[classname], gt[classname], ovthresh, use_07_metric, get_iou_func)182 print(classname, ap[classname])183 184 return rec, prec, ap 185from multiprocessing import Pool186def eval_det_multiprocessing(pred_all, gt_all, ovthresh=0.25, use_07_metric=False, get_iou_func=get_iou):187 """ Generic functions to compute precision/recall for object detection188 for multiple classes.189 Input:190 pred_all: map of {img_id: [(classname, bbox, score)]}191 gt_all: map of {img_id: [(classname, bbox)]}192 ovthresh: scalar, iou threshold193 use_07_metric: bool, if true use VOC07 11 point method194 Output:195 rec: {classname: rec}196 prec: {classname: prec_all}197 ap: {classname: scalar}198 """199 pred = {} # map {classname: pred}200 gt = {} # map {classname: gt}201 for img_id in pred_all.keys():202 for classname, bbox, score in pred_all[img_id]:203 if classname not in pred: pred[classname] = {}204 if img_id not in pred[classname]:205 pred[classname][img_id] = []206 if classname not in gt: gt[classname] = {}207 if img_id not in gt[classname]:208 gt[classname][img_id] = []209 pred[classname][img_id].append((bbox,score))210 for img_id in gt_all.keys():211 for classname, bbox in gt_all[img_id]:212 if classname not in gt: gt[classname] = {}213 if img_id not in gt[classname]:214 gt[classname][img_id] = []215 gt[classname][img_id].append(bbox)216 rec = {}217 prec = {}218 ap = {}219 p = Pool(processes=10)220 ret_values = p.map(eval_det_cls_wrapper, [(pred[classname], gt[classname], ovthresh, use_07_metric, get_iou_func) for classname in gt.keys() if classname in pred])221 p.close()222 for i, classname in enumerate(gt.keys()):223 if classname in pred:224 rec[classname], prec[classname], ap[classname] = ret_values[i]225 else:226 rec[classname] = 0227 prec[classname] = 0228 ap[classname] = 0229 print('eval per class', classname, ap[classname])230 ...
ClassDictionary.py
Source:ClassDictionary.py
1# !/usr/bin/python2# This file is part of snavtofamix (Source Navigator to FAMIX).3#4# snavtofamix is free software; you can redistribute it and/or modify it5# under the terms of the GNU General Public License as published by the6# Free Software Foundation; either version 2 of the License, or (at your7# option) any later version.8#9# snavtofamix is distributed in the hope that it will be useful, but WITHOUT10# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS11# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more12# details.13#14# You should have received a copy of the GNU General Public License along15# with snavtofamix; if not, write to the Free Software Foundation, Inc.,16# 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA17#18# Copyright 2006,2007 University of Antwerp19# Author(s): Bart Van Rompaey <bart.vanrompaey2@ua.ac.be>,20# Bart Du Bois <bart.dubois@ua.ac.be>21from log4py import Logger22from cplusplus.data_types.ClassEntity import ClassReferenceEntity23from QualifiedNameHelperFunctions import getParentNamespaceName,\24 getNonQualifiedName25def getTemplateParameters(classData):26 return classData[0]27def getNamespaceName(classData):28 return classData[1]29def createClassData(templateParameters, namespaceName):30 return [templateParameters, namespaceName]31##32# Encapsulation of a dictionary of classes.33#34# Key: className35# Value: dictionary with36# Key: sourceFile37# Value: [lineNr1xClassData1,..,lineNrNxClassDataN]38# with lineNr x classData = a dictionary with key lineNr and value classData39# with classData = [ templateParameters, namespaceName ]40# with41# templateParameters = e.g., "X" the template parameter description in string-format42# namespaceName = the fully qualified name of the parent namespace43#44# Additional data that needs to be remembered for a class can be appended to classData.45##46class ClassDictionary:47 ##48 # Initialize a dictionary.49 ##50 def __init__(self):51 self.dict = {}52 self.log = Logger().get_instance(self)53 ##54 # Verify whether the dictionary contains a given class-name.55 ##56 def hasKey(self, className):57 return (className in self.dict)58 ##59 # Add a class contained in the given sourceFile at the given line-nr to the dictionary.60 #61 # @className - the name of the class62 # @sourceFile - the name of the file in which the class is declared63 # @lineNr - the line nr at which the class is declared in the source file64 # @classData - a list with data specific for the given class65 #66 # @returns True/False indicating whether the class was added67 ##68 def add(self, className, sourceFile, lineNr, classData):69 isAdded = False70 nonQualifiedClassName = getNonQualifiedName(className)71 parentNamespaceName = getParentNamespaceName(className)72 # adjust the given namespace name in case the classname73 # was qualified74 if parentNamespaceName != "":75 givenNamespaceName = getNamespaceName(classData)76 if givenNamespaceName != "":77 parentNamespaceName = givenNamespaceName + "::" + parentNamespaceName78 classData[1] = parentNamespaceName79 if ( not(nonQualifiedClassName in self.dict) ):80 self.dict[nonQualifiedClassName] = {}81 if ( not(sourceFile in self.dict[nonQualifiedClassName]) ):82 self.dict[nonQualifiedClassName][sourceFile] = {}83 if ( not(lineNr in self.dict[nonQualifiedClassName][sourceFile]) ):84 self.dict[nonQualifiedClassName][sourceFile][lineNr]=classData85 isAdded = True86 return isAdded87 def getClassesByName(self, className):88 classList = []89 if className in self.dict:90 for sourceFile in self.dict[className]:91 for lineNr in self.dict[className][sourceFile]:92 classData = self.dict[className][sourceFile][lineNr]93 classRefEntity = ClassReferenceEntity(className, getNamespaceName(classData), sourceFile, lineNr, getTemplateParameters(classData))94 classList.append(classRefEntity)95 return classList96 ##97 # Retrieve a list of [sourceFile, lineNr] elements for which it holds that in98 # sourceFile at lineNr a class with name className is declared.99 #100 # @param className - the class name for which to find source locations.101 #102 # @returns a list of elements [sourceFile, lineNr]103 ##104 def getSourceLocations(self, className):105 sourceLocations=[]106 if ( className in self.dict ):107 for sourceFile in self.dict[className]:108 for lineNr in self.dict[className][sourceFile]:109 sourceLocations.append([sourceFile,lineNr])110 return sourceLocations111 def getClassesByNamespace(self, className, namespaceName):112 classList = []113 classNameWithoutTemplates = className114 templatePars = ""115 if "<" in className:116 classNameWithoutTemplates = className.split("<")[0]117 templatePars = "<".join(className.split("<")[1:])118 if ">" in templatePars:119 parts = templatePars.split(">")120 templatePars = ">".join(parts[0:len(parts)-1])121 if classNameWithoutTemplates in self.dict:122 for sourceFile in self.dict[classNameWithoutTemplates]:123 for lineNr in self.dict[classNameWithoutTemplates][sourceFile]:124 classData = self.dict[classNameWithoutTemplates][sourceFile][lineNr]125 if getNamespaceName(classData) == namespaceName:126 classRefEntity = ClassReferenceEntity(className, getNamespaceName(classData), sourceFile, lineNr, getTemplateParameters(classData))127 classList.append(classRefEntity)128 return classList129 ##130 # Retrieve a list with data on the class with the given properties.131 # If no such class exists, [] is returned.132 #133 # Currently, classData = [ templateParameters ]134 # with templateParameters = e.g., "X" the template parameter description in string-format135 ##136 def getClassData(self, className, sourceFile, lineNr):137 classData=[]138 if className in self.dict :139 if sourceFile in self.dict[className]:140 if lineNr in self.dict[className][sourceFile]:141 classData = self.dict[className][sourceFile][lineNr]142 return classData143 ##144 # Retrieve a reference to the unique class satisfying the given properties.145 ##146 def getClassReference(self, className, sourceFile, lineNr):147 classReference = None148 if className in self.dict :149 if sourceFile in self.dict[className]:150 if lineNr in self.dict[className][sourceFile]:151 classData = self.dict[className][sourceFile][lineNr]152 classReference = ClassReferenceEntity(className, getNamespaceName(classData), sourceFile, lineNr, getTemplateParameters(classData))153 return classReference154 ##155 # Verify whether the dictionary contains an enty for the given156 # classname in the given namespaceName at the given lineNr in the given157 # sourceFile.158 #159 # This is relevant in scenario's where you do not wish to add two160 # declarations for the same parameters at different line numbers.161 ##162 def containsClass(self, className, sourceFile, namespaceName):163 exists = False164 # In case there exist multiple class definitions with identical method signatures165 # in the same file, we will never be able to retrieve the right target and just166 # drop the invocations.167 if ( (className in self.dict) and (sourceFile in self.dict[className]) ):168 for anyLineNrDeclaringClassInFile in self.dict[className][sourceFile]:169 classData = self.dict[className][sourceFile][anyLineNrDeclaringClassInFile]170 if getNamespaceName(classData) == namespaceName:171 exists = True172 return exists173 174 def createLocationBasedDictionary(self):175 """Creates a class dictionary source location as key and class name as value"""176 locClDict = {}177 for className in self.dict:178 for sourceFile in self.dict[className]:179 for lineNr in self.dict[className][sourceFile]:180 loc = sourceFile+":"+lineNr181 if loc in locClDict:182 locClDict[loc].append(className)183 else:184 locClDict[loc] = [ className ]185 return locClDict186 187 ##188 # Print the contents of the dictionary in the following formats.189 ##190 def printContent(self):191 self.log.info( "ClassDictionary contents:")192 for className in self.dict:193 for sourceFile in self.dict[className]:194 for lineNr in self.dict[className][sourceFile]:...
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