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How to use test_2_2 method in Lemoncheesecake
Best Python code snippet using lemoncheesecake
evaluator.py
Source:evaluator.py 
1import numpy as np2from sklearn.metrics import recall_score, precision_score, f1_score3from dataloader import Dataloader4from distances import calculate_distances5from feature_extractor import compute_histogram6from metrics import mapk, averge_masks_metrics7from opt import parse_args8from utils import save_mask, load_pickle, save_predictions, detect_bboxes, detect_paintings, mkdir, transform_color, \9 remove_bg10import os11from denoise import detect_denoise12def calc_FV(image, opt, mask=None):13 return compute_histogram(opt.histogram, image, splits=opt.mr_splits, rec_level=opt.pyramid_rec_lvl, bins=opt.bins,14 mask=mask,15 sqrt=opt.sqrt,16 concat=opt.concat)17def eval_set(loader, gt_correspondences, bbdd_fvs, opt):18 masks_metrics = {"precision": [], "recall": [], "f1": []}19 ious = []20 predictions = []21 set_bboxes = []22 for name, query_image, gt_mask in loader:23 if opt.apply_denoise:24 query_image, Noise_level_before, Noise_level_after, blur_type_last = detect_denoise(query_image, opt.blur_type)25 # transform to another color space26 multiple_painting, split_point, bg_mask = detect_paintings(query_image)27 bboxes, bbox_mask = detect_bboxes(query_image)28 res_mask = bg_mask.astype(bool) ^ bbox_mask.astype(bool) if loader.detect_bboxes else bg_mask29 if loader.compute_masks:30 if loader.evaluate:31 calc_mask_metrics(masks_metrics, gt_mask / 255, bg_mask)32 if opt.save:33 mask_name = name.split("/")[-1].replace(".jpg", ".png")34 save_mask(os.path.join(opt.output, loader.root.split("/")[-1], mask_name), res_mask * 255)35 # cropped sets, no need to mask image for retrieval36 if gt_mask is None:37 res_mask = None38 if loader.detect_bboxes:39 set_bboxes.append(bboxes)40 # change colorspace before computing feature vector41 query_image = transform_color(query_image, opt.color) if opt.color is not None else query_image42 if multiple_painting and gt_mask is not None:43 im_preds = []44 left_paint = np.zeros_like(res_mask)45 right_paint = np.zeros_like(res_mask)46 left_paint[:, split_point:] = res_mask[:, split_point:]47 right_paint[:, :split_point] = res_mask[:, :split_point]48 res_masks = [left_paint, right_paint]49 for submasks in res_masks:50 query_fv = calc_FV(query_image, opt, submasks).ravel()51 distances = calculate_distances(bbdd_fvs, query_fv, mode=opt.dist)52 im_preds.append((distances.argsort()[:10]).tolist())53 predictions.append(im_preds)54 else:55 query_fv = calc_FV(query_image, opt, res_mask).ravel()56 distances = calculate_distances(bbdd_fvs, query_fv, mode=opt.dist)57 predictions.append((distances.argsort()[:10]).tolist())58 if opt.save:59 save_predictions("{}/{}/result.pkl".format(opt.output, loader.root.split("/")[-1]), predictions)60 save_predictions("{}/{}/text_boxes.pkl".format(opt.output, loader.root.split("/")[-1]), set_bboxes)61 map_k = {i: mapk(gt_correspondences, predictions, k=i) for i in [10, 3, 1]} if loader.evaluate else None62 avg_mask_metrics = averge_masks_metrics(masks_metrics) if loader.evaluate else None63 return map_k, avg_mask_metrics64def calc_mask_metrics(out_dict, gt_mask, pred_mask):65 gt_mask = gt_mask.astype("uint8").ravel()66 pred_mask = pred_mask.astype("uint8").ravel()67 # TODO check what happen with some masks68 if pred_mask.max() != 1:69 pred_mask = (pred_mask / 255).astype("uint8")70 out_dict["recall"].append(recall_score(gt_mask, pred_mask))71 out_dict["precision"].append(precision_score(gt_mask, pred_mask))72 out_dict["f1"].append(f1_score(gt_mask, pred_mask))73if __name__ == '__main__':74 opt = parse_args()75 opt.histogram = "multiresolution"76 opt.dist = "intersection"77 opt.mr_splits = [5, 5]78 opt.color = "RGB"79 opt.bins = 25680 opt.concat = True81 opt.save = True82 os.chdir("..")83 mkdir(opt.output)84 log = os.path.join(opt.output, "log.txt")85 log_file = open(log, "a")86 print(opt, file=log_file)87 train = Dataloader("data/bbdd")88 '''89 test_1_1 = Dataloader("data/qsd1_w1", evaluate=True)90 gt_1_1 = load_pickle("data/qsd1_w1/gt_corresps.pkl")91 mkdir(os.path.join(opt.output, test_1_1.root.split("/")[-1]))92 test_2_1 = Dataloader("data/qsd2_w1", compute_masks=True, evaluate=True)93 gt_2_1 = load_pickle("data/qsd2_w1/gt_corresps.pkl")94 mkdir(os.path.join(opt.output, test_2_1.root.split("/")[-1]))95 test_1_2 = Dataloader("data/qsd1_w2", detect_bboxes=True, evaluate=True)96 gt_1_2 = load_pickle("data/qsd1_w2/gt_corresps.pkl")97 mkdir(os.path.join(opt.output, test_1_2.root.split("/")[-1]))98 99 test_2_2 = Dataloader("data/qsd2_w2", compute_masks=True, detect_bboxes=True, evaluate=True)100 gt_2_2 = load_pickle("data/qsd2_w2/gt_corresps.pkl")101 mkdir(os.path.join(opt.output, test_2_2.root.split("/")[-1]))102 103 testset_1_2 = Dataloader("data/qst1_w2", detect_bboxes=True)104 mkdir(os.path.join(opt.output, testset_1_2.root.split("/")[-1]))105 testset_2_2 = Dataloader("data/qst2_w2", compute_masks=True, detect_bboxes=True)106 mkdir(os.path.join(opt.output, testset_2_2.root.split("/")[-1]))107 '''108 bbdd_matrix = np.array(109 [calc_FV(110 transform_color(image, opt.color) if opt.color is not None else image, opt).ravel()111 for _, image, _ in train112 ])113 print("Train sample loaded", bbdd_matrix.shape)114 '''115 print(test_1_1.root, file=log_file)116 result = eval_set(test_1_1, gt_1_1, bbdd_matrix, opt)117 print(result, file=log_file)118 print(test_2_1.root, file=log_file)119 result = eval_set(test_2_1, gt_2_1, bbdd_matrix, opt)120 print(result, file=log_file)121 print(test_1_2.root, file=log_file)122 print(eval_set(test_1_2, gt_1_2, bbdd_matrix, opt), file=log_file)123 124 print(test_2_2.root, file=log_file)125 result = eval_set(test_2_2, gt_2_2, bbdd_matrix, opt)126 print(result)127 print(result, file=log_file)128 129 print(testset_1_2.root, file=log_file)130 print(eval_set(testset_1_2, None, bbdd_matrix, opt), file=log_file)131 print(testset_2_2.root, file=log_file)132 print(eval_set(testset_2_2, None, bbdd_matrix, opt), file=log_file)133 '''134 '''135 test_2_2 = Dataloader("data/qsd2_w2", compute_masks=True, detect_bboxes=True, evaluate=True)136 gt_2_2 = load_pickle("data/qsd2_w2/gt_corresps.pkl")137 mkdir(os.path.join(opt.output, test_2_2.root.split("/")[-1]))138 print(test_2_2.root, file=log_file)139 result = eval_set(test_2_2, gt_2_2, bbdd_matrix, opt)140 print(result)141 print(result, file=log_file)142 '''143 # test_1_3 = Dataloader("data/qsd1_w3", detect_bboxes=True, evaluate=True)144 # gt_1_3 = load_pickle("data/qsd1_w3/gt_corresps.pkl")145 # mkdir(os.path.join(opt.output, test_1_3.root.split("/")[-1]))146 test_2_3 = Dataloader("data/qsd1_w4", compute_masks=True, detect_bboxes=True, evaluate=True)147 gt_2_3 = load_pickle("data/qsd1_w4/gt_corresps.pkl")148 mkdir(os.path.join(opt.output, test_2_3.root.split("/")[-1]))149 opt.apply_denoise = False150 #for blur_type in ["GaussianBlur", "medianBlur", "bilateralFilter", "blur", "best"]:151 for blur_type in ["best"]:152 opt.blur_type = blur_type153 print(opt, file=log_file)154 # print(test_1_3.root, file=log_file)155 # result = eval_set(test_1_3, gt_1_3, bbdd_matrix, opt)156 # print(result)157 # print(result, file=log_file)158 print(test_2_3.root, file=log_file)159 result = eval_set(test_2_3, gt_2_3, bbdd_matrix, opt)160 print(result)...
test_rename.py
Source:test_rename.py
1#!/usr/bin/env python2# -*- coding: utf8 -*-3import warnings4from agate import Table5from agate.testcase import AgateTestCase6from agate.data_types import *7class TestRename(AgateTestCase):8 def setUp(self):9 self.rows = (10 (1, 4, 'a'),11 (2, 3, 'b'),12 (None, 2, 'c')13 )14 self.number_type = Number()15 self.text_type = Text()16 self.column_names = ['one', 'two', 'three']17 self.column_types = [self.number_type, self.number_type, self.text_type]18 def test_rename_row_names(self):19 table = Table(self.rows, self.column_names, self.column_types)20 table2 = table.rename(row_names=['a', 'b', 'c'])21 self.assertSequenceEqual(table2.row_names, ['a', 'b', 'c'])22 self.assertSequenceEqual(table2.column_names, self.column_names)23 self.assertIs(table.row_names, None)24 self.assertSequenceEqual(table.column_names, self.column_names)25 def test_rename_row_names_dict(self):26 table = Table(self.rows, self.column_names, self.column_types, row_names=['a', 'b', 'c'])27 table2 = table.rename(row_names={'b': 'd'})28 self.assertSequenceEqual(table2.row_names, ['a', 'd', 'c'])29 self.assertSequenceEqual(table2.column_names, self.column_names)30 self.assertSequenceEqual(table.row_names, ['a', 'b', 'c'])31 self.assertSequenceEqual(table.column_names, self.column_names)32 def test_rename_column_names(self):33 table = Table(self.rows, self.column_names, self.column_types)34 table2 = table.rename(column_names=['d', 'e', 'f'])35 self.assertIs(table2.row_names, None)36 self.assertSequenceEqual(table2.column_names, ['d', 'e', 'f'])37 self.assertIs(table.row_names, None)38 self.assertSequenceEqual(table.column_names, self.column_names)39 def test_rename_column_names_dict(self):40 table = Table(self.rows, self.column_names, self.column_types)41 table2 = table.rename(column_names={'two': 'second'})42 self.assertIs(table2.row_names, None)43 self.assertSequenceEqual(table2.column_names, ['one', 'second', 'three'])44 self.assertIs(table.row_names, None)45 self.assertSequenceEqual(table.column_names, self.column_names)46 def test_rename_column_names_renames_row_values(self):47 table = Table(self.rows, self.column_names, self.column_types)48 new_column_names = ['d', 'e', 'f']49 table2 = table.rename(column_names=new_column_names)50 self.assertColumnNames(table2, new_column_names)51 def test_rename_slugify_columns(self):52 strings = ['Test kož', 'test 2', 'test 2']53 table = Table(self.rows, self.column_names, self.column_types)54 table2 = table.rename(strings, slug_columns=True)55 table3 = table.rename(strings, slug_columns=True, separator='.')56 self.assertColumnNames(table, ['one', 'two', 'three'])57 self.assertColumnNames(table2, ['test_koz', 'test_2', 'test_2_2'])58 self.assertColumnNames(table3, ['test.koz', 'test.2', 'test.2.2'])59 def test_rename_slugify_rows(self):60 strings = ['Test kož', 'test 2', 'test 2']61 table = Table(self.rows, self.column_names, self.column_types)62 table2 = table.rename(row_names=strings, slug_rows=True)63 table3 = table.rename(row_names=strings, slug_rows=True, separator='.')64 self.assertIs(table.row_names, None)65 self.assertRowNames(table2, ['test_koz', 'test_2', 'test_2_2'])66 self.assertRowNames(table3, ['test.koz', 'test.2', 'test.2.2'])67 def test_rename_slugify_columns_in_place(self):68 column_names = [u'Test kož', 'test 2', 'test 2']69 warnings.simplefilter('ignore')70 try:71 table = Table(self.rows, column_names, self.column_types)72 finally:73 warnings.resetwarnings()74 table2 = table.rename(slug_columns=True)75 table3 = table.rename(slug_columns=True, separator='.')76 self.assertColumnNames(table, [u'Test kož', 'test 2', 'test 2_2'])77 self.assertColumnNames(table2, ['test_koz', 'test_2', 'test_2_2'])78 self.assertColumnNames(table3, ['test.koz', 'test.2', 'test.2.2'])79 def test_rename_slugify_rows_in_place(self):80 strings = ['Test kož', 'test 2', 'test 2']81 table = Table(self.rows, self.column_names, self.column_types, row_names=strings)82 table2 = table.rename(slug_rows=True)83 table3 = table.rename(slug_rows=True, separator='.')84 self.assertRowNames(table, ['Test kož', 'test 2', 'test 2'])85 self.assertRowNames(table2, ['test_koz', 'test_2', 'test_2_2'])...
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