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Teste 100 Bilder mit direkter Klassifizierung

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v-h 2023-01-17 08:28:58 +01:00
parent bc52bd699b
commit b6d4d5edb1
1 changed files with 6 additions and 5 deletions
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11
test_stepthree_for.m
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@ -1,5 +1,7 @@
% Testscript um ein Bild aus den Daten durch das RCCN_NET mit direkter % Testscript um 100 Bilder durch das RCCN_NET mit direkter
% klassifizierung laufen zu lassen % klassifizierung laufen zu lassen
% 2022-01-17
% leider erkennt das Netz nichts. :(
close all; close all;
clear; clear;
@ -19,13 +21,13 @@ grdata = load(grDataFile);
traficSignDataset = grdata.DataSet; traficSignDataset = grdata.DataSet;
%Random Index %Random Index
%shuffledIndices = randperm(height(traficSignDataset)); shuffledIndices = randperm(height(traficSignDataset));
%testindx = shuffledIndices(1) %testindx = shuffledIndices(1)
for testindx = 126:200 for testindx = 1:100
%testindx = 125; %testindx = 125;
% Bild einlesen % Bild einlesen
imgname = traficSignDataset.imageFilename{testindx} imgname = traficSignDataset.imageFilename{shuffledIndices(testindx)}
I = imresize(imread(imgname),inputSize(1:2)); I = imresize(imread(imgname),inputSize(1:2));
%RCCN-Detector laden %RCCN-Detector laden
@ -34,7 +36,6 @@ detector = pretrained.detector;
[bbox, score, label] = detect(detector, I); [bbox, score, label] = detect(detector, I);
sfigTitle = ""; sfigTitle = "";
bdetected = height(bbox) > 0; bdetected = height(bbox) > 0;
if bdetected if bdetected