DEF_TEST(PDFPrimitives, reporter) {
SkAutoTUnref<SkPDFInt> int42(new SkPDFInt(42));
SimpleCheckObjectOutput(reporter, int42.get(), "42");
SkAutoTUnref<SkPDFScalar> realHalf(new SkPDFScalar(SK_ScalarHalf));
SimpleCheckObjectOutput(reporter, realHalf.get(), "0.5");
SkAutoTUnref<SkPDFScalar> bigScalar(new SkPDFScalar(110999.75f));
#if !defined(SK_ALLOW_LARGE_PDF_SCALARS)
SimpleCheckObjectOutput(reporter, bigScalar.get(), "111000");
#else
SimpleCheckObjectOutput(reporter, bigScalar.get(), "110999.75");
SkAutoTUnref<SkPDFScalar> biggerScalar(new SkPDFScalar(50000000.1));
SimpleCheckObjectOutput(reporter, biggerScalar.get(), "50000000");
SkAutoTUnref<SkPDFScalar> smallestScalar(new SkPDFScalar(1.0/65536));
SimpleCheckObjectOutput(reporter, smallestScalar.get(), "0.00001526");
#endif
SkAutoTUnref<SkPDFString> stringSimple(
new SkPDFString("test ) string ( foo"));
SimpleCheckObjectOutput(reporter, stringSimple.get(),
"(test \\) string \\( foo)");
SkAutoTUnref<SkPDFString> stringComplex(
new SkPDFString("\ttest ) string ( foo"));
SimpleCheckObjectOutput(reporter, stringComplex.get(),
"<0974657374202920737472696E67202820666F6F>");
SkAutoTUnref<SkPDFName> name(new SkPDFName("Test name\twith#tab"));
const char expectedResult[] = "/Test#20name#09with#23tab";
CheckObjectOutput(reporter, name.get(), expectedResult,
strlen(expectedResult), false, false);
SkAutoTUnref<SkPDFName> escapedName(new SkPDFName("A#/%()<>[]{}B"));
const char escapedNameExpected[] = "/A#23#2F#25#28#29#3C#3E#5B#5D#7B#7DB";
CheckObjectOutput(reporter, escapedName.get(), escapedNameExpected,
strlen(escapedNameExpected), false, false);
// Test that we correctly handle characters with the high-bit set.
const unsigned char highBitCString[] = {0xDE, 0xAD, 'b', 'e', 0xEF, 0};
SkAutoTUnref<SkPDFName> highBitName(
new SkPDFName((const char*)highBitCString));
const char highBitExpectedResult[] = "/#DE#ADbe#EF";
CheckObjectOutput(reporter, highBitName.get(), highBitExpectedResult,
strlen(highBitExpectedResult), false, false);
SkAutoTUnref<SkPDFArray> array(new SkPDFArray);
SimpleCheckObjectOutput(reporter, array.get(), "[]");
array->append(int42.get());
SimpleCheckObjectOutput(reporter, array.get(), "[42]");
array->append(realHalf.get());
SimpleCheckObjectOutput(reporter, array.get(), "[42 0.5]");
SkAutoTUnref<SkPDFInt> int0(new SkPDFInt(0));
array->append(int0.get());
SimpleCheckObjectOutput(reporter, array.get(), "[42 0.5 0]");
SkAutoTUnref<SkPDFInt> int1(new SkPDFInt(1));
array->setAt(0, int1.get());
SimpleCheckObjectOutput(reporter, array.get(), "[1 0.5 0]");
SkAutoTUnref<SkPDFDict> dict(new SkPDFDict);
SimpleCheckObjectOutput(reporter, dict.get(), "<<>>");
SkAutoTUnref<SkPDFName> n1(new SkPDFName("n1"));
dict->insert(n1.get(), int42.get());
SimpleCheckObjectOutput(reporter, dict.get(), "<</n1 42\n>>");
SkAutoTUnref<SkPDFName> n2(new SkPDFName("n2"));
SkAutoTUnref<SkPDFName> n3(new SkPDFName("n3"));
dict->insert(n2.get(), realHalf.get());
dict->insert(n3.get(), array.get());
SimpleCheckObjectOutput(reporter, dict.get(),
"<</n1 42\n/n2 0.5\n/n3 [1 0.5 0]\n>>");
TestPDFStream(reporter);
TestCatalog(reporter);
TestObjectRef(reporter);
TestSubstitute(reporter);
test_issue1083();
TestImages(reporter);
}
int main(int argc, char **argv[])
{
string name;
vector<Mat>Images(100), TestImages(50);
vector<Mat> Descriptor(100), TestDescriptor(50), TestPcafeature(50);
vector<vector<KeyPoint>>Keypoints(100), TestKeypoint(50);
Mat histogram = Mat::zeros(100, Cluster, CV_32F);
Mat Testhistogram = Mat::zeros(50, Cluster, CV_32F);
Mat Keyword = Mat::zeros(Cluster, 20, CV_32F);
Mat full_Descriptor, Pcafeature, Pcaduplicate, clusteridx, trainlabels(100, 1, CV_32F);
vector<vector<DMatch>> matches(50);
Mat predicted(Testhistogram.rows, 1, CV_32F);
// Read Training Images.
read_train(Images, name);
//Calculate SIFT features for the Training Images.
calculate_SIFT(Images,Keypoints,Descriptor);
merge_descriptor(full_Descriptor,Descriptor);
//Compute PCA for all the features across all Images.
PCA pca;
perform_PCA(full_Descriptor, Pcafeature, pca);
//Perform K-Means on all the PCA reduced features.
Pcafeature.convertTo(Pcaduplicate, CV_32F);
calculate_Kmeans(Pcaduplicate, clusteridx);
//Calculate the Keywords in the Feature Space.
make_dictionary(clusteridx, Pcaduplicate, Keyword);
//Get the Histogram for each Training Image.
hist(Descriptor, clusteridx, histogram);
//Read Test Image
read_test(TestImages, name);
//Calculate the SIFT feature for all the test Images.
calculate_SIFT(TestImages, TestKeypoint, TestDescriptor);
//Project the SIFT feature of each feature on the lower dimensional PCA plane calculated above.
pca_testProject(TestDescriptor, TestPcafeature, pca);
//Find the Label by searching for keywords closest to current feature.
get_matches(TestPcafeature,Keyword,matches);
//Calculate Histogram for each test Image.
hist_test(TestDescriptor, matches, Testhistogram);
//Perform classification through Knn Classifier.
train_labels(trainlabels);
KNearest knn;
train_classifier(histogram, trainlabels, knn);
test_classify(Testhistogram,predicted,knn);
//Calculate Accuracy for each class.
calculate_accuracy(predicted);
getchar();
return 0;
}
