def method_select(name): if name == "color": ret = Color() elif name == "edge": ret = Edge() elif name == "gabor": ret = Gabor() elif name == "daisy": ret = Daisy() elif name == "HOG": ret = HOG() return ret
def _get_feat(self, db, f_class): if f_class == 'color': f_c = Color() elif f_class == 'daisy': f_c = Daisy() elif f_class == 'edge': f_c = Edge() elif f_class == 'gabor': f_c = Gabor() elif f_class == 'hog': f_c = HOG() return f_c.make_samples(db, verbose=False)
# retrieve by daisy method = Daisy() samples = method.make_samples(db) query = samples[query_idx] _, result = infer(query, samples=samples, depth=depth, d_type=d_type) print(result) # retrieve by edge method = Edge() samples = method.make_samples(db) query = samples[query_idx] _, result = infer(query, samples=samples, depth=depth, d_type=d_type) print(result) # retrieve by gabor method = Gabor() samples = method.make_samples(db) query = samples[query_idx] _, result = infer(query, samples=samples, depth=depth, d_type=d_type) print(result) # retrieve by HOG method = HOG() samples = method.make_samples(db) query = samples[query_idx] _, result = infer(query, samples=samples, depth=depth, d_type=d_type) print(result) # retrieve by VGG method = VGGNetFeat() samples = method.make_samples(db)
name, weight = s.split(':') weight = int(weight) if name not in features.keys() or weight < 1: raise Exception return name, weight except: raise argparse.ArgumentTypeError( f"\nFeature must be 'name:weight'\n\tname in {features.keys()}\n\tweight >= 1" ) features = { "color": Color(), "daisy": Daisy(), "edge": Edge(), "gabor": Gabor(), "hog": HOG(), "vgg": VGGNetFeat(), "res": ResNetFeat(), } if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-n", "--neighbor", help="neighbor by class", type=int, default=3) parser.add_argument( "-c", help="Copy images in a result path (src/CBIR/result/retrieval/)",