model = get_model_instance_segmentation(chanel_data.number_of_classes())
print("loading model", cnf.model_name )
model.load_state_dict(torch.load(cnf.path2model+cnf.model_name, map_location=device ))
model.to(device)
model.eval()
cnf.num_tsts= 1000 # inf
for i, item in enumerate(chanel_data):
if i >= cnf.num_tsts: break
image = item[0]
masks, labels = instance_segmentation_api(model, image, device, chanel_data.class_names,
threshold=0.7, rect_th=1, text_size=1, text_th=3)
masked_img = []
for jj in range(len(labels)):
masks[jj] = masks[jj].astype(dtype='uint')
# zz[masks[jj]] = jj+1; # Image.fromarray(zz*255/(jj+1)).show()
img = ImageChops.multiply(image, Image.fromarray(255*masks[jj]).convert('RGB') ); # img.show()
masked_img.append(np.array(img, dtype='uint8'))
one_person_clothing_colors = ColorExtractor(masks, labels, masked_img, cnf,
image_name=item[1][-10:-1])
one_person_clothing_colors.pie_chart(image, fname=item[1][-10:-1], figure_size=(4, 4))
# color_table_obj = ColorTable(dataset.class_names, cnf)
