ignores = ignore_categories(train_data)
ds = COCODoomDataset(JSON, ROOT, batch_size=1, ignored_class_ids=ignores)
net = fcnn.build(ds.num_classes)
net.load_weights("checkpoint_best.h5")
vis = Visualizer(ds.num_classes)
writer = imageio.get_writer("COCODoomOutput_{}FPS_LVL{}.mp4".format(FPS, LVL), fps=FPS)
for i, (X, [y]) in enumerate(ds.stream(shuffle=False, level=LVL, sparse_y=False)):
image_restored = np.clip(X[0] * 255 + 50, 0, 255).astype("uint8")
pred = net.predict(X)[0]
delta = sparse_categorical_crossentropy(pred, y, ds.num_classes)
gt = vis.colorify_mask(y)
dt = vis.colorify_mask(pred)
gt = set_title(gt, "Label")
overlayed = overlay(image_restored.copy(), dt)
overlayed = set_title(overlayed, "Detection overlayed")
delta = set_title((delta*255).astype("uint8"), "Detection error")
image_restored = set_title(image_restored, "Image")
top = np.concatenate([image_restored, gt], axis=1)
bot = np.concatenate([overlayed, delta], axis=1)
display = np.concatenate([top, bot], axis=0)
display = cv2.resize(display, (0, 0), fx=1.5, fy=1.5, interpolation=cv2.INTER_CUBIC)
writer.append_data(cv2.cvtColor(display, cv2.COLOR_BGR2RGB))
