class EvalPuppet(Callback):
def __init__(self, generator, height=48, width=48, batch_size=1):
super(EvalPuppet, self).__init__()
self.batch_size = batch_size
self.generator = generator
self.postprocess = PostProcess(height, width)
self.evaluate = Eval()
def on_epoch_end(self, epoch, logs=None):
# generate data
image_group, guide_mask_group, annkp_group = self.generator.next()
predict_mask_group = self.model.predict_on_batch(image_group)[-1]
outobjects_group = []
for x in range(self.batch_size):
# select last level see, and unuse other level
mask = predict_mask_group[x, :, :, :]
outobjects_group.append(self.postprocess.process(mask))
self.evaluate.evaluate(annkp_group, outobjects_group)
def main():
# build dataset
batch_size = 1
height = 48
width = 48
dataset = TestDataGenerator(PuppetDataset, 4, batch_size, height=height, width=width)
evaluate = Eval()
postprocess = PostProcess(48, 48)
# generate and display
image_group, guide_mask_group, annkp_group = dataset.next()
outobjects_group = []
for x in range(batch_size):
image = image_group[x]
# select last level see, and unuse other level
mask = guide_mask_group[x][-1]
display_my_masks(image, mask)
# use groudtruth mask directly as predict mask to process
outobjects_group.append(postprocess.process(mask))
evaluate.evaluate(annkp_group, outobjects_group)
