def visualize_mask_samples(dataset_train): image_ids = [0, 1, 2, 3] for image_id in image_ids: image = dataset_train.load_image(image_id) mask, class_ids = dataset_train.load_mask(image_id) visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)
def visualize_dataset(self, dataset, nbImages): try: image_ids = np.random.choice(dataset.image_ids, nbImages) for image_id in image_ids: image = dataset.load_image(image_id, 0)[:, :, :3] mask, class_ids = dataset.load_mask(image_id) visualize.display_top_masks(image, mask, class_ids, dataset.class_names) except Exception as e: print('Error-Could not visualize dataset: ' + str(e))
def visualize_data(data): image_ids = data.image_ids[:10] for image_id in image_ids: image = data.load_image(image_id) mask, class_ids = data.load_mask(image_id) #print(class_ids) make_save("debug/%s" % image_id) visualize.display_top_masks(image, mask, class_ids, data.class_names, limit=1)
dataset_train.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_train.prepare() # Validation dataset dataset_val = ShapesDataset() dataset_val.load_shapes(50, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_val.prepare() # In[6]: # Load and display random samples image_ids = np.random.choice(dataset_train.image_ids, 4) for image_id in image_ids: image = dataset_train.load_image(image_id) mask, class_ids = dataset_train.load_mask(image_id) visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names) # ## Ceate Model # In[ ]: # Create model in training mode model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR) # In[7]: # Which weights to start with? init_with = "coco" # imagenet, coco, or last if init_with == "imagenet": model.load_weights(model.get_imagenet_weights(), by_name=True)
dataset_train = ShapesDataset() dataset_train.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_train.prepare() # Validation dataset dataset_val = ShapesDataset() dataset_val.load_shapes(50, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1]) dataset_val.prepare() # Load and display random samples image_ids = np.random.choice(dataset_train.image_ids, 4) for image_id in image_ids: image = dataset_train.load_image(image_id) mask, class_ids = dataset_train.load_mask(image_id) visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names, target=VIS_DIR+"rnd_smpl_" + str(image_id) + ".jpg" ) ########################################## ###### CREATE MODEL ###### ########################################## # Create model in training mode model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR) # Which weights to start with? init_with = "coco" # imagenet, coco, or last if init_with == "imagenet":
dataset_train = NucleiDataset() dataset_train.load_nuclei() dataset_train.prepare() dataset_val = NucleiDataset() dataset_val.load_nuclei() dataset_val.prepare() # Load and display a test for i in range(0,10): TRAIN_PATH = '../stage1_train/' image = dataset_train.load_image(i) mask, class_ids = dataset_train.load_mask(i) print(class_ids) visualize.display_top_masks(image, mask, class_ids,['background','nuclei']) # Create model in training mode model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR) # Which weights to start with? init_with = "coco" # imagenet, coco, or last if init_with == "imagenet": model.load_weights(model.get_imagenet_weights(), by_name=True) elif init_with == "coco": # Load weights trained on MS COCO, but skip layers that # are different due to the different number of classes # See README for instructions to download the COCO weights model.load_weights(COCO_MODEL_PATH, by_name=True,
data = MujocoData(None) data.load(name) data.load_mujoco(-1) data.prepare() return data #just makes a basic mujoco dataset if __name__ == '__main__': fn = 'dataset.pickle' if not os.path.exists(fn): data = make_dataset(count=256) data.save(fn) else: data = load_dataset(fn) print('loaded dataset') image_ids = data.image_ids[:10] for image_id in image_ids: image = data.load_image(image_id) mask, class_ids = data.load_mask(image_id) make_save("%s" % image_id) visualize.display_top_masks(image, mask, class_ids, data.class_names, limit=2)
Validate, learning_rate=config.LEARNING_RATE / 10, epochs=100, layers="all") if 0: image_ids = np.random.choice([ tid for tid in Train.image_ids if Train.load_mask(tid)[1][0] == 2 ], 4) for image_id in image_ids: image = Train.load_image(image_id) mask, class_ids = Train.load_mask(image_id) print(mask.shape, class_ids, image.shape) visualize.display_top_masks(image, mask, class_ids, Train.class_names, limit=len(class_ids)) class InferenceConfig(RSNAConfig): GPU_COUNT = 1 IMAGES_PER_GPU = 1 inference_config = InferenceConfig() if 0: # Load trained weights # Recreate the model in inference mode model = modellib.MaskRCNN(mode="inference", config=inference_config, model_dir="./model")