def __init__(self) -> None:
# Root directory of the project
root_dir = os.path.abspath("./Mask_RCNN")
# Import Mask RCNN
sys.path.append(root_dir) # To find local version of the library
# Import COCO config
sys.path.append(os.path.join(root_dir,
"samples/coco/")) # To find local version
# Directory to save logs and trained model
model_dir = os.path.join(root_dir, "logs")
# Local path to trained weights file
coco_model_path = os.path.join(root_dir, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(coco_model_path):
utils.download_trained_weights(coco_model_path)
# Create model object in inference mode.
self.model = modellib.MaskRCNN(mode="inference",
model_dir=model_dir,
config=config)
# Load weights trained on MS-COCO
self.model.load_weights(coco_model_path, by_name=True)
self.frames = []
config = MalariaConfig(
) if args.command == "train" else MalariaInferenceConfig()
config.display()
# Create model
model = modellib.MaskRCNN(
mode="training" if args.command == "train" else "inference",
config=config,
model_dir=args.logs)
# Select weights file to load
if args.weights.lower() == "coco":
weights_path = COCO_WEIGHTS_PATH
# Download weights file
if not os.path.exists(weights_path):
utils.download_trained_weights(weights_path)
elif args.weights.lower() == "imagenet":
# Start from ImageNet trained weights
weights_path = model.get_imagenet_weights()
elif args.weights.lower() == "last":
# Find last trained weights
weights_path = model.find_last()
else:
weights_path = args.weights
# Load weights
print("Loading weights ", weights_path)
if args.weights.lower() == "coco":
# Exclude the last layers because they require a matching
# number of classes
model.load_weights(weights_path,
def func(filepath,filename):
# Root directory of the project
ROOT_DIR = os.path.abspath("../")
# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library
from Mask_RCNN.mrcnn import utils
import Mask_RCNN.mrcnn.model as modellib
from Mask_RCNN.mrcnn import visualize
# Import COCO config
sys.path.append(os.path.join(ROOT_DIR, "samples/coco/")) # To find local version
from Mask_RCNN.samples.coco.coco import CocoConfig
# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
utils.download_trained_weights(COCO_MODEL_PATH)
# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")
class InferenceConfig(CocoConfig):
# Set batch size to 1 since we'll be running inference on
# one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
GPU_COUNT = 1
IMAGES_PER_GPU = 1
config = InferenceConfig()
config.display()
# Create model object in inference mode.
model = modellib.Mask_RCNN(mode="inference", model_dir=MODEL_DIR, config=config)
# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)
# COCO Class names
# Index of the class in the list is its ID. For example, to get ID of
# the teddy bear class, use: class_names.index('teddy bear')
class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
'kite', 'baseball bat', 'baseball glove', 'skateboard',
'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
'teddy bear', 'hair drier', 'toothbrush']
# Load a random image from the images folder
# file_names = next(os.walk(IMAGE_DIR))[2]
image = skimage.io.imread(filepath)#os.path.join(IMAGE_DIR, random.choice(file_names)))
# Run detection
results = model.detect([image], verbose=1)
# Visualize results
r = results[0]
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'],filename=filename)
# func()
import os
import Mask_RCNN.mrcnn.model as modellib
from Mask_RCNN.mrcnn import utils
# Download COCO trained weights from Releases if needed
COCO_MODEL_PATH = os.path.join('..', '..', 'Mask_RCNN', "mask_rcnn_coco.h5")
if not os.path.exists(COCO_MODEL_PATH):
utils.download_trained_weights(COCO_MODEL_PATH)
import algorithmic_suggestions.configs_for_AlgorithmicSuggestions_MaskRCNN as mrcnn_configs
# =================================================================
# Params
# Directory to save logs and trained model
MODEL_DIR = "/home/mohamedt/Desktop/WSI_Segmentation/Models/TCGA_maskrcnn/19July2018/"
# Which weights to start with?
init_with = "weight_file" # imagenet, coco, last, or weight_file
if init_with == "weight_file":
model_to_use = "nucleus20180720T1413"
model_epoch = "mask_rcnn_nucleus_0022.h5"
model_weights_path = "/home/mohamedt/Desktop/WSI_Segmentation/Models/TCGA_maskrcnn/19July2018/%s/%s" % (
model_to_use, model_epoch)
augmentation = None
n_epochs = 50
# Configurations
config_train = mrcnn_configs.NucleusConfig(is_training=True)
