MaskRCNN
`!pip install keras==2.2.5 !pip install h5py==2.10.0 %tensorflow_version 1.x import os import sys import json import datetime import numpy as np import skimage.draw import cv2 from mrcnn.visualize import display_instances import matplotlib.pyplot as plt
Root directory of the project
ROOT_DIR = os.path.abspath("/content/wastedata-Mask_RCNN-multiple-classes/main/Mask_RCNN/")
Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library from mrcnn.config import Config from mrcnn import model as modellib, utils
Path to trained weights file
COCO_WEIGHTS_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
Directory to save logs and model checkpoints
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs") class CustomConfig(Config): """Configuration for training on the dataset. Derives from the base Config class and overrides some values. """ # Give the configuration a recognizable name NAME = "object"
# We use a GPU with 12GB memory, which can fit two images.
# Adjust down if you use a smaller GPU.
IMAGES_PER_GPU = 1
# Number of classes (including background)
NUM_CLASSES = 1 + 2 # Background + (Horse and Man)
# Number of training steps per epoch
STEPS_PER_EPOCH = 100
# Skip detections with < 90% confidence
DETECTION_MIN_CONFIDENCE = 0.9
class CustomDataset(utils.Dataset): def load_custom(self, dataset_dir, subset): """Load a subset of the Horse-Man dataset. dataset_dir: Root directory of the dataset. subset: Subset to load: train or val """ # Add classes. We have only one class to add. self.add_class("object", 1, "FBH") self.add_class("object", 2, "No disease") # self.add_class("object", 3, "xyz") #likewise
# Train or validation dataset?
assert subset in ["train", "val"]
dataset_dir = os.path.join(dataset_dir, subset)
# Load annotations
# VGG Image Annotator saves each image in the form:
# { 'filename': '28503151_5b5b7ec140_b.jpg',
# 'regions': {
# '0': {
# 'region_attributes': {},
# 'shape_attributes': {
# 'all_points_x': [...],
# 'all_points_y': [...],
# 'name': 'polygon'}},
# ... more regions ...
# },
# 'size': 100202
# }
# We mostly care about the x and y coordinates of each region
annotations1 = json.load(open(os.path.join(dataset_dir, "result.json")))
# print(annotations1)
annotations = list(annotations1.values()) # don't need the dict keys
# The VIA tool saves images in the JSON even if they don't have any
# annotations. Skip unannotated images.
# Add images
for a in annotations:
# print(a)
# Get the x, y coordinaets of points of the polygons that make up
# the outline of each object instance. There are stores in the
# shape_attributes (see json format above)
polygons = [r['shape_attributes'] for r in a['regions']]
objects = [s['region_attributes']['name'] for s in a['regions']]
print("objects:",objects)
name_dict = {"FBH": 1,"No Disease": 2} #,"xyz": 3}
# key = tuple(name_dict)
num_ids = [name_dict[a] for a in objects]
# num_ids = [int(n['Event']) for n in objects]
# load_mask() needs the image size to convert polygons to masks.
# Unfortunately, VIA doesn't include it in JSON, so we must read
# the image. This is only managable since the dataset is tiny.
print("numids",num_ids)
image_path = os.path.join(dataset_dir, a['filename'])
image = skimage.io.imread(image_path)
height, width = image.shape[:2]
self.add_image(
"object", ## for a single class just add the name here
image_id=a['filename'], # use file name as a unique image id
path=image_path,
width=width, height=height,
polygons=polygons,
num_ids=num_ids
)
def load_mask(self, image_id):
"""Generate instance masks for an image.
Returns:
masks: A bool array of shape [height, width, instance count] with
one mask per instance.
class_ids: a 1D array of class IDs of the instance masks.
"""
# If not a Horse/Man dataset image, delegate to parent class.
image_info = self.image_info[image_id]
if image_info["source"] != "object":
return super(self.__class__, self).load_mask(image_id)
# Convert polygons to a bitmap mask of shape
# [height, width, instance_count]
info = self.image_info[image_id]
if info["source"] != "object":
return super(self.__class__, self).load_mask(image_id)
num_ids = info['num_ids']
mask = np.zeros([info["height"], info["width"], len(info["polygons"])],
dtype=np.uint8)
for i, p in enumerate(info["polygons"]):
# Get indexes of pixels inside the polygon and set them to 1
rr, cc = skimage.draw.polygon(p['all_points_y'], p['all_points_x'])
mask[rr, cc, i] = 1
# Return mask, and array of class IDs of each instance. Since we have
# one class ID only, we return an array of 1s
# Map class names to class IDs.
num_ids = np.array(num_ids, dtype=np.int32)
return mask, num_ids #np.ones([mask.shape[-1]], dtype=np.int32)
def image_reference(self, image_id):
"""Return the path of the image."""
info = self.image_info[image_id]
if info["source"] == "object":
return info["path"]
else:
super(self.__class__, self).image_reference(image_id)
def train(model): """Train the model.""" # Training dataset. dataset_train = CustomDataset() dataset_train.load_custom("/content/dataset", "train") dataset_train.prepare()
# Validation dataset
dataset_val = CustomDataset()
dataset_val.load_custom("/content/dataset", "val")
dataset_val.prepare()
# *** This training schedule is an example. Update to your needs ***
# Since we're using a very small dataset, and starting from
# COCO trained weights, we don't need to train too long. Also,
# no need to train all layers, just the heads should do it.
print("Training network heads")
model.train(dataset_train, dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=10,
layers='heads')
config = CustomConfig() model = modellib.MaskRCNN(mode="training", config=config, model_dir=DEFAULT_LOGS_DIR)
weights_path = COCO_WEIGHTS_PATH # Download weights file if not os.path.exists(weights_path): utils.download_trained_weights(weights_path)
model.load_weights(weights_path, by_name=True, exclude=[ "mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox", "mrcnn_mask"]) train(model)`