Surgery-Robot-Detection-Segmentation
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SUYEgit
it seems that it can only segment one class, and no label plot, please help!
""" Mask R-CNN Train on the surgery robot dataset.
Copyright (c) 2018 Matterport, Inc. Licensed under the MIT License (see LICENSE for details) Written by Waleed Abdulla
Usage: import the module (see Jupyter notebooks for examples), or run from the command line as such:
#Train a new model starting from pre-trained COCO weights
python surgery.py train --dataset=/home/.../mask_rcnn/data/surgery/ --weights=coco
#Train a new model starting from pre-trained ImageNet weights
python surgery.py train --dataset=/home/.../mask_rcnn/data/surgery/ --weights=imagenet
# Continue training the last model you trained. This will find
# the last trained weights in the model directory.
python surgery.py train --dataset=/home/.../mask_rcnn/data/surgery/ --weights=last
#Detect and color splash on a image with the last model you trained.
#This will find the last trained weights in the model directory.
python surgery.py splash --weights=last --image=/home/...../*.jpg
#Detect and color splash on a video with a specific pre-trained weights of yours.
python sugery.py splash --weights=/home/.../logs/mask_rcnn_surgery_0030.h5 --video=/home/simon/Videos/Center.wmv
"""
import os import sys import json import datetime import numpy as np import skimage.draw from matplotlib import pyplot as plt
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 mrcnn.config import Config from mrcnn import model as modellib, utils from mrcnn import visualize
Path to trained weights file
COCO_WEIGHTS_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
Directory to save logs and model checkpoints, if not provided
through the command line argument --logs
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
############################################################
Configurations
############################################################
class SurgeryConfig(Config): """Configuration for training on the toy dataset. Derives from the base Config class and overrides some values. """ # Give the configuration a recognizable name NAME = "surgery"
# We use a GPU with 12GB memory, which can fit two images.
# Adjust down if you use a smaller GPU.
IMAGES_PER_GPU = 2
# Number of classes (including background)
NUM_CLASSES = 1 + 2 # Background + objects
# Number of training steps per epoch
STEPS_PER_EPOCH = 100
# Skip detections with < 90% confidence
DETECTION_MIN_CONFIDENCE = 0.9
############################################################
Dataset
############################################################
class SurgeryDataset(utils.Dataset): def load_VIA(self, dataset_dir, subset, hc=False): """Load the surgery dataset from VIA. dataset_dir: Root directory of the dataset. subset: Subset to load: train or val or predict """ # Add classes. We have only one class to add. self.add_class("surgery", 1, "cat") self.add_class("surgery", 2, "dog") if hc is True: for i in range(1,14): self.add_class("surgery", i, "{}".format(i)) self.add_class("surgery", 14, "dog")
# Train or validation dataset?
assert subset in ["train", "val", "predict"]
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': {name:'a'},
# '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
annotations = json.load(open(os.path.join(dataset_dir, "via_region_data.json")))
annotations = list(annotations.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.
annotations = [a for a in annotations if a['regions']]
# Add images
for a in annotations:
# 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']]
names = [r['region_attributes'] for r in a['regions']]
# 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.
image_path = os.path.join(dataset_dir, a['filename'])
image = skimage.io.imread(image_path)
height, width = image.shape[:2]
self.add_image(
"surgery",
image_id=a['filename'], # use file name as a unique image id
path=image_path,
width=width, height=height,
polygons=polygons,
names=names)
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 surgery dataset image, delegate to parent class.
image_info = self.image_info[image_id]
if image_info["source"] != "surgery":
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]
class_names = info["names"]
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
# Assign class_ids by reading class_names
class_ids = np.zeros([len(info["polygons"])])
# In the surgery dataset, pictures are labeled with name 'a' and 'r' representing arm and ring.
for i, p in enumerate(class_names):
#"name" is the attributes name decided when labeling, etc. 'region_attributes': {name:'a'}
if p['animal'] == 'cat':
class_ids[i] = 1
elif p['animal'] == 'dog':
class_ids[i] = 2
#assert code here to extend to other labels
class_ids = class_ids.astype(int)
# Return mask, and array of class IDs of each instance. Since we have
# one class ID only, we return an array of 1s
return mask.astype(np.bool), class_ids
def image_reference(self, image_id):
"""Return the path of the image."""
info = self.image_info[image_id]
if info["source"] == "surgery":
return info["path"]
else:
super(self.__class__, self).image_reference(image_id)
def load_mask_hc(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 surgery dataset image, delegate to parent class.
image_info = self.image_info[image_id]
if image_info["source"] != "surgery":
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]
#"name" is the attributes name decided when labeling, etc. 'region_attributes': {name:'a'}
class_names = info["names"]
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
# Assign class_ids by reading class_names
class_ids = np.zeros([len(info["polygons"])])
# In the surgery dataset, pictures are labeled with name 'a' and 'r' representing arm and ring.
for i, p in enumerate(class_names):
if p['animal'] == 'cat':
class_ids[i] = 1
elif p['animal']=='dog':
class_ids[i]==2
elif p['animal'] == 'error':
pass
else:
class_ids[i] = int(p['animal'])
#assert code here to extend to other labels
class_ids = class_ids.astype(int)
# Return mask, and array of class IDs of each instance. Since we have
# one class ID only, we return an array of 1s
return mask.astype(np.bool), class_ids
def train(model, *dic): """Train the model.""" # Training dataset. dataset_train = SurgeryDataset() dataset_train.load_VIA(args.dataset, "train") dataset_train.prepare()
# Validation dataset
dataset_val = SurgeryDataset()
dataset_val.load_VIA(args.dataset, "val")
dataset_val.prepare()
# *** This training schedu le 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')
def color_splash(image, mask): """Apply color splash effect. image: RGB image [height, width, 3] mask: instance segmentation mask [height, width, instance count]
Returns result image.
"""
# Make a grayscale copy of the image. The grayscale copy still
# has 3 RGB channels, though.
gray = skimage.color.gray2rgb(skimage.color.rgb2gray(image)) * 255
# We're treating all instances as one, so collapse the mask into one layer
mask = (np.sum(mask, -1, keepdims=True) >= 1)
# Copy color pixels from the original color image where mask is set
if mask.shape[0] > 0:
splash = np.where(mask, image, gray).astype(np.uint8)
else:
splash = gray
return splash
def detect_and_color_splash(model, image_path=None, video_path=None, out_dir=''): assert image_path or video_path
class_names = ['BG', 'cat', 'dog']
# Image or video?
if image_path:
# Run model detection and generate the color splash effect
print("Running on {}".format(args.image))
# Read image
image = skimage.io.imread(args.image)
# Detect objects
r = model.detect([image], verbose=1)[0]
# Color splash
# splash = color_splash(image, r['masks'])
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'], making_image=True)
file_name = 'splash.png'
# Save output
# file_name = "splash_{:%Y%m%dT%H%M%S}.png".format(datetime.datetime.now())
# save_file_name = os.path.join(out_dir, file_name)
# skimage.io.imsave(save_file_name, splash)
elif video_path:
import cv2
# Video capture
vcapture = cv2.VideoCapture(video_path)
# width = int(vcapture.get(cv2.CAP_PROP_FRAME_WIDTH))
# height = int(vcapture.get(cv2.CAP_PROP_FRAME_HEIGHT))
width = 1600
height = 1600
fps = vcapture.get(cv2.CAP_PROP_FPS)
# Define codec and create video writer
file_name = "splash_{:%Y%m%dT%H%M%S}.wmv".format(datetime.datetime.now())
vwriter = cv2.VideoWriter(file_name,
cv2.VideoWriter_fourcc(*'MJPG'),
fps, (width, height))
count = 0
success = True
#For video, we wish classes keep the same mask in frames, generate colors for masks
colors = visualize.random_colors(len(class_names))
while success:
print("frame: ", count)
# Read next image
plt.clf()
plt.close()
success, image = vcapture.read()
if success:
# OpenCV returns images as BGR, convert to RGB
image = image[..., ::-1]
# Detect objects
r = model.detect([image], verbose=0)[0]
# Color splash
# splash = color_splash(image, r['masks'])
splash = visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'], colors=colors, making_video=True)
# Add image to video writer
vwriter.write(splash)
count += 1
vwriter.release()
print("Saved to ", file_name)
############################################################
RLE Encoding
############################################################
def rle_encode(mask): """Encodes a mask in Run Length Encoding (RLE). Returns a string of space-separated values. """ assert mask.ndim == 2, "Mask must be of shape [Height, Width]" # Flatten it column wise m = mask.T.flatten() # Compute gradient. Equals 1 or -1 at transition points g = np.diff(np.concatenate([[0], m, [0]]), n=1) # 1-based indicies of transition points (where gradient != 0) rle = np.where(g != 0)[0].reshape([-1, 2]) + 1 # Convert second index in each pair to lenth rle[:, 1] = rle[:, 1] - rle[:, 0] return " ".join(map(str, rle.flatten()))
def rle_decode(rle, shape): """Decodes an RLE encoded list of space separated numbers and returns a binary mask.""" rle = list(map(int, rle.split())) rle = np.array(rle, dtype=np.int32).reshape([-1, 2]) rle[:, 1] += rle[:, 0] rle -= 1 mask = np.zeros([shape[0] * shape[1]], np.bool) for s, e in rle: assert 0 <= s < mask.shape[0] assert 1 <= e <= mask.shape[0], "shape: {} s {} e {}".format(shape, s, e) mask[s:e] = 1 # Reshape and transpose mask = mask.reshape([shape[1], shape[0]]).T return mask
def mask_to_rle(image_id, mask, scores): "Encodes instance masks to submission format." assert mask.ndim == 3, "Mask must be [H, W, count]" # If mask is empty, return line with image ID only if mask.shape[-1] == 0: return "{},".format(image_id) # Remove mask overlaps # Multiply each instance mask by its score order # then take the maximum across the last dimension order = np.argsort(scores)[::-1] + 1 # 1-based descending mask = np.max(mask * np.reshape(order, [1, 1, -1]), -1) # Loop over instance masks lines = [] for o in order: m = np.where(mask == o, 1, 0) # Skip if empty if m.sum() == 0.0: continue rle = rle_encode(m) lines.append("{}, {}".format(image_id, rle)) return "\n".join(lines)
def detect(model, dataset_dir, subset): """Run detection on images in the given directory.""" print("Running on {}".format(dataset_dir))
os.makedirs('RESULTS')
submit_dir = os.path.join(os.getcwd(), "RESULTS/")
# Read dataset
dataset = SurgeryDataset()
dataset.load_VIA(dataset_dir, subset)
dataset.prepare()
# Load over images
submission = []
for image_id in dataset.image_ids:
# Load image and run detection
image = dataset.load_image(image_id)
# Detect objects
r = model.detect([image], verbose=0)[0]
# Encode image to RLE. Returns a string of multiple lines
source_id = dataset.image_info[image_id]["id"]
rle = mask_to_rle(source_id, r["masks"], r["scores"])
submission.append(rle)
# Save image with masks
canvas = visualize.display_instances(
image, r['rois'], r['masks'], r['class_ids'],
dataset.class_names, r['scores'], detect=True)
# show_bbox=False, show_mask=False,
# title="Predictions",
# detect=True)
canvas.print_figure("{}/{}.png".format(submit_dir, dataset.image_info[image_id]["id"][:-4]))
# Save to csv file
submission = "ImageId,EncodedPixels\n" + "\n".join(submission)
file_path = os.path.join(submit_dir, "submit.csv")
with open(file_path, "w") as f:
f.write(submission)
print("Saved to ", submit_dir)
############################################################
Training
############################################################
if name == 'main': import argparse
# Parse command line arguments
parser = argparse.ArgumentParser(
description='Train Mask R-CNN to detect rings and robot arms.')
parser.add_argument("command",
metavar="<command>",
help="'train' or 'splash'")
parser.add_argument('--dataset', required=False,
metavar="/home/simon/mask_rcnn/data/surgery",
help='Directory of the surgery dataset')
parser.add_argument('--weights', required=True,
metavar="/home/simon/logs/weights.h5",
help="Path to weights .h5 file or 'coco'")
parser.add_argument('--logs', required=False,
default=DEFAULT_LOGS_DIR,
metavar="/path/to/logs/",
help='Logs and checkpoints directory (default=logs/)')
parser.add_argument('--image', required=False,
metavar="path or URL to image",
help='Image to apply the color splash effect on')
parser.add_argument('--video', required=False,
metavar="path or URL to video",
help='Video to apply the color splash effect on')
parser.add_argument('--subset', required=False,
metavar="Dataset sub-directory",
help="Subset of dataset to run prediction on")
args = parser.parse_args()
# Validate arguments
if args.command == "train":
assert args.dataset, "Argument --dataset is required for training"
elif args.command == "splash":
assert args.image or args.video,\
"Provide --image or --video to apply color splash"
print("Weights: ", args.weights)
print("Dataset: ", args.dataset)
print("Logs: ", args.logs)
# Configurations
if args.command == "train":
config = SurgeryConfig()
else:
class InferenceConfig(SurgeryConfig):
# 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
if args.command == "train":
model = modellib.MaskRCNN(mode="training", config=config,
model_dir=args.logs)
else:
model = modellib.MaskRCNN(mode="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() == "last":
# Find last trained weights
weights_path = model.find_last()[1]
elif args.weights.lower() == "imagenet":
# Start from ImageNet trained weights
weights_path = model.get_imagenet_weights()
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, by_name=True, exclude=[
"mrcnn_class_logits", "mrcnn_bbox_fc",
"mrcnn_bbox", "mrcnn_mask"])
else:
model.load_weights(weights_path, by_name=True)
# Train or evaluate
if args.command == "train":
train(model)
elif args.command == "detect":
detect(model, args.dataset, args.subset)
elif args.command == "splash":
detect_and_color_splash(model, image_path=args.image,
video_path=args.video)
else:
print("'{}' is not recognized. "
"Use 'train' or 'splash'".format(args.command))
