介绍
- 没有过多的解释, 需要一定程序基础和动手能力
- 没有环境配置
- 第一次写东西, 有问题请见谅
克隆以及观察代码
- clone https://github.com/matterport/Mask_RCNN.git
-
找到文件 balloon image.png
- 查看注释
- 看 BalloonDataset 中的 load_balloon 方法可知使用的标记软件为 VGG Image Annotator,
def load_balloon(self, dataset_dir, subset):
"""Load a subset of the Balloon 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("balloon", 1, "balloon")
# Train or validation dataset?
assert subset in ["train", "val"]
dataset_dir = os.path.join(dataset_dir, subset)
# Load annotations
# VGG Image Annotator (up to version 1.6) 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
# Note: In VIA 2.0, regions was changed from a dict to a list.
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. These are stores in the
# shape_attributes (see json format above)
# The if condition is needed to support VIA versions 1.x and 2.x.
if type(a['regions']) is dict:
polygons = [r['shape_attributes'] for r in a['regions'].values()]
else:
polygons = [r['shape_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(
"balloon",
image_id=a['filename'], # use file name as a unique image id
path=image_path,
width=width, height=height,
polygons=polygons)
-
在这里我选用这个版本image.png
准备供训练的图集
网上随便找到一些埼玉老师的图片
-
分别放到训练目录train和测试目录val
image.png 使用 VGG Image Annotator 中的 Polygon region shape 框选需要检测的区域
点击Region Attributes 添加source(balloon)和class_name(埼玉)
-
其他图片也是一样的处理, 具体不多说了.
image.png -
导出json结构, 放到图片目录, 如下
image.png
接下来是修改代
- 在这里我们只修改 load_balloon 方法中的 self.add_class("balloon", 1, "balloon), 修改后如下
def load_balloon(self, dataset_dir, subset):
"""Load a subset of the Balloon 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("balloon", 1, "埼玉") #old: self.add_class("balloon", 1, "balloon)
# Train or validation dataset?
assert subset in ["train", "val"]
dataset_dir = os.path.join(dataset_dir, subset)
- 修改train方法
- 添加custom_callback, 用来保存训练好的权重. 每个epoch完成会自动保存权重. 随时可以强制停止, 然后测试训练效果.
def train(model):
"""Train the model."""
# Training dataset.
dataset_train = BalloonDataset()
dataset_train.load_balloon(args.dataset, "train")
dataset_train.prepare()
# Validation dataset
dataset_val = BalloonDataset()
dataset_val.load_balloon(args.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=100,
layers='heads', custom_callbacks=[keras.callbacks.ModelCheckpoint(weights_path,
verbose=0, save_weights_only=True)])
- 手动指定权重文件路径
- 添加代码, 当文件不存在的时候, 创建权重文件, 如下
# 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()
elif args.weights.lower() == "imagenet":
# Start from ImageNet trained weights
weights_path = model.get_imagenet_weights()
else:
# 添加代码, 当文件不存在的时候, 创建权重文件
if os.path.isfile(args.weights) == False:
model.keras_model.save_weights(args.weights)
weights_path = args.weights
- 修改detect_and_color_splash方法
- 添加方法box_splash, 用来画框
- 在下方的detect_and_color_splash方法中注释掉splash = color_splash(image, r['masks'])使用splash = box_splash(image, r)
def box_splash(image, r):
rois = r["rois"]
class_ids = r["class_ids"]
scores = r["scores"]
height, width = image.shape[:2]
print("height, width =", height, width)
font = ImageFont.truetype('simsun.ttc',12)
image = Image.fromarray(image)
draw = ImageDraw.Draw(image)
# 画框
for index in range(len(rois)):
roi = rois[index]
class_id = class_ids[index]
score = scores[index]
y1, x1, y2, x2 = roi[0], roi[1], roi[2], roi[3]
print("y1, x1, y2, x2 =", y1, x1, y2, x2)
draw.line((x1, y1, x1, y2), "red")
draw.line((x1, y2, x2, y2), "red")
draw.line((x2, y2, x2, y1), "red")
draw.line((x2, y1, x1, y1), "red")
# 画类别名
draw.text((x1, y1), "class_name: qiyu, score: " + str(score), (255,255,255), font = font)
return np.asarray(image)
def detect_and_color_splash(model, image_path=None, video_path=None):
assert image_path or video_path
# 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'])
splash = box_splash(image, r)
# Save output
file_name = "splash_{:%Y%m%dT%H%M%S}.png".format(datetime.datetime.now())
skimage.io.imsave(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))
fps = vcapture.get(cv2.CAP_PROP_FPS)
# Define codec and create video writer
file_name = "splash_{:%Y%m%dT%H%M%S}.avi".format(datetime.datetime.now())
vwriter = cv2.VideoWriter(file_name,
cv2.VideoWriter_fourcc(*'MJPG'),
fps, (width, height))
count = 0
success = True
while success:
print("frame: ", count)
# Read next image
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'])
# RGB -> BGR to save image to video
splash = splash[..., ::-1]
# Add image to video writer
vwriter.write(splash)
count += 1
vwriter.release()
print("Saved to ", file_name)
开始训练咯
- python balloon.py train --weights ball --dataset D:\Mine\Pictures\maskrcnn\ones
- 使用train命令
- weights后面是指定的保存权重文件的名称
- dataset后面是数据集的根目录
-
训练中
image.png -
训练了12个epoch后
image.png
图片测试
python balloon.py splash --weights ball --image D:\Mine\Pictures\maskrcnn\ones\train\2.jpg
使用命令splash
weights用之前训练的ball文件
image 指定图片测试
-
检测一张, 还不错?
image.png -
再试一试, 居然无法区分埼玉老师和小丸子爷爷?
image.png
完
附上代码, 其实和balloon差不多就改了几行
#-*- coding:utf8 -*-
"""
Mask R-CNN
Train on the toy Balloon dataset and implement color splash effect.
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
python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=coco
# Resume training a model that you had trained earlier
python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=last
# Train a new model starting from ImageNet weights
python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=imagenet
# Apply color splash to an image
python3 balloon.py splash --weights=/path/to/weights/file.h5 --image=<URL or path to file>
# Apply color splash to video using the last weights you trained
python3 balloon.py splash --weights=last --video=<URL or path to file>
"""
import os
import sys
import json
import datetime
import numpy as np
import skimage.draw
import keras
# 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
# 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 BalloonConfig(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 = "balloon"
# 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 + 1 # Background + balloon
# Number of training steps per epoch
STEPS_PER_EPOCH = 100
# Skip detections with < 90% confidence
DETECTION_MIN_CONFIDENCE = 0.9
############################################################
# Dataset
############################################################
class BalloonDataset(utils.Dataset):
def load_balloon(self, dataset_dir, subset):
"""Load a subset of the Balloon 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("balloon", 1, "balloon")
# Train or validation dataset?
assert subset in ["train", "val"]
dataset_dir = os.path.join(dataset_dir, subset)
# Load annotations
# VGG Image Annotator (up to version 1.6) 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
# Note: In VIA 2.0, regions was changed from a dict to a list.
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. These are stores in the
# shape_attributes (see json format above)
# The if condition is needed to support VIA versions 1.x and 2.x.
if type(a['regions']) is dict:
polygons = [r['shape_attributes'] for r in a['regions'].values()]
else:
polygons = [r['shape_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(
"balloon",
image_id=a['filename'], # use file name as a unique image id
path=image_path,
width=width, height=height,
polygons=polygons)
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 balloon dataset image, delegate to parent class.
image_info = self.image_info[image_id]
if image_info["source"] != "balloon":
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]
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
return mask.astype(np.bool), 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"] == "balloon":
return info["path"]
else:
super(self.__class__, self).image_reference(image_id)
def train(model):
"""Train the model."""
# Training dataset.
dataset_train = BalloonDataset()
dataset_train.load_balloon(args.dataset, "train")
dataset_train.prepare()
# Validation dataset
dataset_val = BalloonDataset()
dataset_val.load_balloon(args.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=100,
layers='heads', custom_callbacks=[keras.callbacks.ModelCheckpoint(weights_path,
verbose=0, save_weights_only=True)])
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
# Copy color pixels from the original color image where mask is set
if mask.shape[-1] > 0:
# We're treating all instances as one, so collapse the mask into one layer
mask = (np.sum(mask, -1, keepdims=True) >= 1)
splash = np.where(mask, image, gray).astype(np.uint8)
else:
splash = gray.astype(np.uint8)
return splash
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
#画框
def box_splash(image, r):
rois = r["rois"]
class_ids = r["class_ids"]
scores = r["scores"]
height, width = image.shape[:2]
print("height, width =", height, width)
font = ImageFont.truetype('simsun.ttc',12)
image = Image.fromarray(image)
draw = ImageDraw.Draw(image)
# 画框
for index in range(len(rois)):
roi = rois[index]
class_id = class_ids[index]
score = scores[index]
y1, x1, y2, x2 = roi[0], roi[1], roi[2], roi[3]
print("y1, x1, y2, x2 =", y1, x1, y2, x2)
draw.line((x1, y1, x1, y2), "red")
draw.line((x1, y2, x2, y2), "red")
draw.line((x2, y2, x2, y1), "red")
draw.line((x2, y1, x1, y1), "red")
# 画类别名
draw.text((x1, y1), "class_name: qiyu, score: " + str(score), (255,255,255), font = font)
return np.asarray(image)
def detect_and_color_splash(model, image_path=None, video_path=None):
assert image_path or video_path
# 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'])
splash = box_splash(image, r)
# Save output
file_name = "splash_{:%Y%m%dT%H%M%S}.png".format(datetime.datetime.now())
skimage.io.imsave(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))
fps = vcapture.get(cv2.CAP_PROP_FPS)
# Define codec and create video writer
file_name = "splash_{:%Y%m%dT%H%M%S}.avi".format(datetime.datetime.now())
vwriter = cv2.VideoWriter(file_name,
cv2.VideoWriter_fourcc(*'MJPG'),
fps, (width, height))
count = 0
success = True
while success:
print("frame: ", count)
# Read next image
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'])
# RGB -> BGR to save image to video
splash = splash[..., ::-1]
# Add image to video writer
vwriter.write(splash)
count += 1
vwriter.release()
print("Saved to ", file_name)
############################################################
# Training
############################################################
if __name__ == '__main__':
import argparse
# Parse command line arguments
parser = argparse.ArgumentParser(
description='Train Mask R-CNN to detect balloons.')
parser.add_argument("command",
metavar="<command>",
help="'train' or 'splash'")
parser.add_argument('--dataset', required=False,
metavar="/path/to/balloon/dataset/",
help='Directory of the Balloon dataset')
parser.add_argument('--weights', required=True,
metavar="/path/to/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')
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 = BalloonConfig()
else:
class InferenceConfig(BalloonConfig):
# 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()
elif args.weights.lower() == "imagenet":
# Start from ImageNet trained weights
weights_path = model.get_imagenet_weights()
else:
if os.path.isfile(args.weights) == False:
model.keras_model.save_weights(args.weights)
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 == "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))
