版權所有，翻版必究。https://mp.csdn.net/console/editor/html/106091110
運行環境：WIN10，pycharm，相應的CUDA，CUDNN，tensorflow1.15.0，tensorflow-gpu-1.14.0，Anaconda3
Mask RCNN開源項目：https://github.com/matterport/Mask_RCNN
第一步：數據準備
首先安裝labelme進行數據的標註。安裝方法pip install labelme。根據後面程序中的有關問題，有些大佬說安裝labelme3.2版本。
（網上很多使用方法，不會用可以查一查）
有關mask-rcnn算法可以參考：https://blog.csdn.net/linolzhang/article/details/71774168 （可以去看看論文比較好，網上容易搜索到）
注意：官網中並不需要準備數據，因爲他是程序中生成的簡單數據。有關自己數據的訓練，下一篇博客再說。
第二步：使用Anaconda3中的Jupyter Notebook。
點擊Upload添加demo.ipynb（Mask_RCNN-master\samples路徑下）。打開的目的不是在這個上面運行，我一般都是在pycharm中運行。目的是將其轉到pycharm中。
不過在這之前還需要添加一些網絡上學好的模型：https://github.com/matterport/Mask_RCNN/releases
有兩種形式，這裏選擇的是mask_rcnn_coco.h5。新建了一個demo.py，並將mask_rcnn_coco.h5下載放進其中。
demo.py中存放的是demo.ipynb中的代碼（注意提取mask_rcnn_coco.h5的路徑問題！！！）：
import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
import cv2
import time
# 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 import utils
import mrcnn.model as modellib
from mrcnn import visualize
# Import COCO config
sys.path.append(os.path.join(ROOT_DIR, "samples/coco/")) # To find local version
from samples.coco import coco #這個地方需要更改，主要就是加載路徑問題！！1
# Directory to save logs and trained model
# MODEL_DIR = os.path.join(ROOT_DIR, "logs")
MODEL_DIR = ROOT_DIR #路徑問題，爲了提取mask_rcnn_coco.h5
# Local path to trained weights file
# COCO_MODEL_PATH = os.path.join(MODEL_DIR, "mask_rcnn_coco.h5")
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "Mask_RCNN-master\\mask_rcnn_coco.h5") #路徑！
print("COCO_MODEL_PATH: ", COCO_MODEL_PATH)
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
utils.download_trained_weights(COCO_MODEL_PATH)
print("cuiwei***********************")
# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")
class InferenceConfig(coco.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.MaskRCNN(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(os.path.join(IMAGE_DIR, random.choice(file_names)))
image= cv2.imread("./images/12283150_12d37e6389_z.jpg")
# 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'])
結果顯示：
OK！基本測試已經搞定。
第三步：添加train_shapes.ipynb。並按照上面的步驟，更改爲train_shapes.py。
import os
import sys
import random
import math
import re
import time
import numpy as np
import cv2
import matplotlib
import matplotlib.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 utils
import mrcnn.model as modellib
from mrcnn import visualize
from mrcnn.model import log
# %matplotlib inline
# 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-master-1\\Mask_RCNN-master\\mask_rcnn_coco.h5")
print(COCO_MODEL_PATH)
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
utils.download_trained_weights(COCO_MODEL_PATH)
class ShapesConfig(Config):
"""Configuration for training on the toy shapes dataset.
Derives from the base Config class and overrides values specific
to the toy shapes dataset.
"""
# Give the configuration a recognizable name
NAME = "shapes"
# Train on 1 GPU and 8 images per GPU. We can put multiple images on each
# GPU because the images are small. Batch size is 8 (GPUs * images/GPU).
GPU_COUNT = 1
IMAGES_PER_GPU = 8
# Number of classes (including background)
NUM_CLASSES = 1 + 3 # background + 3 shapes
# NUM_CLASSES = 1 + 1 # background + 1 shapes
# Use small images for faster training. Set the limits of the small side
# the large side, and that determines the image shape.
IMAGE_MIN_DIM = 128
IMAGE_MAX_DIM = 128
# Use smaller anchors because our image and objects are small
RPN_ANCHOR_SCALES = (8, 16, 32, 64, 128) # anchor side in pixels
# Reduce training ROIs per image because the images are small and have
# few objects. Aim to allow ROI sampling to pick 33% positive ROIs.
TRAIN_ROIS_PER_IMAGE = 32
# Use a small epoch since the data is simple
STEPS_PER_EPOCH = 100
# use small validation steps since the epoch is small
VALIDATION_STEPS = 5
config = ShapesConfig()
config.display()
def get_ax(rows=1, cols=1, size=8):
"""Return a Matplotlib Axes array to be used in
all visualizations in the notebook. Provide a
central point to control graph sizes.
Change the default size attribute to control the size
of rendered images
返回要在其中使用的Matplotlib Axes數組
筆記本中的所有可視化。 提供一個
控制圖形大小的中心點。
更改默認大小屬性以控制大小
渲染圖像
"""
_, ax = plt.subplots(rows, cols, figsize=(size * cols, size * rows))
return ax
class ShapesDataset(utils.Dataset):
"""Generates the shapes synthetic dataset. The dataset consists of simple
shapes (triangles, squares, circles) placed randomly on a blank surface.
The images are generated on the fly. No file access required.
生成形狀數據集。 數據集由簡單形狀（三角形，正方形，圓形）隨機放置在空白表面上。這些圖像是即時生成的。 無需文件訪問。
"""
def load_shapes(self, count, height, width):
"""Generate the requested number of synthetic images.
count: number of images to generate.
height, width: the size of the generated images.
生成所需數量的合成圖像。
count：要生成的圖像數。
高度，寬度：所生成圖像的大小。
"""
# Add classes
self.add_class("shapes", 1, "square")
self.add_class("shapes", 2, "circle")
self.add_class("shapes", 3, "triangle")
# self.add_class("shapes", 1, "xueqing")
# Add images
# Generate random specifications of images (i.e. color and
# list of shapes sizes and locations). This is more compact than
# actual images. Images are generated on the fly in load_image().
'''＃添加圖片
＃生成圖像的隨機規格（即彩色和形狀大小和位置的列表）。 這比
實際圖像。 圖像是在load_image（）中即時生成的。
'''
for i in range(count):
bg_color, shapes = self.random_image(height, width)
self.add_image("shapes", image_id=i, path=None,
width=width, height=height,
bg_color=bg_color, shapes=shapes)
def load_image(self, image_id):
"""Generate an image from the specs of the given image ID.
Typically this function loads the image from a file, but
in this case it generates the image on the fly from the
specs in image_info.
"""
info = self.image_info[image_id]
bg_color = np.array(info['bg_color']).reshape([1, 1, 3])
image = np.ones([info['height'], info['width'], 3], dtype=np.uint8)
image = image * bg_color.astype(np.uint8)
for shape, color, dims in info['shapes']:
image = self.draw_shape(image, shape, dims, color)
return image
def image_reference(self, image_id):
"""Return the shapes data of the image."""
info = self.image_info[image_id]
if info["source"] == "shapes":
return info["shapes"]
else:
super(self.__class__).image_reference(self, image_id)
def load_mask(self, image_id):
"""Generate instance masks for shapes of the given image ID.
"""
info = self.image_info[image_id]
shapes = info['shapes']
count = len(shapes)
mask = np.zeros([info['height'], info['width'], count], dtype=np.uint8)
for i, (shape, _, dims) in enumerate(info['shapes']):
mask[:, :, i:i + 1] = self.draw_shape(mask[:, :, i:i + 1].copy(),
shape, dims, 1)
# Handle occlusions
occlusion = np.logical_not(mask[:, :, -1]).astype(np.uint8)
for i in range(count - 2, -1, -1):
mask[:, :, i] = mask[:, :, i] * occlusion
occlusion = np.logical_and(occlusion, np.logical_not(mask[:, :, i]))
# Map class names to class IDs.
class_ids = np.array([self.class_names.index(s[0]) for s in shapes])
return mask.astype(np.bool), class_ids.astype(np.int32)
def draw_shape(self, image, shape, dims, color):
"""Draws a shape from the given specs."""
# Get the center x, y and the size s
x, y, s = dims
if shape == 'square':
cv2.rectangle(image, (x - s, y - s), (x + s, y + s), color, -1)
elif shape == "circle":
cv2.circle(image, (x, y), s, color, -1)
elif shape == "triangle":
points = np.array([[(x, y - s),
(x - s / math.sin(math.radians(60)), y + s),
(x + s / math.sin(math.radians(60)), y + s),
]], dtype=np.int32)
cv2.fillPoly(image, points, color)
return image
def random_shape(self, height, width):
"""Generates specifications of a random shape that lies within
the given height and width boundaries.
Returns a tuple of three valus:
* The shape name (square, circle, ...)
* Shape color: a tuple of 3 values, RGB.
* Shape dimensions: A tuple of values that define the shape size
and location. Differs per shape type.
"""
# Shape
shape = random.choice(["square", "circle", "triangle"])
# Color
color = tuple([random.randint(0, 255) for _ in range(3)])
# Center x, y
buffer = 20
y = random.randint(buffer, height - buffer - 1)
x = random.randint(buffer, width - buffer - 1)
# Size
s = random.randint(buffer, height // 4)
return shape, color, (x, y, s)
def random_image(self, height, width):
"""Creates random specifications of an image with multiple shapes.
Returns the background color of the image and a list of shape
specifications that can be used to draw the image.
創建具有多種形狀的圖像的隨機規格。
返回圖像的背景色和形狀列表可用於繪製圖像的規格。
"""
# Pick random background color
bg_color = np.array([random.randint(0, 255) for _ in range(3)])
# Generate a few random shapes and record their
# bounding boxes
shapes = []
boxes = []
N = random.randint(1, 4)
for _ in range(N):
shape, color, dims = self.random_shape(height, width)
shapes.append((shape, color, dims))
x, y, s = dims
boxes.append([y - s, x - s, y + s, x + s])
# Apply non-max suppression wit 0.3 threshold to avoid
# shapes covering each other
keep_ixs = utils.non_max_suppression(np.array(boxes), np.arange(N), 0.3)
shapes = [s for i, s in enumerate(shapes) if i in keep_ixs]
return bg_color, shapes
# Training dataset 訓練數據
dataset_train = ShapesDataset()
dataset_train.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
dataset_train.prepare()
# Validation dataset 測試數據
dataset_val = ShapesDataset()
dataset_val.load_shapes(50, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
dataset_val.prepare()
# Load and display random samples 加載並顯示隨機樣本
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)
# 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,
exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
"mrcnn_bbox", "mrcnn_mask"])
elif init_with == "last":
# Load the last model you trained and continue training
model.load_weights(model.find_last(), by_name=True)
# Train the head branches
# Passing layers="heads" freezes all layers except the head
# layers. You can also pass a regular expression to select
# which layers to train by name pattern.
# 訓練頭上的樹枝，傳遞layers =“ heads”將凍結除head以外的所有圖層
# 還可以傳遞正則表達式來選擇，按名稱模式訓練哪些層。
model.train(dataset_train, dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=1,
layers='heads')
# Fine tune all layers
# Passing layers="all" trains all layers. You can also
# pass a regular expression to select which layers to
# train by name pattern.
# 微調所有圖層。通過layers =“all”訓練所有層。 你也可以傳遞正則表達式以選擇要圖層。按名稱訓練模式。
model.train(dataset_train, dataset_val,
learning_rate=config.LEARNING_RATE / 10,
epochs=2,
layers="all")
需要更改model.py中的2200行：
把2199這一行改爲2200這一行！！！！！