Mask R-CNNのDemoをGoogleColaboratoryでやった。

Mask_RCNN/demo.ipynb at master · matterport/Mask_RCNN · GitHub
をGoogleColaboratoryでやった。

※!を行頭につけるとLinuxのコマンドを実行できる
※%が行頭についているのはIPythonのマジックコマンド

Mask R-CNN（keras）で人物検出 on Colaboratory - Qiita
を参考にしてGoogleColaboratoryに入ってないやつを入れる。

!git clone https://github.com/matterport/Mask_RCNN.git
!cd Mask_RCNN
!pip install -r requirements.txt
%run -i setup.py install
!wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5
!git clone https://github.com/waleedka/coco.git
!cd coco/PythonAPI
%run -i setup.py build_ext --inplace
%run -i setup.py build_ext install
#Mask_RCNNディレクトリまで戻る
cd ../../

matplotlibの設定

%matplotlib inline

import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt

# Root directory of the project
#GoogleColaboratoryのディレクトリに合わせる
ROOT_DIR = "/content/Mask_RCNN"

# 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
import coco


# 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")

#エラーが出るので足す
from samples.coco import coco

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')
#BGはBackgroundの略。常に0番目に来る。
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)))

をそのままやるとGoogleColaboratoryだとStopiterationのエラーが出るので

image = skimage.io.imread(os.path.join(IMAGE_DIR, "1045023827_4ec3e8ba5c_z.jpg"))

にした。(file_namesのところはMask_RCNN/imagesから適当に選ぶ)
後は

# 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'])

で表示する。

メモです

メモです

Mask R-CNNのDemoをGoogleColaboratoryでやった。