import os
import sys
import shutil
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
import pickle
from tqdm import tqdm
# Root directory of the project
ROOT_DIR = os.path.abspath("./Mask_RCNN")
import warnings
warnings.filterwarnings("ignore")
# 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
%matplotlib inline
Using TensorFlow backend.
# 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('', "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("./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
DETECTION_MIN_CONFIDENCE = 0.525
config = InferenceConfig()
# Create model object in inference mode.
model = modellib.MaskRCNN(mode="inference", model_dir='mask_rcnn_coco.hy', config=config)
# Load weights trained on MS-COCO
model.load_weights('mask_rcnn_coco.h5', by_name=True)
WARNING:tensorflow:From E:\ProgramData\Anaconda3\envs\mask_rcnn\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. Instructions for updating: If using Keras pass *_constraint arguments to layers. WARNING:tensorflow:From E:\ProgramData\Anaconda3\envs\mask_rcnn\lib\site-packages\keras\backend\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead. WARNING:tensorflow:From D:\projet E4\Mask_RCNN\mrcnn\model.py:341: The name tf.log is deprecated. Please use tf.math.log instead. WARNING:tensorflow:From D:\projet E4\Mask_RCNN\mrcnn\model.py:399: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where WARNING:tensorflow:From D:\projet E4\Mask_RCNN\mrcnn\model.py:423: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From D:\projet E4\Mask_RCNN\mrcnn\model.py:720: The name tf.sets.set_intersection is deprecated. Please use tf.sets.intersection instead. WARNING:tensorflow:From D:\projet E4\Mask_RCNN\mrcnn\model.py:722: The name tf.sparse_tensor_to_dense is deprecated. Please use tf.sparse.to_dense instead. WARNING:tensorflow:From D:\projet E4\Mask_RCNN\mrcnn\model.py:772: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead.
# COCO Class names
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']
files = os.listdir('./images')
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 14497312495509646053
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 4965466112
locality {
bus_id: 1
links {
}
}
incarnation: 7804349373482791636
physical_device_desc: "device: 0, name: GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5"
]
for i in tqdm(range(len(files))):
try:
# Load a random image from the images folder
file = files[i]
pfile = './images/'+file
image = skimage.io.imread(pfile)
# Run detection
results = model.detect([image], verbose=0)
r = results[0]
mask = r['masks']
fmask = np.zeros((mask.shape[0],mask.shape[1]))
for i in range(mask.shape[0]):
for j in range(mask.shape[1]):
for k in range(mask.shape[2]):
fmask[i][j] += mask[i][j][k]
fmask = fmask.astype(bool)
fmask = fmask.astype(np.uint8)
temp = skimage.io.imread(pfile)
for i in range(temp.shape[2]):
temp[:,:,i] *= fmask[:,:]
skimage.io.imsave('./masked_images/'+file,temp)
except:
try:
file = files[i]
dest = './masked_images/'+file
shutil.copy(file,dest)
except : pass
0%| | 0/249470 [00:00<?, ?it/s]
WARNING:tensorflow:From E:\ProgramData\Anaconda3\envs\mask_rcnn\lib\site-packages\keras\backend\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
100%|████████████████████████████████████████████████████████████████████████| 249470/249470 [9:15:45<00:00, 7.48it/s]