def OD_camera(args=None):
print("hello world")
# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
class SmallEvalConfig(siamese_config.Config):
# 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
NUM_CLASSES = 1 + 1
NAME = 'coco'
EXPERIMENT = 'evaluation'
CHECKPOINT_DIR = ROOT_DIR + 'checkpoints/'
NUM_TARGETS = 1
class LargeEvalConfig(siamese_config.Config):
# 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
NUM_CLASSES = 1 + 1
NAME = 'coco'
EXPERIMENT = 'evaluation'
CHECKPOINT_DIR = ROOT_DIR + 'checkpoints/'
NUM_TARGETS = 1
# Large image sizes
TARGET_MAX_DIM = 192
TARGET_MIN_DIM = 150
IMAGE_MIN_DIM = 800
IMAGE_MAX_DIM = 1024
# Large model size
FPN_CLASSIF_FC_LAYERS_SIZE = 1024
FPN_FEATUREMAPS = 256
# Large number of rois at all stages
RPN_ANCHOR_STRIDE = 1
RPN_TRAIN_ANCHORS_PER_IMAGE = 256
POST_NMS_ROIS_TRAINING = 2000
POST_NMS_ROIS_INFERENCE = 1000
TRAIN_ROIS_PER_IMAGE = 200
DETECTION_MAX_INSTANCES = 100
MAX_GT_INSTANCES = 100
# The small model trains on a single GPU and runs much faster.
# The large model is the same we used in our experiments but needs multiple GPUs and more time for training.
model_size = 'large'
if model_size == 'small':
config = SmallEvalConfig()
elif model_size == 'large':
config = LargeEvalConfig()
config.display()
# Provide training schedule of the model
# When evaluationg intermediate steps the tranining schedule must be provided
train_schedule = OrderedDict()
if model_size == 'small':
train_schedule[1] = {
"learning_rate": config.LEARNING_RATE,
"layers": "heads"
}
train_schedule[120] = {
"learning_rate": config.LEARNING_RATE,
"layers": "4+"
}
train_schedule[160] = {
"learning_rate": config.LEARNING_RATE / 10,
"layers": "all"
}
elif model_size == 'large':
train_schedule[1] = {
"learning_rate": config.LEARNING_RATE,
"layers": "heads"
}
train_schedule[240] = {
"learning_rate": config.LEARNING_RATE,
"layers": "all"
}
train_schedule[320] = {
"learning_rate": config.LEARNING_RATE / 10,
"layers": "all"
}
# Select checkpoint
if model_size == 'small':
checkpoint = ROOT_DIR + 'checkpoints/small_siamese_mrcnn_0160.h5'
elif model_size == 'large':
checkpoint = ROOT_DIR + 'checkpoints/large_siamese_mrcnn_coco_full_0320.h5'
config.NUM_TARGETS = 1
print("test begin")
# Create model object in inference mode.
model = siamese_model.SiameseMaskRCNN(mode="inference",
model_dir=MODEL_DIR,
config=config)
model.load_checkpoint(checkpoint, training_schedule=train_schedule)
category = 1
ref_images = []
for i in range(1, config.NUM_TARGETS + 1):
ref_image = io.imread(ROOT_DIR + "testimage/train/train%d.jpg" % i)
ref_images.append(ref_image)
cap = cv2.VideoCapture(1)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
# check camera availabe
print("Camera available ？ {}".format(cap.isOpened()))
# plot size
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
# Use GridSpec to show target smaller than image
img_count = 1
while (True):
start = time.time()
ret, frame = cap.read()
## opencv image to skimage BGR->RGB
# image = img_as_float(frame)
# cv2.imshow('image_win', frame)
image = frame[:, :, ::-1]
query_image = image
results = model.detect_category(category=category,
targets=[ref_images],
images=[query_image],
verbose=1)
# Run detection
r = results[0]
if max(r['scores']) > 0.9:
yield ("Door detected!")
else:
yield ("Door NOT detected!")
key = cv2.waitKey(1)
cap.release()
train_schedule[120] = {"learning_rate": config.LEARNING_RATE, "layers": "4+"}
train_schedule[160] = {"learning_rate": config.LEARNING_RATE/10, "layers": "all"}
elif model_size == 'large':
train_schedule[1] = {"learning_rate": config.LEARNING_RATE, "layers": "heads"}
train_schedule[240] = {"learning_rate": config.LEARNING_RATE, "layers": "all"}
train_schedule[320] = {"learning_rate": config.LEARNING_RATE/10, "layers": "all"}
# Select checkpoint
if model_size == 'small':
checkpoint = 'checkpoints/small_siamese_mrcnn_0160.h5'
elif model_size == 'large':
checkpoint = 'checkpoints/large_siamese_mrcnn_coco_full_0320.h5'
# Load and evaluate model
# Create model object in inference mode.
model = siamese_model.SiameseMaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
model.load_checkpoint(checkpoint, training_schedule=train_schedule)
# Evaluate only active classes
active_class_idx = np.array(coco_val.ACTIVE_CLASSES) - 1
count = 0
for layer in model.keras_model.layers:
count += 1
if str(layer).find('Model') != -1:
print(count, str(layer))
break
index = count - 1
model.keras_model.layers[index].summary()
filters, biases = model.keras_model.layers[index].layers[9].get_weights()
coco_train = siamese_utils.IndexedFssCellDataset()
coco_train.load_coco(COCO_DATA, subset="train_val")
coco_train.prepare()
coco_train.build_indices()
coco_train.ACTIVE_CLASSES = train_classes
# Load COCO/val dataset
coco_val = siamese_utils.IndexedFssCellDataset()
coco_val.load_coco(COCO_DATA, subset="test")
coco_val.prepare()
coco_val.build_indices()
coco_val.ACTIVE_CLASSES = test_classes
# Create model object in inference mode.
model = siamese_model.SiameseMaskRCNN(mode="training",
model_dir=MODEL_DIR,
config=config)
train_schedule = OrderedDict()
train_schedule[1] = {"learning_rate": config.LEARNING_RATE, "layers": "heads"}
train_schedule[10] = {"learning_rate": config.LEARNING_RATE, "layers": "all"}
train_schedule[10] = {
"learning_rate": config.LEARNING_RATE / 10,
"layers": "all"
}
# Load weights trained on Imagenet
model.load_imagenet_weights(pretraining='imagenet-687')
for epochs, parameters in train_schedule.items():
print("")
NUM_TARGETS = 1
# The small model trains on a single GPU and runs much faster.
# The large model is the same we used in our experiments but needs multiple GPUs and more time for training.
model_size = 'small' # or 'large'
if model_size == 'small':
config = SmallTrainConfig()
elif model_size == 'large':
config = LargeTrainConfig()
config.display()
config_test = SmallEvalConfig()
# Create model object in inference mode.
model = siamese_model.SiameseMaskRCNN(mode="training", model_dir=MODEL_DIR, config=config)
# train_schedule = OrderedDict()
# train_schedule[1] = {"learning_rate": config.LEARNING_RATE, "layers": "heads"}
# train_schedule[120] = {"learning_rate": config.LEARNING_RATE, "layers": "all"}
# train_schedule[160] = {"learning_rate": config.LEARNING_RATE/10, "layers": "all"}
# We commented out the above as google colab kept crashing, so we reduced the load
# Check if both folders under 'logs' folder are empty before running model.train
train_schedule = OrderedDict()
train_schedule[1] = {"learning_rate": config.LEARNING_RATE, "layers": "heads"}
train_schedule[10] = {"learning_rate": config.LEARNING_RATE, "layers": "all"}
# Load weights trained on Imagenet
try:
