def extract(self, images, target_dir):
config = InferenceConfig()
dataset = InferenceDataset(images)
config.display()
dataset.prepare()
utils.ensure_dir(target_dir)
coco_model_path = 'mask_rcnn_coco.h5'
if not os.path.exists(coco_model_path):
mrcnn.utils.download_trained_weights(coco_model_path)
model = ResNet(mode="inference", config=config, model_dir=target_dir)
exclude_layers = [
"mrcnn_class_logits",
"mrcnn_bbox_fc",
"mrcnn_bbox",
"mrcnn_mask",
]
model.load_weights(coco_model_path,
by_name=True,
exclude=exclude_layers)
executor = ProcessPoolExecutor(max_workers=4)
for i, image_info in enumerate(dataset.image_info):
image_name = os.path.basename(image_info['path'])
print('Processing image {}'.format(image_name))
image = dataset.load_image(i)
features = model.detect(image)
executor.submit(self.store_features, features[:4],
os.path.join(target_dir, image_name))
executor.shutdown(wait=True)
def predecir(self, event):
print("Funciono boton")
config = estadios.BalloonConfig()
config = InferenceConfig()
config.display()
DATA_DIR = "./Modelo/tfg_notebook/main_1/dataset"
BALLOON_DIR = DATA_DIR
dataset = estadios.BalloonDataset()
with wx.FileDialog(self,
"Selecciona una imagen para predecir su clase",
style=wx.FD_OPEN
| wx.FD_FILE_MUST_EXIST) as fileDialog:
if fileDialog.ShowModal() == wx.ID_CANCEL:
return
pathname = fileDialog.GetPath()
try:
with open(pathname, 'r') as file:
print(pathname)
dataset.load_balloon(BALLOON_DIR, "val")
dataset.prepare()
print("Images: {}\nClasses: {}".format(
len(dataset.image_ids), dataset.class_names))
config = InferenceConfig()
with tf.device(DEVICE):
model = modellib.MaskRCNN(mode="inference",
model_dir=MODEL_DIR,
config=config)
weights_path = model.find_last()
print("Loading weights ", weights_path)
model.load_weights(weights_path, by_name=True)
#image = skimage.io.imread("./Modelo/tfg_notebook/main_1/dataset/val/estadio-25.JPG")
image = skimage.io.imread(pathname)
plt.figure(figsize=(12, 10))
skimage.io.imshow(image)
plt.show()
result = model.detect([image], verbose=1)
r = result[0]
visualize.display_instances(image, r['rois'], r['masks'],
r['class_ids'],
dataset.class_names,
r['scores'])
except IOError:
wx.LogError("No se pudo abrir el archivo")
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")
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 and Load Trained Weights"""
# 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',
def imagedetection():
# 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
import mrcnn.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
# 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")
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]
filename = os.path.join(IMAGE_DIR ,"car.jpg")
#image = skimage.io.imread(filename)
image = cv2.imread(filename,color)
# Run detection
results = model.detect([image], verbose=1)
# Visualize results
r = results[0]
overlaps = mrcnn.utils.compute_overlaps(parked_car_boxes, result['rois'])
for parking_area, overlap_areas in zip(parked_car_boxes, overlaps):
max_IoU_overlap = np.max(overlap_areas)
if max_IoU_overlap < 0.85:
return 0
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'])