def detect(self, image):
cuda.select_device(0)
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
ROOT_DIR = "/home/bernihoh/Bachelor/SMS/MaskRCNN/samples/SMSNetworks/face_feature_detection/"
MODEL_DIR = os.path.join(ROOT_DIR, "logsFaceFeatureDetection")
COCO_MODEL_PATH = "/home/bernihoh/Bachelor/SMS/MaskRCNN/samples/SMSNetworks/face_feature_detection/mask_rcnn_face_feature_detection_0029.h5"
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)
class_names = ["bg", "iris_l", "inner_eye_l", "outer_eye_l", "eye_brow_l", "cheek_l", "iris_r",
"inner_eye_r", "outer_eye_r", "eye_brow_r", "cheek_r", "nose_tip", "nose", "mouth",
"chin", "face", "head", "distortion"]
results = model.detect([image], verbose=1)
r = results[0]
session.close()
cuda.close()
return r
def detect(self):
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
self.MODEL_DIR = self.model_dir()
self.NETWORK_PATH = self.network_path()
self.InferenceConfig = self.inference_config()
self.InferenceConfig.display()
self.model = modellib.MaskRCNN(mode="inference",
model_dir=self.MODEL_DIR,
config=self.InferenceConfig)
self.model.load_weights(self.NETWORK_PATH, by_name=True)
results = self.model.detect([self.image], verbose=1)
"""r looks like this: r['rois'], r['masks'], r['class_ids'], r['scores']"""
r = results[0]
class_names = self.class_names()
visualize.display_instances(self.image, r['rois'], r['masks'],
r['class_ids'], class_names, r['scores'])
session.close()
ret = self.further_image_manipulation(r)
return ret
def detect(self, image_path, background_color):
cuda.select_device(0)
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
ROOT_DIR = os.path.abspath("/")
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
sys.path.append(ROOT_DIR) # To find local version of the library
sys.path.append(os.path.join(ROOT_DIR, "samples/coco/")) # To find local version
COCO_MODEL_PATH = "/home/bernihoh/Bachelor/MaskRCNN/mask_rcnn_coco.h5"
if not os.path.exists(COCO_MODEL_PATH):
utils.download_trained_weights(COCO_MODEL_PATH)
config = InferenceConfig()
config.display()
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
model.load_weights(COCO_MODEL_PATH, by_name=True)
COCO_DIR = "/home/bernihoh/Bachelor/MaskRCNN/samples/coco" # geändert: Zeile eingefügt
dataset = coco.CocoDataset()
dataset.load_coco(COCO_DIR, "train")
dataset.prepare()
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']
image = skimage.io.imread(image_path)
print(dataset.class_names)
plt.imshow(image)
# 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'])
used_class = r["class_ids"]
print(used_class)
mask = r["masks"]
mask = mask.astype(np.ubyte)
# maskimg = mask[:, :, 1] ^ mask[:, :, 1]
maskimg = np.zeros((image.shape[0], image.shape[1]))
maskimg = maskimg.astype(np.ubyte)
background_mask = np.full((image.shape[0], image.shape[1]), background_color, dtype=np.uint8)
skimage.io.imshow(background_mask)
plt.show()
for i in range(mask.shape[2]):
# skimage.io.imshow(mask[:, :, i])
# plt.show()
# maskimg = maskimg | mask[:, :, i]
a = used_class[i] - 1
if used_class[i] - 1 < 0:
a = 0
background_mask = background_mask - mask[:, :, i] * (a + 127)
maskimg = np.maximum(maskimg, mask[:, :, i] * a)
skimage.io.imshow(background_mask)
plt.show()
maskimg = np.maximum(maskimg, background_mask)
# maskimg[maskimg == 0] = 124
# maskimg = skimage.exposure.rescale_intensity(maskimg)
skimage.io.imshow(maskimg)
plt.show()
# skimage.io.imsave("/home/bernihoh/Bachelor/MaskRCNN/ownimages/mask138-1.jpg", maskimg)
session.close()
cuda.close()
return maskimg
# Directory of images to run detection on
#IMAGE_DIR = os.path.join(ROOT_DIR, "pic_train")
class InferenceConfig(head_detection.CocoHeadDetectionConfig):
# 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)
# Load COCO dataset
# COCO_DIR = "/home/bernihoh/Bachelor/MaskRCNN/samples/coco" # geändert: Zeile eingefügt
#dataset = head_detection.CocoHeadDetectionDataset()
#dataset.load_head_detection("/home/bernihoh/Bachelor/SMS/MaskRCNN/samples/SMSNetworks/head_place_detection/", "train")
#dataset.prepare()
class_names = ["bg", "head"]
# Load a random image from the images folder
"""
for i in range(8):
image = skimage.io.imread("/home/bernihoh/Bachelor/SMS/MaskRCNN/samples/SMSNetworks/head_place_detection/pic_val/"+str(i)+".png")
else:
class InferenceConfig(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
config = InferenceConfig()
config.display()
# Create model
if args.command == "train":
model = modellib.MaskRCNN(mode="training",
config=config,
model_dir=args.logs)
else:
model = modellib.MaskRCNN(mode="inference",
config=config,
model_dir=args.logs)
# Select weights file to load
if args.model.lower() == "coco":
model_path = COCO_MODEL_PATH
elif args.model.lower() == "last":
# Find last trained weights
model_path = model.find_last()
elif args.model.lower() == "imagenet":
# Start from ImageNet trained weights
model_path = model.get_imagenet_weights()