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 create_face_net_dict_files_for_train_val(picture_path, pic_file):
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
pic = skimage.io.imread(picture_path + pic_file)
ffn = FFN(pic)
ffn_results = ffn.detect()
session.close()
if ffn_results == "redo":
print("redo")
return "Failure"
else:
output = open(
"/home/bernihoh/Bachelor/SMS/modification/netcompare/services/face_net/face_net_pkl_val/"
+ pic_file + ".pkl", "wb")
pkl.dump(ffn_results["face_score_input"], output)
output.close()
return "Success"
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