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
for i in range(mask.shape[2]):
maskimg = np.maximum(maskimg, mask[:, :, i]*(used_class[i]))
skimage.io.imshow(maskimg)
plt.show()
# skimage.io.imsave("/home/bernihoh/Bachelor/MaskRCNN/ownimages/mask138-1.jpg", maskimg)
"""
image = skimage.io.imread(
"/home/bernihoh/Bachelor/SPADE/results/coco_pretrained/test_latest/images/input_label/2020-05-28 13:58:02.png"
)
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)
for i in range(mask.shape[2]):
maskimg = np.maximum(maskimg, mask[:, :, i] * (used_class[i]))
skimage.io.imshow(maskimg)
plt.show()
# skimage.io.imsave("/home/bernihoh/Bachelor/MaskRCNN/ownimages/mask138-1.jpg", maskimg)