def extract_bboxes_from_mask(mask):
import cv2
from skimage.io import imread
from skimage.segmentation import mark_boundaries
from skimage.measure import label, regionprops
from skimage.util.montage import montage2d as montage
montage_rgb = lambda x: np.stack([montage(x[:,:,:,i]) for i in range(x.shape[3])],-1)
from skimage.morphology import label
lbl_0 = label(mask)
props = regionprops(lbl_0)
bbox = []
for prop in props:
bbox.append(list(prop.bbox))
boxes = np.array(bbox)
return boxes.astype(np.int32)
# # Overview
# Since making all the predictions takes a long time and quite a bit of memory, we make a seperate kernel for just the submission, where we read the test data and apply the model. The model is built and trained in the kernel at https://www.kaggle.com/kmader/baseline-u-net-model-part-1
#
# In[ ]:
import os
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from skimage.io import imread
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
from skimage.util.montage import montage2d as montage
montage_rgb = lambda x: np.stack(
[montage(x[:, :, :, i]) for i in range(x.shape[3])], -1)
ship_dir = '../input/'
train_image_dir = os.path.join(ship_dir, 'train')
test_image_dir = os.path.join(ship_dir, 'test')
import gc
gc.enable() # memory is tight
from skimage.morphology import label
def multi_rle_encode(img):
labels = label(img[:, :, 0])
return [rle_encode(labels == k) for k in np.unique(labels[labels > 0])]
# ref: https://www.kaggle.com/paulorzp/run-length-encode-and-decode
BATCH_SIZE = 16
# In[ ]:
import os
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from skimage.io import imread
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
from skimage.util.montage import montage2d as montage
from utillc import *
montage_rgb = lambda x: np.stack(
[montage(x[:, :, :, i]) for i in range(x.shape[3])], -1)
ship_dir = '../input'
ship_dir = '/local/qnap/Data/ab'
train_image_dir = os.path.join(ship_dir, 'train')
test_image_dir = os.path.join(ship_dir, 'test')
# ref: https://www.kaggle.com/paulorzp/run-length-encode-and-decode
def rle_encode(img):
'''
img: numpy array, 1 - mask, 0 - background
Returns run length as string formated
'''
pixels = img.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
BATCH_SIZE = 16
EDGE_CROP = 16
GAUSSIAN_NOISE = 0.1
UPSAMPLE_MODE = 'SIMPLE'
# downsampling inside the network
NET_SCALING = (1, 1)
# downsampling in preprocessing
IMG_SCALING = (4, 4)
# number of validation images to use
VALID_IMG_COUNT = 600
# maximum number of steps_per_epoch in training
MAX_TRAIN_STEPS = 150
AUGMENT_BRIGHTNESS = False
montage_rgb = lambda x: np.stack([montage(x[:, :, :, i]) for i in range(x.shape[3])], -1)
ship_dir = '/Users/msun/data/'
train_image_dir = os.path.join(ship_dir, 'train')
test_image_dir = os.path.join(ship_dir, 'test')
def multi_rle_encode(img):
labels = label(img[:, :, 0])
return [rle_encode(labels==k) for k in np.unique(labels[labels>0])]
# ref: https://www.kaggle.com/paulorzp/run-length-encode-and-decode
def rle_encode(img):
'''
img: numpy array, 1 - mask, 0 - background
# # Overview
# Since making all the predictions takes a long time and quite a bit of memory, we make a seperate kernel for just the submission, where we read the test data and apply the model. The model is built and trained in the kernel at https://www.kaggle.com/kmader/baseline-u-net-model-part-1
#
# In[ ]:
import os
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from skimage.io import imread
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
from skimage.util.montage import montage2d as montage
montage_rgb = lambda x: np.stack([montage(x[:, :, :, i]) for i in range(x.shape[3])], -1)
ship_dir = '../input/'
train_image_dir = os.path.join(ship_dir, 'train')
test_image_dir = os.path.join(ship_dir, 'test')
import gc; gc.enable() # memory is tight
from skimage.morphology import label
def multi_rle_encode(img):
labels = label(img[:, :, 0])
return [rle_encode(labels==k) for k in np.unique(labels[labels>0])]
# ref: https://www.kaggle.com/paulorzp/run-length-encode-and-decode
def rle_encode(img):
'''
img: numpy array, 1 - mask, 0 - background
Returns run length as string formated
# downsampling in preprocessing
IMG_SCALING = (4, 4)
# number of validation images to use
VALID_IMG_COUNT = 600
# maximum number of steps_per_epoch in training
MAX_TRAIN_STEPS = 150
AUGMENT_BRIGHTNESS = False
import os
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from skimage.io import imread
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
from skimage.util.montage import montage2d as montage
montage_rgb = lambda x: np.stack([montage(x[:, :, :, i]) for i in range(x.shape[3])], -1)
ship_dir = '/Users/msun/data/'
train_image_dir = os.path.join(ship_dir, 'train')
test_image_dir = os.path.join(ship_dir, 'test')
import gc; gc.enable() # memory is tight
from skimage.morphology import label
def multi_rle_encode(img):
labels = label(img[:, :, 0])
return [rle_encode(labels==k) for k in np.unique(labels[labels>0])]
# ref: https://www.kaggle.com/paulorzp/run-length-encode-and-decode
def rle_encode(img):
'''
img: numpy array, 1 - mask, 0 - background
Returns run length as string formated
