File size: 3,041 Bytes
e67f813 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
#https://www.kaggle.com/code/kmader/segmenting-buildings-in-satellite-images
from keras import models, layers
from skimage.io import imread
import numpy as np
import cv2
t_x_shape = (300, 300, 3)
GAUSSIAN_NOISE = 0.1
BASE_DEPTH = 16
BLOCK_COUNT = 1
SPATIAL_DROPOUT = 0.25
EDGE_CROP = 16
def conv_bn(x, filt, dl_rate=(1,1), preblock = False):
y = layers.Convolution2D(filt, (3, 3),
activation='linear',
padding='same',
dilation_rate=dl_rate,
use_bias=False)(x)
if preblock: return y
y = layers.BatchNormalization()(y)
return layers.Activation('elu')(y)
def keras_model():
in_layer = layers.Input(t_x_shape, name = 'RGB_Input')
pp_in_layer = layers.GaussianNoise(GAUSSIAN_NOISE)(in_layer)
pp_in_layer = layers.BatchNormalization()(pp_in_layer)
c = conv_bn(pp_in_layer, BASE_DEPTH//2)
c = conv_bn(c, BASE_DEPTH//2)
c = conv_bn(c, BASE_DEPTH)
skip_layers = [pp_in_layer]
for j in range(BLOCK_COUNT):
depth_steps = int(np.log2(t_x_shape[0])-2)
d = layers.concatenate(skip_layers+[conv_bn(c, BASE_DEPTH*2**j, (2**i, 2**i), preblock=True)
for i in range(depth_steps)])
d = layers.SpatialDropout2D(SPATIAL_DROPOUT)(d)
d = layers.BatchNormalization()(d)
d = layers.Activation('elu')(d)
# bottleneck
d = conv_bn(d, BASE_DEPTH*2**(j+1))
skip_layers += [c]
c = d
d = layers.Convolution2D(1, (1, 1), activation='sigmoid', padding='same')(d)
d = layers.Cropping2D((EDGE_CROP, EDGE_CROP))(d)
d = layers.ZeroPadding2D((EDGE_CROP, EDGE_CROP))(d)
seg_model = models.Model(inputs = [in_layer],outputs = [d])
return seg_model
def load_model(weight_path):
seg_model = keras_model()
seg_model.load_weights(weight_path)
return seg_model
def preprocess_image(img_data):
img_data = imread(img_data)
if img_data.shape[:2] == (300, 300):
img_data = img_data
else:
img_data = cv2.resize(img_data, (300, 300))
out_img = []
out_img += [img_data]
out_img = (np.stack(out_img, 0)/255.0).astype(np.float32)
return out_img
def attempt_download_from_hub(repo_id, hf_token=None):
# https://github.com/fcakyon/yolov5-pip/blob/main/yolov5/utils/downloads.py
from huggingface_hub import hf_hub_download, list_repo_files
from huggingface_hub.utils._errors import RepositoryNotFoundError
from huggingface_hub.utils._validators import HFValidationError
try:
repo_files = list_repo_files(repo_id=repo_id, repo_type='model', token=hf_token)
model_file = [f for f in repo_files if f.endswith('.h5')][0]
file = hf_hub_download(
repo_id=repo_id,
filename=model_file,
repo_type='model',
token=hf_token,
)
return file
except (RepositoryNotFoundError, HFValidationError):
return None
|