def main(patch_size, n_classes, model_path, output_filename, output_mapname, test_id, bands, class_weights):
# model = get_model(n_channels=4)
# test_id = 'test'
# test_id = '24'
img = normalize(tiff.imread('../data/mband/{}.tif'.format(test_id)).transpose([1, 2, 0])) # make channels last
n_channels = len(bands)
if bands[0] == -1:
n_channels = 8
print("\t num_channels", n_channels)
img = img[:, :, bands]
model = get_model(n_classes, patch_size, n_channels, class_weights)
model.load_weights(model_path)
"""
# Apply PCA to the image
pca_path = "./pca.pkl"
# open a file, where you stored the pickled data
file = open(pca_path, 'rb')
pca = pickle.load(file)
file.close()
reshaped_img_m = img.reshape((img.shape[0]*img.shape[1], img.shape[2]))
reshaped_img_pca = pca.transform(reshaped_img_m)
img_pca = reshaped_img_pca.reshape((img.shape[0], img.shape[1],
reshaped_img_pca.shape[1]))
img = img_pca
"""
mask = predict(img, model, patch_sz=patch_size, n_classes=n_classes).transpose([2,0,1]) # make channels first
map = picture_from_mask(mask, 0.5)
tiff.imsave(output_filename, (255*mask).astype('uint8'))
tiff.imsave(output_mapname, map)
4: 7,
5: 1,
6: 0,
7: 4,
8: 3
}
pict = 255*np.ones(shape=(3, mask.shape[1], mask.shape[2]), dtype=np.uint8)
for i in range(1, 9):
cl = z_order[i]
for ch in range(3):
pict[ch,:,:][mask[cl,:,:] > threshold] = colors[cl][ch]
return pict
if __name__ == '__main__':
model = get_model()
weights_path = "weights/unet_weights_30_50ep.hdf5"
model.load_weights(weights_path)
print(weights_path)
for i in range(1,15):
test_id = str(i)
print('Predicting for data/sat/{}.tif'.format(test_id))
img = normalize(tiff.imread('data/sat/{}.tif'.format(test_id)))
print(img.shape)
for i in range(15):
if i == 0: # reverse first dimension
mymat = predict(img[::-1,:,:], model, patch_sz=PATCH_SZ, n_classes=N_CLASSES).transpose([2,0,1])
#print(mymat[0][0][0], mymat[3][12][13])
# print("Case 1",img.shape, mymat.shape)
elif i == 1: # reverse second dimension
def segment_result(path):
li = [os.path.join(path, i) for i in os.listdir(path)]
model = get_model()
model.load_weights(weights_path)
count = 0
for i in li:
img = normalize(tiff.imread(i).transpose([1, 2,
0])) # make channels last
img1 = i.split("/")[-1]
# test_id = img1.split(".")[0]
print(img1)
# img = normalize(tiff.imread('data/new_test/{}.tif'.format(test_id)).transpose([1,2,0])) # make channels last
for i in range(7):
if i == 0: # reverse first dimension
mymat = predict(img[::-1, :, :],
model,
patch_sz=PATCH_SZ,
n_classes=N_CLASSES).transpose([2, 0, 1])
print("================ mat ===================")
print(mymat[0][0][0], mymat[3][12][13])
print("Case 1", img.shape, mymat.shape)
elif i == 1: # reverse second dimension
temp = predict(img[:, ::-1, :],
model,
patch_sz=PATCH_SZ,
n_classes=N_CLASSES).transpose([2, 0, 1])
print("================ mat ===================")
print(temp[0][0][0], temp[3][12][13])
print("Case 2", temp.shape, mymat.shape)
mymat = np.mean(np.array([temp[:, ::-1, :], mymat]), axis=0)
elif i == 2: # transpose(interchange) first and second dimensions
temp = predict(img.transpose([1, 0, 2]),
model,
patch_sz=PATCH_SZ,
n_classes=N_CLASSES).transpose([2, 0, 1])
print("================ mat ===================")
print(temp[0][0][0], temp[3][12][13])
print("Case 3", temp.shape, mymat.shape)
mymat = np.mean(np.array([temp.transpose(0, 2, 1), mymat]),
axis=0)
elif i == 3:
temp = predict(np.rot90(img, 1),
model,
patch_sz=PATCH_SZ,
n_classes=N_CLASSES)
print("================ mat ===================")
print(
temp.transpose([2, 0, 1])[0][0][0],
temp.transpose([2, 0, 1])[3][12][13])
print("Case 4", temp.shape, mymat.shape)
mymat = np.mean(np.array(
[np.rot90(temp, -1).transpose([2, 0, 1]), mymat]),
axis=0)
elif i == 4:
temp = predict(np.rot90(img, 2),
model,
patch_sz=PATCH_SZ,
n_classes=N_CLASSES)
print("================ mat ===================")
print(
temp.transpose([2, 0, 1])[0][0][0],
temp.transpose([2, 0, 1])[3][12][13])
print("Case 5", temp.shape, mymat.shape)
mymat = np.mean(np.array(
[np.rot90(temp, -2).transpose([2, 0, 1]), mymat]),
axis=0)
elif i == 5:
temp = predict(np.rot90(img, 3),
model,
patch_sz=PATCH_SZ,
n_classes=N_CLASSES)
print("================ mat ===================")
print(
temp.transpose([2, 0, 1])[0][0][0],
temp.transpose([2, 0, 1])[3][12][13])
print("Case 6", temp.shape, mymat.shape)
mymat = np.mean(np.array(
[np.rot90(temp, -3).transpose(2, 0, 1), mymat]),
axis=0)
else:
temp = predict(img,
model,
patch_sz=PATCH_SZ,
n_classes=N_CLASSES).transpose([2, 0, 1])
print("================ mat ===================")
print(temp[0][0][0], temp[3][12][13])
print("Case 7", temp.shape, mymat.shape)
mymat = np.mean(np.array([temp, mymat]), axis=0)
print(mymat[0][0][0], mymat[3][12][13])
map = picture_from_mask(mymat, 0.5)
mask = predict(img, model, patch_sz=PATCH_SZ,
n_classes=N_CLASSES).transpose(
[2, 0, 1]) # make channels first
# print("====================Mask=======================")
# print(mask)
# print("=================map============================")
map = picture_from_mask(mask, 0.5)
# print(map)
# tiff.imsave('data/test1/mask.tif', (255*mask).astype('uint8'))
# tiff.imsave('data/test1/result.tif', (255*mymat).astype('uint8'))
tiff.imsave('data/test_set/test_map/map.{}.tif'.format(img1), map)
count += 1