def train_RS():
george_points_tif = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/shp/Training_points_clip_dropTransition_add.tif"
# training_tif = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/all_19bands_stack.tif"
# outmodel = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/george_data_19bands_add.pkl"
# training_summary = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/george_data_19bands_add.csv"
# band_labels = ["ndvi", "ci", "psri", "gndvi", "s2_rep", "ireci", "s2_b", "s2_g",
# "s2_r", "s2_nir", "hv", "vv","s2_20m_1","s2_20m_2","s2_20m_3","s2_20m_4","s2_20m_5","s2_20m_6","seg"]
#bands = 19
training_tif = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/all_13bands_stack_v2.tif"
outmodel = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/george_data_13bands_add_255_eRF_v2.pkl"
training_summary = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/george_data_13bands_add_255_v2.csv"
band_labels = [
"ndvi", "ci", "psri", "gndvi", "s2_rep", "ireci", "s2_b", "s2_g",
"s2_r", "s2_nir", "hv", "vv", "seg"
]
bands = 13
features, labels = PYEO_model.get_training_data_tif(
image_path=training_tif, training_tif_path=george_points_tif)
features_df = pd.DataFrame(data=features,
columns=band_labels).astype(np.int32)
scale = preprocessing.MinMaxScaler(feature_range=(0, 255)).fit(features)
features_trans = scale.transform(features)
features_trans_df = pd.DataFrame(features_trans,
index=features_df.index,
columns=features_df.columns)
features_df.describe()
features_trans_df.describe()
labels_df = pd.DataFrame(data=labels,
columns=['class_name']).astype(np.int)
features_df["class_name"] = labels_df["class_name"]
features_df.describe().to_csv(training_summary)
#PYEO_model.train_rs_simple(X_train_input=features_trans,y_train_input=labels,outModel_path=outmodel, bands = bands)
PYEO_model.train_eRF_gs_simple(X_train_input=features_trans,
y_train_input=labels,
outModel_path=outmodel,
bands=bands)
image_tobe_classified = training_tif
PYEO_model.classify_image(model_path=outmodel,
in_image_path=image_tobe_classified,
out_image_path=image_tobe_classified[:-4] +
"_classified_13bands_255_eRF2_v2.tif",
rescale_predict_image=scale)
image_tobe_classified = "/media/ubuntu/Data/Ghana/north_region/s2_NWN/images/stacked/with_s1_seg/composite_20180122T102321_T30NWN.tif"
PYEO_model.classify_image(model_path=outmodel,
in_image_path=image_tobe_classified,
out_image_path=image_tobe_classified[:-4] +
"_classified_13bands_255_eRF2_v2.tif",
rescale_predict_image=scale)
def test_histmatching_classify():
training_tif = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/all_13bands_stack_v2.tif"
george_points_tif = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/shp/Training_points_clip_dropTransition_add.tif"
input_model = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/george_data_13bands_add_255_eRF_v2.pkl"
image_tobe_classified = "/media/ubuntu/Data/Ghana/north_region/s2/images/stacked/with_s1_seg/composite_20180122T102321_T30NWN.tif"
features, labels = PYEO_model.get_training_data_tif(
image_path=training_tif, training_tif_path=george_points_tif)
scale = preprocessing.MinMaxScaler(feature_range=(0, 255)).fit(features)
PYEO_model.classify_image(model_path=input_model,
in_image_path=image_tobe_classified,
out_image_path=image_tobe_classified[:-4] +
"_classified_13bands_255_eRF2_v4.tif",
rescale_predict_image=scale,
ref_img_for_linear_shift=training_tif,
generate_mask=False)
def do_classify(working_dir):
training_tif = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/all_13bands_stack_v2.tif"
george_points_tif = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/shp/Training_points_clip_dropTransition_add.tif"
input_model = "/media/ubuntu/Data/Ghana/cocoa_upscale_test/george_data_13bands_add_255_eRF_v2.pkl"
features, labels = PYEO_model.get_training_data_tif(
image_path=training_tif, training_tif_path=george_points_tif)
scale = preprocessing.MinMaxScaler(feature_range=(0, 255)).fit(features)
os.chdir(working_dir)
for image in os.listdir("images/stacked/with_s1_seg"):
if image.endswith(".tif"):
image_tobe_classified = os.path.join("images/stacked/with_s1_seg",
image)
out_image_path = os.path.join("output/",
image[:-4] + "_13bands_255_eRF.tif")
PYEO_model.classify_image(model_path=input_model,
in_image_path=image_tobe_classified,
out_image_path=out_image_path,
rescale_predict_image=scale,
ref_img_for_linear_shift=training_tif,
generate_mask=False)