Initial Commit
Browse files- .gitignore +4 -0
- README.md +2 -3
- app.py +397 -0
- requirements.txt +4 -0
- utils.py +22 -0
.gitignore
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data/
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flagged/
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**/__pycache__/
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venv/
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README.md
CHANGED
@@ -4,10 +4,9 @@ emoji: 🚀
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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-
sdk_version:
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app_file: app.py
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pinned: false
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license: mit
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---
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-
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-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: 3.21.0
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python_version: 3.8
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app_file: app.py
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pinned: false
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license: mit
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---
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app.py
ADDED
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1 |
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import os
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import shutil
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import tempfile
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import gradio as gr
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import plotly.graph_objects as go
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import pandas as pd
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from time import time
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from utils import (
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create_file_structure,
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11 |
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init_info_csv,
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add_to_info_csv,
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)
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from satseg.dataset import create_datasets, create_inference_dataset
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from satseg.model import train_model, save_model, run_inference, load_model
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from satseg.seg_result import combine_seg_maps, get_combined_map_contours
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from satseg.geo_tools import (
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shapefile_to_latlong,
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shapefile_to_grid_indices,
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points_to_shapefile,
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contours_to_shapefile,
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get_tif_n_channels,
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)
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DATA_DIR = "data"
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MODEL_DIR = os.path.join(DATA_DIR, "models")
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TIF_DIR = os.path.join(DATA_DIR, "tifs")
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MASK_DIR = os.path.join(DATA_DIR, "masks")
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INFO_DIR = os.path.join(DATA_DIR, "info")
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MODEL_INFO_PATH = os.path.join(INFO_DIR, "model_data.csv")
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DATASET_TIF_INFO_PATH = os.path.join(INFO_DIR, "dataset_tif_data.csv")
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DATASET_MASK_INFO_PATH = os.path.join(INFO_DIR, "dataset_mask_data.csv")
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35 |
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create_file_structure(
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[DATA_DIR, TIF_DIR, MASK_DIR, INFO_DIR],
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[MODEL_INFO_PATH, DATASET_TIF_INFO_PATH, DATASET_MASK_INFO_PATH],
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)
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init_info_csv(
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MODEL_INFO_PATH,
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[
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"Name",
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"Architecture",
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"# of channels",
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"Train TIF",
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"Train Mask",
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"Expression",
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"Path",
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],
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)
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init_info_csv(DATASET_TIF_INFO_PATH, ["Name", "# of channels", "Path"])
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init_info_csv(DATASET_MASK_INFO_PATH, ["Name", "Class", "Path"])
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def gr_train_model(
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tif_names, mask_names, model_name, expression, progress=gr.Progress()
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):
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tif_paths = list(map(lambda x: os.path.join(TIF_DIR, x), tif_names))
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mask_paths = list(map(lambda x: os.path.join(MASK_DIR, x), mask_names))
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61 |
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expression = expression.strip().split()
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# if arch.lower() == "best":
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# arch = "dcama" if len(train_set) > 8 and len(train_set) < 20 else "unet"
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# ( c6 - c0 ) / ( c6 + c0 ) =
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progress(0, desc="Creating Dataset...")
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with tempfile.TemporaryDirectory() as tempdir:
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train_set, val_set = create_datasets(
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tif_paths, mask_paths, tempdir, expression=expression
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)
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progress(0.05, desc="Training Model...")
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model, _ = train_model(train_set, val_set, "unet")
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progress(0.95, desc="Model Trained! Saving...")
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model_name = "_".join(model_name.split()) + ".pt"
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76 |
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model_path = os.path.join(MODEL_DIR, model_name)
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save_model(model, model_path)
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add_to_info_csv(
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MODEL_INFO_PATH,
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80 |
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[
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model_name,
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82 |
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"UNet",
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83 |
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val_set.n_channels,
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84 |
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";".join(tif_names),
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85 |
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";".join(mask_names),
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86 |
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" ".join(expression),
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model_path,
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88 |
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],
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89 |
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)
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90 |
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progress(1.0, desc="Done!")
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91 |
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model_df = pd.read_csv(MODEL_INFO_PATH)
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92 |
+
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93 |
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return "Done!", model_df, gr.Dropdown.update(choices=model_df["Name"].to_list())
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94 |
+
|
95 |
+
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96 |
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def gr_run_inference(tif_names, model_name, progress=gr.Progress()):
|
97 |
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t = time()
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98 |
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tif_paths = list(map(lambda x: os.path.join(TIF_DIR, x), tif_names))
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99 |
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model_df = pd.read_csv(MODEL_INFO_PATH, index_col="Name")
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100 |
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model_path = model_df["Path"][model_name]
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101 |
+
|
102 |
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with tempfile.TemporaryDirectory() as tempdir:
|
103 |
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progress(0, desc="Creating Dataset...")
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104 |
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dataset = create_inference_dataset(
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105 |
+
tif_paths,
|
106 |
+
tempdir,
|
107 |
+
256,
|
108 |
+
expression=model_df["Expression"][model_name].split(),
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109 |
+
)
|
110 |
+
progress(0.1, desc="Loading Model...")
|
111 |
+
model = load_model(model_path)
|
112 |
+
|
113 |
+
result_dir = os.path.join(tempdir, "infer")
|
114 |
+
comb_result_dir = os.path.join(tempdir, "comb")
|
115 |
+
os.makedirs(result_dir)
|
116 |
+
os.makedirs(comb_result_dir)
|
117 |
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progress(0.2, desc="Running Inference...")
|
118 |
+
run_inference(dataset, model, result_dir)
|
119 |
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progress(0.8, desc="Preparing output...")
|
120 |
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combine_seg_maps(result_dir, comb_result_dir)
|
121 |
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results = get_combined_map_contours(comb_result_dir)
|
122 |
+
|
123 |
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file_paths = []
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124 |
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out_dir = os.path.join(MASK_DIR, "output")
|
125 |
+
if os.path.exists(out_dir):
|
126 |
+
shutil.rmtree(out_dir)
|
127 |
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os.makedirs(out_dir)
|
128 |
+
for tif_name, (contours, hierarchy) in results.items():
|
129 |
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tif_path = os.path.join(TIF_DIR, f"{tif_name}.tif")
|
130 |
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mask_path = os.path.join(out_dir, f"{tif_name}_mask.shp")
|
131 |
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zip_path = contours_to_shapefile(contours, hierarchy, tif_path, mask_path)
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132 |
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file_paths.append(zip_path)
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133 |
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print(time() - t, "seconds")
|
134 |
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return file_paths
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135 |
+
|
136 |
+
|
137 |
+
def gr_save_mask_file(file_objs, filenames, obj_class):
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138 |
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print("Saving file(s)...")
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139 |
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idx = 0
|
140 |
+
for filename in filenames.split(";"):
|
141 |
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if filename.strip() == "":
|
142 |
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continue
|
143 |
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|
144 |
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filepath = os.path.join(MASK_DIR, filename.strip())
|
145 |
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obj = file_objs[idx]
|
146 |
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idx += 1
|
147 |
+
|
148 |
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shutil.move(obj.name, filepath)
|
149 |
+
if filename.endswith(".shp"):
|
150 |
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add_to_info_csv(DATASET_MASK_INFO_PATH, [filename, obj_class, filepath])
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151 |
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print("Done!")
|
152 |
+
|
153 |
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dataset_df = pd.read_csv(DATASET_MASK_INFO_PATH)
|
154 |
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choices = dataset_mask_df["Name"].to_list()
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155 |
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update = gr.Dropdown.update(choices=choices)
|
156 |
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157 |
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return dataset_df, update, update
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158 |
+
|
159 |
+
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160 |
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def gr_save_tif_file(file_objs, filenames):
|
161 |
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print("Saving file(s)...")
|
162 |
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idx = 0
|
163 |
+
for filename in filenames.split(";"):
|
164 |
+
if filename.strip() == "":
|
165 |
+
continue
|
166 |
+
|
167 |
+
filepath = os.path.join(TIF_DIR, filename.strip())
|
168 |
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obj = file_objs[idx]
|
169 |
+
idx += 1
|
170 |
+
|
171 |
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shutil.copy2(obj.name, filepath)
|
172 |
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n = get_tif_n_channels(filepath)
|
173 |
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add_to_info_csv(DATASET_TIF_INFO_PATH, [filename, n, filepath])
|
174 |
+
print("Done!")
|
175 |
+
|
176 |
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dataset_df = pd.read_csv(DATASET_TIF_INFO_PATH)
|
177 |
+
choices = dataset_mask_df["Name"].to_list()
|
178 |
+
update = gr.Dropdown.update(choices=choices)
|
179 |
+
|
180 |
+
return dataset_df, update, update
|
181 |
+
|
182 |
+
|
183 |
+
def gr_generate_map(mask_name: str, token: str = "", show_grid=True, show_mask=False):
|
184 |
+
mask_path = os.path.join(MASK_DIR, mask_name)
|
185 |
+
# token = "pk.eyJ1IjoiZGlsaXRoIiwiYSI6ImNsaDQ3NXF3ZDAxdDMzZXMxeWJic2h1cDQifQ.DDczQCDfTgQEUt6pGvjUAg"
|
186 |
+
center = (7.753769, 80.691730)
|
187 |
+
|
188 |
+
scattermaps = []
|
189 |
+
if show_grid:
|
190 |
+
indices = shapefile_to_grid_indices(mask_path)
|
191 |
+
points_to_shapefile(indices, mask_path[: -len(".shp")] + "-grid.shp")
|
192 |
+
scattermaps.append(
|
193 |
+
go.Scattermapbox(
|
194 |
+
lat=indices[:, 1],
|
195 |
+
lon=indices[:, 0],
|
196 |
+
mode="markers",
|
197 |
+
marker=go.scattermapbox.Marker(size=6),
|
198 |
+
)
|
199 |
+
)
|
200 |
+
if show_mask:
|
201 |
+
contours = shapefile_to_latlong(mask_path)
|
202 |
+
for contour in contours[38:39]:
|
203 |
+
lons = contour[:, 0]
|
204 |
+
lats = contour[:, 1]
|
205 |
+
scattermaps.append(
|
206 |
+
go.Scattermapbox(
|
207 |
+
fill="toself",
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208 |
+
lat=lats,
|
209 |
+
lon=lons,
|
210 |
+
mode="markers",
|
211 |
+
marker=go.scattermapbox.Marker(size=6),
|
212 |
+
)
|
213 |
+
)
|
214 |
+
|
215 |
+
fig = go.Figure(scattermaps)
|
216 |
+
|
217 |
+
if token:
|
218 |
+
fig.update_layout(
|
219 |
+
mapbox=dict(
|
220 |
+
style="satellite-streets",
|
221 |
+
accesstoken=token,
|
222 |
+
center=go.layout.mapbox.Center(lat=center[0], lon=center[1]),
|
223 |
+
pitch=0,
|
224 |
+
zoom=7,
|
225 |
+
),
|
226 |
+
mapbox_layers=[
|
227 |
+
{
|
228 |
+
# "below": "traces",
|
229 |
+
"sourcetype": "raster",
|
230 |
+
"sourceattribution": "United States Geological Survey",
|
231 |
+
"source": [
|
232 |
+
"https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryOnly/MapServer/tile/{z}/{y}/{x}"
|
233 |
+
],
|
234 |
+
}
|
235 |
+
],
|
236 |
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)
|
237 |
+
else:
|
238 |
+
fig.update_layout(
|
239 |
+
mapbox_style="open-street-map",
|
240 |
+
hovermode="closest",
|
241 |
+
mapbox=dict(
|
242 |
+
bearing=0,
|
243 |
+
center=go.layout.mapbox.Center(lat=center[0], lon=center[1]),
|
244 |
+
pitch=0,
|
245 |
+
zoom=7,
|
246 |
+
),
|
247 |
+
)
|
248 |
+
|
249 |
+
return fig
|
250 |
+
|
251 |
+
|
252 |
+
with gr.Blocks() as demo:
|
253 |
+
gr.Markdown(
|
254 |
+
"""# SatSeg
|
255 |
+
Train models and run inference for segmentation of multispectral satellite images."""
|
256 |
+
)
|
257 |
+
|
258 |
+
model_df = pd.read_csv(MODEL_INFO_PATH)
|
259 |
+
dataset_tif_df = pd.read_csv(DATASET_TIF_INFO_PATH)
|
260 |
+
dataset_mask_df = pd.read_csv(DATASET_MASK_INFO_PATH)
|
261 |
+
|
262 |
+
with gr.Tab("Train"):
|
263 |
+
train_tif_names = gr.Dropdown(
|
264 |
+
label="TIF Files",
|
265 |
+
choices=dataset_tif_df["Name"].to_list(),
|
266 |
+
multiselect=True,
|
267 |
+
)
|
268 |
+
train_mask_names = gr.Dropdown(
|
269 |
+
label="Mask files",
|
270 |
+
choices=dataset_mask_df["Name"].to_list(),
|
271 |
+
multiselect=True,
|
272 |
+
)
|
273 |
+
train_rs_index = gr.Textbox(
|
274 |
+
label="Remote Sensing Index", placeholder="( c0 + c1 ) / ( c0 - c1 ) ="
|
275 |
+
)
|
276 |
+
# train_arch = gr.Dropdown(
|
277 |
+
# label="Model Architecture", choices=["Best", "UNet", "DCAMA"], value="Best"
|
278 |
+
# )
|
279 |
+
train_model_name = gr.Textbox(
|
280 |
+
label="Model Name", placeholder="Give the model a name"
|
281 |
+
)
|
282 |
+
train_button = gr.Button("Train")
|
283 |
+
|
284 |
+
train_completion = gr.Text(label="Training Status", value="Not Started")
|
285 |
+
|
286 |
+
with gr.Tab("Infer"):
|
287 |
+
infer_tif_names = gr.Dropdown(
|
288 |
+
label="TIF Files",
|
289 |
+
choices=dataset_tif_df["Name"].to_list(),
|
290 |
+
multiselect=True,
|
291 |
+
)
|
292 |
+
infer_model_name = gr.Dropdown(
|
293 |
+
label="Model Name",
|
294 |
+
choices=model_df["Name"].to_list(),
|
295 |
+
)
|
296 |
+
infer_button = gr.Button("Infer")
|
297 |
+
|
298 |
+
infer_mask = gr.Files(label="Output Shapefile", interactive=False)
|
299 |
+
|
300 |
+
# with gr.Tab("Sampling"):
|
301 |
+
# grid_mask_name = gr.Dropdown(
|
302 |
+
# label="Mask",
|
303 |
+
# choices=dataset_mask_df["Name"].to_list(),
|
304 |
+
# )
|
305 |
+
|
306 |
+
# grid_token = gr.Textbox(
|
307 |
+
# value="", label="Mapbox Token (https://account.mapbox.com/)"
|
308 |
+
# )
|
309 |
+
# grid_side_len = gr.Textbox(value="100", label="Sampling Gap (m)")
|
310 |
+
|
311 |
+
# grid_show_grid = gr.Checkbox(True, label="Show Grid")
|
312 |
+
# grid_show_mask = gr.Checkbox(False, label="Show Mask")
|
313 |
+
|
314 |
+
# grid_button = gr.Button("Generate Grid")
|
315 |
+
|
316 |
+
# grid_map = gr.Plot(label="Plot")
|
317 |
+
|
318 |
+
with gr.Tab("Datasets"):
|
319 |
+
dataset_tif_df = pd.read_csv(DATASET_TIF_INFO_PATH)
|
320 |
+
dataset_mask_df = pd.read_csv(DATASET_MASK_INFO_PATH)
|
321 |
+
|
322 |
+
datasets_upload_tif = gr.File(label="Images (.tif)", file_count="multiple")
|
323 |
+
datasets_upload_tif_name = gr.Textbox(
|
324 |
+
label="TIF name", placeholder="tif_file_1.tif;tif_file_2.tif"
|
325 |
+
)
|
326 |
+
datasets_save_uploaded_tif = gr.Button("Save")
|
327 |
+
|
328 |
+
datasets_upload_mask = gr.File(
|
329 |
+
label="Masks (Please upload all extensions (.shp, .shx, etc.))",
|
330 |
+
file_count="multiple",
|
331 |
+
)
|
332 |
+
datasets_upload_mask_name = gr.Textbox(
|
333 |
+
label="Mask name", placeholder="mask_1.shp;mask_1.shx"
|
334 |
+
)
|
335 |
+
datasets_mask_class_name = gr.Textbox(
|
336 |
+
label="Class (The name of the object you want to segment)"
|
337 |
+
)
|
338 |
+
datasets_save_uploaded_mask = gr.Button("Save")
|
339 |
+
|
340 |
+
datasets_tif_table = gr.Dataframe(dataset_tif_df, label="TIFs")
|
341 |
+
datasets_mask_table = gr.Dataframe(dataset_mask_df, label="Masks")
|
342 |
+
|
343 |
+
with gr.Tab("Models"):
|
344 |
+
models_table = gr.Dataframe(model_df)
|
345 |
+
|
346 |
+
train_button.click(
|
347 |
+
gr_train_model,
|
348 |
+
inputs=[
|
349 |
+
train_tif_names,
|
350 |
+
train_mask_names,
|
351 |
+
# train_arch,
|
352 |
+
train_model_name,
|
353 |
+
train_rs_index,
|
354 |
+
],
|
355 |
+
outputs=[train_completion, models_table, infer_model_name],
|
356 |
+
)
|
357 |
+
|
358 |
+
infer_button.click(
|
359 |
+
gr_run_inference,
|
360 |
+
inputs=[infer_tif_names, infer_model_name],
|
361 |
+
outputs=[infer_mask],
|
362 |
+
)
|
363 |
+
|
364 |
+
datasets_upload_tif.upload(
|
365 |
+
lambda y: ";".join(list(map(lambda x: os.path.basename(x.orig_name), y))),
|
366 |
+
inputs=datasets_upload_tif,
|
367 |
+
outputs=datasets_upload_tif_name,
|
368 |
+
)
|
369 |
+
|
370 |
+
datasets_upload_mask.upload(
|
371 |
+
lambda y: ";".join(list(map(lambda x: os.path.basename(x.orig_name), y))),
|
372 |
+
inputs=datasets_upload_mask,
|
373 |
+
outputs=datasets_upload_mask_name,
|
374 |
+
)
|
375 |
+
|
376 |
+
# grid_button.click(
|
377 |
+
# gr_generate_map,
|
378 |
+
# inputs=[grid_mask_name, grid_token, grid_show_grid, grid_show_mask],
|
379 |
+
# outputs=grid_map,
|
380 |
+
# )
|
381 |
+
|
382 |
+
datasets_save_uploaded_tif.click(
|
383 |
+
gr_save_tif_file,
|
384 |
+
inputs=[datasets_upload_tif, datasets_upload_tif_name],
|
385 |
+
outputs=[datasets_tif_table, train_tif_names, infer_tif_names],
|
386 |
+
)
|
387 |
+
datasets_save_uploaded_mask.click(
|
388 |
+
gr_save_mask_file,
|
389 |
+
inputs=[
|
390 |
+
datasets_upload_mask,
|
391 |
+
datasets_upload_mask_name,
|
392 |
+
datasets_mask_class_name,
|
393 |
+
],
|
394 |
+
outputs=[datasets_mask_table, train_mask_names],
|
395 |
+
)
|
396 |
+
|
397 |
+
demo.queue(concurrency_count=10).launch(debug=True)
|
requirements.txt
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gradio==3.21.0
|
2 |
+
pandas==2.0.0
|
3 |
+
plotly==5.13.1
|
4 |
+
satseg==0.1.1
|
utils.py
ADDED
@@ -0,0 +1,22 @@
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
from typing import List
|
3 |
+
|
4 |
+
|
5 |
+
def init_info_csv(data_info_path: str, header: List[str]):
|
6 |
+
with open(data_info_path, "r") as fp:
|
7 |
+
if not fp.read().strip():
|
8 |
+
add_to_info_csv(data_info_path, header)
|
9 |
+
|
10 |
+
|
11 |
+
def add_to_info_csv(data_info_path: str, info: List[str]):
|
12 |
+
with open(data_info_path, "a") as fp:
|
13 |
+
fp.write(",".join(list(map(str, info))) + "\n")
|
14 |
+
|
15 |
+
|
16 |
+
def create_file_structure(dirs: List[str], files: List[str]):
|
17 |
+
for dir_path in dirs:
|
18 |
+
os.makedirs(dir_path, exist_ok=True)
|
19 |
+
|
20 |
+
for file_path in files:
|
21 |
+
with open(file_path, "a"):
|
22 |
+
pass
|