Added etrainee_m4_utils to the python env, fixed missing figure (#43)

* Added etrainee_m4_utils

* import of our modules changed to etrainee_m4_utils

* Fixed missing fig 3

---------

Co-authored-by: jdvorak <jakub.dvorak@natur.cuni.cz>

course/assets/python_envs/etrainee_m4.yml

@@ -252,4 +252,5 @@ dependencies:
     - networkx==3.1
     - torchnet==0.0.4
     - visdom==0.2.4
+    - etrainee_m4_utils
 

course/module4/03_relating_imagery_lab_vegetation/03_02_principles_of_laboratory_spectroscopy.md

@@ -49,10 +49,10 @@ Field spectroradiometers measure reflectance at various hierarchical levels depe
 However, the reflectance measurements with CP or IS are not interchangeable. The main reason is the difference between geometry of the light source and the sensor, providing specific advantages regarding speed, laboriousness, and field operability of the spectra collection. The radiation quantity measured by the CP is usually termed in the literature as bidirectional reflectance factor (BRF) as the light source and sensor geometry is fixed in stable directional position to each other (*Figure 3A* ). The radiation quantity acquired by the IS is called directional-hemispherical reflectance factor (DHRF) for having the directional light source and having the reflected or transmitted radiation integrated from the whole hemisphere (*Figure 3B* ). There are several studies focused on the comparison of those two types of spectral quantities [Potůčková et al., 2016a](#references)), or between DHRF acquired by two types of integrating spheres ([Hovi et al., 2020](#references)).
 
 <p align="center">
-<img src="media/t03_02_fig3.jpg" title="Bidirectional and Directional-Hemispherical reflectance factors" alt="Figure 3" width="600"/>
+<img src="media/t03_02_fig3.png" title="Bidirectional and Directional-Hemispherical reflectance factors" alt="Figure 3" width="600"/>
 </p>
 
-*Figure 3. Two most commonly measured reflectance quantities A) Bidirectional Reflectance Factor by the contact probe or leaf clip and B) Directional-Hemispherical reflectance factor by the integrating sphere. (Figure modified from [Schaepman-Strub et al. 2006](https://doi.org/10.1016/j.rse.2006.03.002) / [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))*
+*Figure 3. Two most commonly measured reflectance quantities A) Bidirectional Reflectance Factor by the contact probe or leaf clip and B) Directional-Hemispherical reflectance factor by the integrating sphere. (Figure by the authors).*
 
 
 ## Spectral reflectance acquisition with the spectroradiometer ASD FieldSpec®

course/module4/04_time_series_specifics/04_exercise_cnn_1d.ipynb

@@ -70,9 +70,9 @@
     "- __os.path__ - Path manipulation\n",
     "- __tqdm__ - show progress bars during training\n",
     "\n",
-    "- __image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
-    "- __inference_utils__ - Our library for correctly exporting classifed images\n",
-    "- __visualisation_utils__ - Our library for visualising the data\n",
+    "- __etrainee_m4_utils.image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
+    "- __etrainee_m4_utils.inference_utils__ - Our library for correctly exporting classifed images\n",
+    "- __etrainee_m4_utils.visualisation_utils__ - Our library for visualising the data\n",
     "\n",
     "Two external libraries are not imported directly in this notebook, but are used by functions in _image_preprocessing_ and _inference_utils_:\n",
     "\n",
@@ -102,9 +102,9 @@
     "from sklearn.model_selection import KFold, StratifiedKFold, train_test_split\n",
     "from sklearn.metrics import classification_report\n",
     "\n",
-    "import image_preprocessing\n",
-    "import inference_utils\n",
-    "import visualisation_utils\n",
+    "from etrainee_m4_utils import image_preprocessing\n",
+    "from etrainee_m4_utils import inference_utils\n",
+    "from etrainee_m4_utils import visualisation_utils\n",
     "\n",
     "# GLOBAL SETTINGS\n",
     "plt.rcParams['figure.figsize'] = [4, 4]\n",

course/module4/04_time_series_specifics/04_exercise_cnn_2d.ipynb

@@ -66,9 +66,9 @@
     "- __os.path__ - Path manipulation\n",
     "- __tqdm__ - show progress bars during training\n",
     "\n",
-    "- __image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
-    "- __inference_utils__ - Our library for correctly exporting classifed images\n",
-    "- __visualisation_utils__ - Our library for visualising the data\n",
+    "- __etrainee_m4_utils.image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
+    "- __etrainee_m4_utils.inference_utils__ - Our library for correctly exporting classifed images\n",
+    "- __etrainee_m4_utils.visualisation_utils__ - Our library for visualising the data\n",
     "\n",
     "Two external libraries are not imported directly in this notebook, but are used by functions in _image_preprocessing_ and _inference_utils_:\n",
     "\n",
@@ -97,9 +97,9 @@
     "from sklearn.metrics import classification_report\n",
     "from sklearn.model_selection import KFold, StratifiedKFold, train_test_split\n",
     "\n",
-    "import image_preprocessing\n",
-    "import inference_utils\n",
-    "import visualisation_utils\n",
+    "from etrainee_m4_utils import image_preprocessing\n",
+    "from etrainee_m4_utils import inference_utils\n",
+    "from etrainee_m4_utils import visualisation_utils\n",
     "\n",
     "# GLOBAL SETTINGS\n",
     "plt.rcParams['figure.figsize'] = [5, 5]\n",

course/module4/04_time_series_specifics/04_exercise_cnn_3d.ipynb

@@ -66,9 +66,9 @@
     "- __os.path__ - Path manipulation\n",
     "- __tqdm__ - show progress bars during training\n",
     "\n",
-    "- __image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
-    "- __inference_utils__ - Our library for correctly exporting classifed images\n",
-    "- __visualisation_utils__ - Our library for visualising the data\n",
+    "- __etrainee_m4_utils.image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
+    "- __etrainee_m4_utils.inference_utils__ - Our library for correctly exporting classifed images\n",
+    "- __etrainee_m4_utils.visualisation_utils__ - Our library for visualising the data\n",
     "\n",
     "Two external libraries are not imported directly in this notebook, but are used by functions in _image_preprocessing_ and _inference_utils_:\n",
     "\n",
@@ -98,9 +98,9 @@
     "from sklearn.model_selection import KFold, StratifiedKFold, train_test_split\n",
     "from tqdm import notebook as tqdm\n",
     "\n",
-    "import image_preprocessing\n",
-    "import inference_utils\n",
-    "import visualisation_utils\n",
+    "from etrainee_m4_utils import image_preprocessing\n",
+    "from etrainee_m4_utils import inference_utils\n",
+    "from etrainee_m4_utils import visualisation_utils\n",
     "\n",
     "# GLOBAL SETTINGS\n",
     "plt.rcParams['figure.figsize'] = [5, 5]  \n",

course/module4/04_time_series_specifics/04_time_series_specifics_exercise_rf.ipynb

@@ -74,9 +74,9 @@
     "- __tqdm__ - show progress bars during training\n",
     "- __joblib__ - Saving and loading trained classifiers\n",
     "\n",
-    "- __image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
-    "- __inference_utils__ - Our library for correctly exporting classifed images\n",
-    "- __visualisation_utils__ - Our library for visualising the data\n",
+    "- __etrainee_m4_utils.image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
+    "- __etrainee_m4_utils.inference_utils__ - Our library for correctly exporting classifed images\n",
+    "- __etrainee_m4_utils.visualisation_utils__ - Our library for visualising the data\n",
     "\n",
     "Two external libraries are not imported directly in this notebook, but are used by functions in _image_preprocessing_ and _inference_utils_:\n",
     "\n",
@@ -106,9 +106,9 @@
     "from tqdm import notebook as tqdm\n",
     "from joblib import dump, load\n",
     "\n",
-    "import image_preprocessing\n",
-    "import inference_utils\n",
-    "import visualisation_utils\n",
+    "from etrainee_m4_utils import image_preprocessing\n",
+    "from etrainee_m4_utils import inference_utils\n",
+    "from etrainee_m4_utils import visualisation_utils\n",
     "\n",
     "# GLOBAL SETTINGS\n",
     "plt.rcParams['figure.figsize'] = [5, 5]\n",

course/module4/04_time_series_specifics/04_time_series_specifics_exercise_sam.ipynb

@@ -74,9 +74,9 @@
     "- __tqdm__ - show progress bars during training\n",
     "- __joblib__ - Saving and loading trained classifiers\n",
     "\n",
-    "- __image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
-    "- __inference_utils__ - Our library for correctly exporting classifed images\n",
-    "- __visualisation_utils__ - Our library for visualising the data\n",
+    "- __etrainee_m4_utils.image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
+    "- __etrainee_m4_utils.inference_utils__ - Our library for correctly exporting classifed images\n",
+    "- __etrainee_m4_utils.visualisation_utils__ - Our library for visualising the data\n",
     "\n",
     "Two external libraries are not imported directly in this notebook, but are used by functions in _image_preprocessing_ and _inference_utils_:\n",
     "\n",
@@ -108,9 +108,9 @@
     "from tqdm import notebook as tqdm\n",
     "from joblib import dump, load\n",
     "\n",
-    "import image_preprocessing\n",
-    "import inference_utils\n",
-    "import visualisation_utils\n",
+    "from etrainee_m4_utils import image_preprocessing\n",
+    "from etrainee_m4_utils import inference_utils\n",
+    "from etrainee_m4_utils import visualisation_utils\n",
     "\n",
     "# GLOBAL SETTINGS\n",
     "plt.rcParams['figure.figsize'] = [5, 5]\n",

course/module4/04_time_series_specifics/04_time_series_specifics_exercise_sid.ipynb

@@ -74,9 +74,9 @@
     "- __tqdm__ - show progress bars during training\n",
     "- __joblib__ - Saving and loading trained classifiers\n",
     "\n",
-    "- __image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
-    "- __inference_utils__ - Our library for correctly exporting classifed images\n",
-    "- __visualisation_utils__ - Our library for visualising the data\n",
+    "- __etrainee_m4_utils.image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
+    "- __etrainee_m4_utils.inference_utils__ - Our library for correctly exporting classifed images\n",
+    "- __etrainee_m4_utils.visualisation_utils__ - Our library for visualising the data\n",
     "\n",
     "Two external libraries are not imported directly in this notebook, but are used by functions in _image_preprocessing_ and _inference_utils_:\n",
     "\n",
@@ -108,9 +108,9 @@
     "from tqdm import notebook as tqdm\n",
     "from joblib import dump, load\n",
     "\n",
-    "import image_preprocessing\n",
-    "import inference_utils\n",
-    "import visualisation_utils\n",
+    "from etrainee_m4_utils import image_preprocessing\n",
+    "from etrainee_m4_utils import inference_utils\n",
+    "from etrainee_m4_utils import visualisation_utils\n",
     "\n",
     "# GLOBAL SETTINGS\n",
     "plt.rcParams['figure.figsize'] = [5, 5]\n",

course/module4/04_time_series_specifics/04_time_series_specifics_exercise_svm.ipynb

@@ -74,9 +74,9 @@
     "- __tqdm__ - show progress bars during training\n",
     "- __joblib__ - Saving and loading trained classifiers\n",
     "\n",
-    "- __image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
-    "- __inference_utils__ - Our library for correctly exporting classifed images\n",
-    "- __visualisation_utils__ - Our library for visualising the data\n",
+    "- __etrainee_m4_utils.image_preprocessing__ - Our library holding functions for image tiling, preprocessing, etc.\n",
+    "- __etrainee_m4_utils.inference_utils__ - Our library for correctly exporting classifed images\n",
+    "- __etrainee_m4_utils.visualisation_utils__ - Our library for visualising the data\n",
     "\n",
     "Two external libraries are not imported directly in this notebook, but are used by functions in _image_preprocessing_ and _inference_utils_:\n",
     "\n",
@@ -107,9 +107,9 @@
     "from tqdm import notebook as tqdm\n",
     "from joblib import dump, load\n",
     "\n",
-    "import image_preprocessing\n",
-    "import inference_utils\n",
-    "import visualisation_utils\n",
+    "from etrainee_m4_utils import image_preprocessing\n",
+    "from etrainee_m4_utils import inference_utils\n",
+    "from etrainee_m4_utils import visualisation_utils\n",
     "\n",
     "# GLOBAL SETTINGS\n",
     "plt.rcParams['figure.figsize'] = [5, 5]\n",