Updated datasets, backend choice, and added new dataset bean_synthetic_earlygrowth_aerial

Author: amogh7joshi

agml/_assets/public_datasources.json

@@ -627,8 +627,8 @@
         "ml_task": "object_detection",
         "ag_task": "fruit_detection",
         "location": {
-            "continent": "north_america",
-            "country": "usa"
+            "continent": "digital",
+            "country": "digital"
         },
         "sensor_modality": "rgb",
         "real_synthetic": "synthetic",
@@ -1198,5 +1198,25 @@
                 0.23829665780067444
             ]
         }
+    },
+    "bean_synthetic_earlygrowth_aerial": {
+        "classes": {
+            "1": "leaves",
+            "2": "branches"
+        },
+        "ml_task": "semantic_segmentation",
+        "ag_task": "plant_segmentation",
+        "location": {
+            "continent": "digital",
+            "country": "digital"
+        },
+        "sensor_modality": "rgb",
+        "real_synthetic": "synthetic",
+        "platform": "aerial",
+        "input_data_format": "jpg",
+        "annotation_format": "image",
+        "n_images": "2500",
+        "docs_url": "https://github.com/Project-AgML/AgML/blob/main/docs/datasets/bean_synthetic_earlygrowth_aerial.md",
+        "external_image_sources": []
     }
 }

agml/_assets/source_citations.json

@@ -134,5 +134,9 @@
     "ghai_broccoli_detection": {
         "license": "CC BY-SA 4.0",
         "citation": ""
+    },
+    "bean_synthetic_earlygrowth_aerial": {
+        "license": "MIT",
+        "citation": "@ARTICLE{10.3389/fpls.2019.01185,\n  \nAUTHOR={Bailey, Brian N.},   \n\t \nTITLE={Helios: A Scalable 3D Plant and Environmental Biophysical Modeling Framework},      \n\t\nJOURNAL={Frontiers in Plant Science},      \n\t\nVOLUME={10},      \n\t\nYEAR={2019},      \n\t  \nURL={https://www.frontiersin.org/article/10.3389/fpls.2019.01185},       \n\t\nDOI={10.3389/fpls.2019.01185},      \n\t\nISSN={1664-462X},   \n   \nABSTRACT={This article presents an overview of Helios, a new three-dimensional (3D) plant and environmental modeling framework. Helios is a model coupling framework designed to provide maximum flexibility in integrating and running arbitrary 3D environmental system models. Users interact with Helios through a well-documented open-source C++ API. Version 1.0 comes with model plug-ins for radiation transport, the surface energy balance, stomatal conductance, photosynthesis, solar position, and procedural tree generation. Additional plug-ins are also available for visualizing model geometry and data and for processing and integrating LiDAR scanning data. Many of the plug-ins perform calculations on the graphics processing unit, which allows for efficient simulation of very large domains with high detail. An example modeling study is presented in which leaf-level heterogeneity in water usage and photosynthesis of an orchard is examined to understand how this leaf-scale variability contributes to whole-tree and -canopy fluxes.}\n}"
     }
 }

agml/backend/tftorch.py

@@ -67,7 +67,7 @@ def _check_tf_torch():
 
 
 # Default backend is PyTorch.
-_BACKEND = 'torch'
+_BACKEND = None
 _USER_SET_BACKEND = False
 
 

requirements.txt

@@ -2,6 +2,7 @@ numpy
 matplotlib
 tqdm
 scikit-learn
+requests>=2.0.0
 opencv-python; sys.platform != 'linux'
 opencv-python-headless; sys.platform == 'linux'
 pyyaml>=5.4.1

scripts/generate_dataset_markdown.py

@@ -116,21 +116,8 @@ def build_table(json):
 def generate_example_images(name):
     """Generates the example images for the given dataset."""
     agml.backend.set_seed(189)
-    info = agml.data.source(name)
     loader = agml.data.AgMLDataLoader(name)
-    if info.tasks.ml == 'image_classification':
-        return agml.viz.visualize_images_with_labels(
-            loader[:NUM_EXAMPLES], info = info, show = False)
-    elif info.tasks.ml == 'object_detection':
-        return agml.viz.visualize_images([
-            agml.viz.annotate_bboxes_on_image(i, show = False)
-            for i in loader[:NUM_EXAMPLES]])
-    elif info.tasks.ml == 'semantic_segmentation':
-        return agml.viz.visualize_images([
-            agml.viz.visualize_overlaid_masks(i)
-            for i in loader[:NUM_EXAMPLES]], show = False)
-    else:
-        raise ValueError(f'Unknown ML task: {info.tasks.ml}')
+    return agml.viz.show_sample(loader, no_show = True)
 
 
 def build_examples(name):
@@ -140,7 +127,7 @@ def build_examples(name):
         LOCAL_AGML_REPO, 'docs/sample_images', f'{name}_examples.png')
     save_path_remote = os.path.join(
         AGML_REPO, 'docs/sample_images', f'{name}_examples.png')
-    sample.savefig(save_path_local)
+    cv2.imwrite(save_path_local, sample)
     return f'![Example Images for {name}]({save_path_remote})'