Added new dataset ghai_strawberry_fruit_detection

Author: amogh7joshi

agml/_assets/public_datasources.json

@@ -1230,5 +1230,33 @@
                 0.08992248773574829
             ]
         }
+    },
+    "ghai_strawberry_fruit_detection": {
+        "classes": {
+            "1": "Bud",
+            "2": "Calyx",
+            "3": "Detached Fruit",
+            "4": "Flower",
+            "5": "Large green",
+            "6": "Leaf",
+            "7": "Ripe fruit",
+            "8": "Small Green",
+            "9": "Stem",
+            "10": "Unripe fruit"
+        },
+        "ml_task": "object_detection",
+        "ag_task": "crop_detection",
+        "location": {
+            "continent": "north_america",
+            "country": "usa"
+        },
+        "sensor_modality": "rgb",
+        "real_synthetic": "real",
+        "platform": "handheld/ground",
+        "input_data_format": "jpg",
+        "annotation_format": "coco_json",
+        "n_images": "500",
+        "docs_url": "https://github.com/AxisAg/GHAIDatasets/blob/main/datasets/strawberry.md",
+        "external_image_sources": []
     }
 }

agml/_assets/source_citations.json

@@ -138,5 +138,9 @@
     "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}"
+    },
+    "ghai_strawberry_fruit_detection": {
+        "license": "CC BY-SA 4.0",
+        "citation": ""
     }
 }

agml/_internal/preprocess.py

@@ -1016,6 +1016,19 @@ def ghai_broccoli_detection(self, dataset_name):
         shutil.move(os.path.join(original_dir, 'coco.json'),
                     os.path.join(processed_dir, 'annotations.json'))
 
+    def ghai_strawberry_fruit_detection(self, dataset_name):
+        # Create processed directories
+        original_dir = os.path.join(self.data_original_dir, dataset_name)
+        processed_dir = os.path.join(self.data_processed_dir, dataset_name)
+        processed_image_dir = os.path.join(processed_dir, 'images')
+        os.makedirs(processed_image_dir, exist_ok = True)
+
+        # Move images
+        for image in tqdm(glob.glob(os.path.join(original_dir, '*.jpg'))):
+            shutil.move(image, processed_image_dir)
+        shutil.move(os.path.join(original_dir, 'coco.json'),
+                    os.path.join(processed_dir, 'annotations.json'))
+
 
 if __name__ == '__main__':
     # Initialize program arguments.