Update README.md

README.md

@@ -9,6 +9,60 @@ tags:
 - drone imagery
 - agriculture
 - in the wild
+dataset_info:
+  features:
+    - name: index
+      dtype: int64
+    - name: image
+      dtype: image
+    - name: width
+      dtype: int64
+    - name: height
+      dtype: int64
+    - name: split
+      dtype: str
+    - name: altitude
+      dtype: float64
+    - name: aperture
+      dtype: float64
+    - name: area
+      dtype: float64
+    - name: date
+      dtype: str
+    - name: device
+      dtype: str
+    - name: exposure
+      dtype: float64
+    - name: focal
+      dtype: float64
+    - name: iso
+      dtype: float64
+    - name: latitude_deg
+      dtype: float64
+    - name: latitude_dir
+      dtype: str
+    - name: longitude_deg
+      dtype: float64
+    - name: longitude_dir
+      dtype: str
+    - name: source_image_id
+      dtype: str
+    - name: time
+      dtype: str
+    - name: labels
+      sequence:
+        - name: class
+          dtype: int64
+        - name: label
+          dtype: int64
+        - name: x
+          dtype: float32
+        - name: y
+          dtype: float32
+        - name: width
+          dtype: float32
+        - name: height
+          dtype: float32
 ---
 # Wild Berry image dataset collected in Finnish forests and peatlands using drones
 
@@ -27,31 +81,36 @@ bounding boxes.
 ## How to use: an example of visualization
 ```python
 import json
+
 import numpy as np
 from datasets import load_dataset
 from PIL import Image, ImageDraw
+
 # Color map for classes
 classes_color_map = {
     0: (225,15,10),
     1: (40, 150, 210),
-    2: (10,0,210),
-    3: (130,5,125),
+    2: (10,0,210) ,
+    3: (130,5,125) ,
 }
+
 # Load the dataset
 dataset = load_dataset("FBK-TeV/WildBe", split="validation")
-#Read first image and its lables
-image_bytes = dataset[0]["image"]
+
+image_bytes = dataset[50]["image"]["bytes"]
 np_image = np.frombuffer(image_bytes, dtype=np.uint8)
-np_image = np_image.reshape(dataset[0]["image_height"], dataset[0]["image_width"], 3)
+np_image = np_image.reshape(dataset[50]["height"], dataset[50]["width"], 3)
+
 image = Image.fromarray(np_image)
-labels = json.loads(dataset[0]["labels"])
+labels = json.loads(dataset[50]["labels"])
+
 draw = ImageDraw.Draw(image)
-#Draw lables
+
 for label in labels:
-    center_x = label["x"] * dataset[0]["image_width"]
-    center_y = label["y"] * dataset[0]["image_height"]
-    width = label["width"] * dataset[0]["image_width"]
-    height = label["height"] * dataset[0]["image_height"]
+    center_x = label["x"] * dataset[50]["width"]
+    center_y = label["y"] * dataset[50]["height"]
+    width = label["width"] * dataset[50]["width"]
+    height = label["height"] * dataset[50]["height"]
     draw.rectangle(
         [
             (center_x - width / 2, center_y - height / 2),
@@ -60,6 +119,7 @@ for label in labels:
         outline=classes_color_map[label["class"]],
         width=2,
     )
+
 image.show()
 ```
 ![Teaser](resources/images_examples.png)