[TEST]: Pr813

deepforest/main.py

@@ -410,13 +410,14 @@ def predict_image(self,
                 result["label"] = result.label.apply(
                     lambda x: self.numeric_to_label_dict[x])
 
-        result = utilities.read_file(result)
         if path is None:
+            result = utilities.read_file(result)
             warnings.warn(
-                "An image was passed directly to predict_image, the root_dir will be None in the output dataframe, to use visualize.plot_results, please assign results.root_dir = <directory name>"
+                "An image was passed directly to predict_image, the result.root_dir attribute will be None in the output dataframe, to use visualize.plot_results, please assign results.root_dir = <directory name>"
             )
         else:
-            result.root_dir = os.path.dirname(path)
+            root_dir = getattr(result, 'root_dir', None) or os.path.dirname(path)
+            results = utilities.read_file(result, root_dir=root_dir)
 
         return result
 
@@ -556,6 +557,7 @@ def predict_tile(self,
                 lambda x: self.numeric_to_label_dict[x])
             if raster_path:
                 results["image_path"] = os.path.basename(raster_path)
+
             if return_plot:
                 # Add deprecated warning
                 warnings.warn("return_plot is deprecated and will be removed in 2.0. "
@@ -591,18 +593,19 @@ def predict_tile(self,
                                                    trainer=self.trainer,
                                                    transform=crop_transform,
                                                    augment=crop_augment)
+
         if results.empty:
             warnings.warn("No predictions made, returning None")
             return None
 
-        results = utilities.read_file(results)
-
         if raster_path is None:
             warnings.warn(
-                "An image was passed directly to predict_tile, the root_dir will be None in the output dataframe, to use visualize.plot_results, please assign results.root_dir = <directory name>"
+                "An image was passed directly to predict_tile, the results.root_dir attribute will be None in the output dataframe, to use visualize.plot_results, please assign results.root_dir = <directory name>"
             )
+            results = utilities.read_file(results)
         else:
-            results.root_dir = os.path.dirname(raster_path)
+            root_dir = getattr(results, 'root_dir', None) or os.path.dirname(raster_path)
+            results = utilities.read_file(results, root_dir=root_dir)
 
         return results
 

deepforest/predict.py

@@ -42,7 +42,9 @@ def _predict_image_(model,
 
     df = visualize.format_boxes(prediction[0])
     df = across_class_nms(df, iou_threshold=nms_thresh)
-
+    if path:
+        df["image_path"] = os.path.basename(path)
+        # df["root_dir"] = os.path.basename(path)
     if return_plot:
         # Bring to gpu
         image = image.cpu()
@@ -53,12 +55,7 @@ def _predict_image_(model,
         image = image[:, :, ::-1] * 255
         image = image.astype("uint8")
         image = visualize.plot_predictions(image, df, color=color, thickness=thickness)
-
         return image
-    else:
-        if path:
-            df["image_path"] = os.path.basename(path)
-
     return df
 
 

deepforest/utilities.py

@@ -294,9 +294,10 @@ def read_file(input, root_dir=None):
     This is the main entry point for reading annotations into deepforest.
     Args:
         input: a path to a file or a pandas dataframe
-        root_dir: Optional directory to prepend to the image_path column
+        root_dir (str): location of the image files, if not in the same directory as the annotations file
     Returns:
         df: a geopandas dataframe with the properly formatted geometry column
+        df.root_dir: the root directory of the image files
     """
     # read file
     if isinstance(input, str):
@@ -352,7 +353,13 @@ def read_file(input, root_dir=None):
     df = gpd.GeoDataFrame(df, geometry='geometry')
 
     # If root_dir is specified, add as attribute
-    df.root_dir = root_dir
+    if root_dir is not None:
+        df.root_dir = root_dir
+    else:
+        try:
+            df.root_dir = os.path.dirname(input)
+        except TypeError:
+            warnings.warn("root_dir argument for the location of images should be specified if input is not a path, returning without results.root_dir attribute", UserWarning)
 
     return df
 

deepforest/visualize.py

@@ -357,6 +357,98 @@ def convert_to_sv_format(df, width=None, height=None):
 
     return detections
 
+def __check_color__(color, num_labels):
+    if isinstance(color, list) and len(color) == 3:
+        if num_labels > 1:
+            warnings.warn(
+                """Multiple labels detected in the results and results_color argument provides a single color.
+                Each label will be plotted with a different color using a built-in color ramp.
+                If you want to customize colors with multiple labels pass a supervision.ColorPalette object to results_color with the appropriate number of labels"""
+            )
+            return sv.ColorPalette.from_matplotlib('viridis', num_labels)
+        else:
+            return sv.Color(color[0], color[1], color[2])
+    elif isinstance(color, sv.draw.color.ColorPalette):
+        if num_labels > len(color.colors):
+            warnings.warn(
+                """The number of colors provided in results_color does not match the number number of labels.
+                Replacing the provided color palette with a built-in built-in color palette.
+                To use a custom color palette make sure the number of values matches the number of labels"""
+            )
+            return sv.ColorPalette.from_matplotlib('viridis', num_labels)
+        else:
+            return color
+    elif isinstance(color, list):
+        raise ValueError(
+            "results_color must be either a 3 item list containing RGB values or an sv.ColorPalette instance"
+        )
+    else:
+        raise TypeError(
+            "results_color must be either a list of RGB values or an sv.ColorPalette instance"
+        )
+
+def plot_annotations(annotations,
+                 savedir=None,
+                 height=None,
+                 width=None,
+                 color=[245, 135, 66],
+                 thickness=2,
+                 basename=None,
+                 root_dir=None,
+                 radius=3):
+    """Plot the prediction results.
+
+    Args:
+        annotations: a pandas dataframe with prediction results
+        savedir: optional path to save the figure. If None (default), the figure will be interactively plotted.
+        height: height of the image in pixels. Required if the geometry type is 'polygon'.
+        width: width of the image in pixels. Required if the geometry type is 'polygon'.
+        results_color (list or sv.ColorPalette): color of the results annotations as a tuple of RGB color (if a single color), e.g. orange annotations is [245, 135, 66], or an supervision.ColorPalette if multiple labels and specifying colors for each label
+        thickness: thickness of the rectangle border line in px
+        basename: optional basename for the saved figure. If None (default), the basename will be extracted from the image path.
+        root_dir: optional path to the root directory of the images. If None (default), the root directory will be extracted from the annotations dataframe.root_dir attribute. 
+        radius: radius of the points in px
+    Returns:
+        None
+    """
+    # Convert colors, check for multi-class labels
+    num_labels = len(annotations.label.unique())
+    annotation_color = __check_color__(color, num_labels)
+
+    # Read images
+    if not hasattr(annotations, 'root_dir'):
+        if root_dir is None:
+            raise ValueError("The 'annotations.root_dir' attribute does not exist. Please specify the 'root_dir' argument.")
+        else:
+            root_dir = root_dir
+    else:
+        root_dir = annotations.root_dir
+
+    image_path = os.path.join(root_dir, annotations.image_path.unique()[0])
+    image = np.array(Image.open(image_path))
+
+    # Plot the results following https://supervision.roboflow.com/annotators/
+    fig, ax = plt.subplots()
+    annotated_scene = _plot_image_with_geometry(df=annotations,
+                                               image=image,
+                                               sv_color=annotation_color,
+                                               height=height,
+                                               width=width,
+                                               thickness=thickness,
+                                               radius=radius)
+
+    if savedir:
+        if basename is None:
+            basename = os.path.splitext(os.path.basename(
+                annotations.image_path.unique()[0]))[0]
+        image_name = "{}.png".format(basename)
+        image_path = os.path.join(savedir, image_name)
+        cv2.imwrite(image_path, annotated_scene)
+    else:
+        # Display the image using Matplotlib
+        plt.imshow(annotated_scene)
+        plt.axis('off')  # Hide axes for a cleaner look
+        plt.show()
 
 def plot_results(results,
                  ground_truth=None,
@@ -386,38 +478,8 @@ def plot_results(results,
     """
     # Convert colors, check for multi-class labels
     num_labels = len(results.label.unique())
-    if isinstance(results_color, list) and len(results_color) == 3:
-        if num_labels > 1:
-            warnings.warn(
-                """Multiple labels detected in the results and results_color argument provides a single color.
-                Each label will be plotted with a different color using a built-in color ramp.
-                If you want to customize colors with multiple labels pass a supervision.ColorPalette object to results_color with the appropriate number of labels"""
-            )
-            results_color_sv = sv.ColorPalette.from_matplotlib('viridis', num_labels)
-        else:
-            results_color_sv = sv.Color(results_color[0], results_color[1],
-                                        results_color[2])
-    elif isinstance(results_color, sv.draw.color.ColorPalette):
-        if num_labels > len(results_color.colors):
-            warnings.warn(
-                """The number of colors provided in results_color does not match the number number of labels.
-                Replacing the provided color palette with a built-in built-in color palette.
-                To use a custom color palette make sure the number of values matches the number of labels"""
-            )
-            results_color_sv = sv.ColorPalette.from_matplotlib('viridis', num_labels)
-        else:
-            results_color_sv = results_color
-    elif isinstance(results_color, list):
-        raise ValueError(
-            "results_color must be either a 3 item list containing RGB values or an sv.ColorPalette instance"
-        )
-    else:
-        raise TypeError(
-            "results_color must be either a list of RGB values or an sv.ColorPalette instance"
-        )
-
-    ground_truth_color_sv = sv.Color(ground_truth_color[0], ground_truth_color[1],
-                                     ground_truth_color[2])
+    results_color_sv = __check_color__(results_color, num_labels)
+    ground_truth_color_sv = __check_color__(ground_truth_color, num_labels)
 
     # Read images
     root_dir = results.root_dir
@@ -426,7 +488,7 @@ def plot_results(results,
 
     # Plot the results following https://supervision.roboflow.com/annotators/
     fig, ax = plt.subplots()
-    annotated_scene = _plot_image_with_results(df=results,
+    annotated_scene = _plot_image_with_geometry(df=results,
                                                image=image,
                                                sv_color=results_color_sv,
                                                height=height,
@@ -436,7 +498,7 @@ def plot_results(results,
 
     if ground_truth is not None:
         # Plot the ground truth annotations
-        annotated_scene = _plot_image_with_results(df=ground_truth,
+        annotated_scene = _plot_image_with_geometry(df=ground_truth,
                                                    image=annotated_scene,
                                                    sv_color=ground_truth_color_sv,
                                                    height=height,
@@ -458,7 +520,7 @@ def plot_results(results,
         plt.show()
 
 
-def _plot_image_with_results(df,
+def _plot_image_with_geometry(df,
                              image,
                              sv_color,
                              thickness=1,

docs/user_guide/01_Reading_Writing_data.md

@@ -4,7 +4,12 @@ The most time-consuming part of many open-source projects is getting the data in
 
 ## Annotation Geometries and Coordinate Systems
 
-DeepForest was originally designed for bounding box annotations. As of DeepForest 1.4.0, point and polygon annotations are also supported. There are two ways to format annotations, depending on the annotation platform you are using. `read_file` can read points, polygons, and boxes, in both image coordinate systems (relative to image origin at top-left 0,0) as well as projected coordinates on the Earth's surface.
+DeepForest was originally designed for bounding box annotations. As of DeepForest 1.4.0, point and polygon annotations are also supported. There are two ways to format annotations, depending on the annotation platform you are using. `read_file` can read points, polygons, and boxes, in both image coordinate systems (relative to image origin at top-left 0,0) as well as projected coordinates on the Earth's surface. The `read_file` method also appends the location of the current image directory as an attribute. To access this attribute use
+
+```
+filename = get_data("OSBS_029.csv")
+
+```
 
 **Note:** For CSV files, coordinates are expected to be in the image coordinate system, not projected coordinates (such as latitude/longitude or UTM).
 
@@ -28,7 +33,7 @@ OSBS_029.tif,364,204,400,246,Tree
 
 ```python
 filename = get_data("OSBS_029.csv")
-utilities.read_file(filename)
+df = utilities.read_file(filename)
 ```
 
 Example output:
@@ -44,6 +49,14 @@ Example output:
 
 **Note:** To maintain continuity with versions < 1.4.0, the function for boxes continues to output `xmin`, `ymin`, `xmax`, and `ymax` columns as individual columns as well.
 
+The location of these image files is saved in the root_dir attribute
+
+```
+df.root_dir
+'/Users/benweinstein/Documents/DeepForest/deepforest/data'
+```
+
+
 #### Shapefiles
 
 Geographic data can also be saved as shapefiles with projected coordinates.

tests/test_main.py

@@ -232,7 +232,7 @@ def test_predict_image_fromarray(m):
 
     assert isinstance(prediction, pd.DataFrame)
     assert set(prediction.columns) == {"xmin", "ymin", "xmax", "ymax", "label", "score", "geometry"} 
-
+    assert not hasattr(prediction, 'root_dir')
 
 def test_predict_big_file(m, tmpdir):
     m.config["train"]["fast_dev_run"] = False