[image-processing] deprecate plot_keypoint_matching, make visualize_keypoint_matching as a standard (#39830)

* fix: deprecate plot_keypoint_matching and make visualize_keypoint_matching for all Keypoint Matching models

* refactor: added copied from

* fix: make style

* fix: repo consistency

* fix: make style

* docs: added missing method in SuperGlue docs

docs/source/en/model_doc/lightglue.md

@@ -107,7 +107,7 @@ processed_outputs = processor.post_process_keypoint_matching(outputs, image_size
 
     ```py
     # Easy visualization using the built-in plotting method
-    processor.plot_keypoint_matching(images, processed_outputs)
+    processor.visualize_keypoint_matching(images, processed_outputs)
     ```
 
 <div class="flex justify-center">
@@ -128,7 +128,7 @@ processed_outputs = processor.post_process_keypoint_matching(outputs, image_size
 
 - preprocess
 - post_process_keypoint_matching
-- plot_keypoint_matching
+- visualize_keypoint_matching
 
 <frameworkcontent>
 <pt>

docs/source/en/model_doc/superglue.md

@@ -103,38 +103,11 @@ processed_outputs = processor.post_process_keypoint_matching(outputs, image_size
             print(f"Keypoint at {keypoint0.numpy()} matches with keypoint at {keypoint1.numpy()} with score {matching_score}")
     ```
 
-- The example below demonstrates how to visualize matches between two images.
+- Visualize the matches between the images using the built-in plotting functionality.
 
     ```py
-    import matplotlib.pyplot as plt
+    # Easy visualization using the built-in plotting method
-    import numpy as np
+    processor.visualize_keypoint_matching(images, processed_outputs)
-
-    # Create side by side image
-    merged_image = np.zeros((max(image1.height, image2.height), image1.width + image2.width, 3))
-    merged_image[: image1.height, : image1.width] = np.array(image1) / 255.0
-    merged_image[: image2.height, image1.width :] = np.array(image2) / 255.0
-    plt.imshow(merged_image)
-    plt.axis("off")
-
-    # Retrieve the keypoints and matches
-    output = processed_outputs[0]
-    keypoints0 = output["keypoints0"]
-    keypoints1 = output["keypoints1"]
-    matching_scores = output["matching_scores"]
-
-    # Plot the matches
-    for keypoint0, keypoint1, matching_score in zip(keypoints0, keypoints1, matching_scores):
-        plt.plot(
-            [keypoint0[0], keypoint1[0] + image1.width],
-            [keypoint0[1], keypoint1[1]],
-            color=plt.get_cmap("RdYlGn")(matching_score.item()),
-            alpha=0.9,
-            linewidth=0.5,
-        )
-        plt.scatter(keypoint0[0], keypoint0[1], c="black", s=2)
-        plt.scatter(keypoint1[0] + image1.width, keypoint1[1], c="black", s=2)
-
-    plt.savefig("matched_image.png", dpi=300, bbox_inches='tight')
     ```
 
 <div class="flex justify-center">
@@ -155,6 +128,7 @@ processed_outputs = processor.post_process_keypoint_matching(outputs, image_size
 
 - preprocess
 - post_process_keypoint_matching
+- visualize_keypoint_matching
 
 <frameworkcontent>
 <pt>

src/transformers/models/efficientloftr/image_processing_efficientloftr.py

@@ -408,7 +408,7 @@ class EfficientLoFTRImageProcessor(BaseImageProcessor):
             images (`ImageInput`):
                 Image pairs to plot. Same as `EfficientLoFTRImageProcessor.preprocess`. Expects either a list of 2
                 images or a list of list of 2 images list with pixel values ranging from 0 to 255.
-            outputs (List[Dict[str, torch.Tensor]]]):
+            keypoint_matching_output (List[Dict[str, torch.Tensor]]]):
                 A post processed keypoint matching output
 
         Returns:

src/transformers/models/lightglue/image_processing_lightglue.py

@@ -17,6 +17,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import warnings
 from typing import Optional, Union
 
 import numpy as np
@@ -44,6 +45,9 @@ from ...utils.import_utils import requires
 from .modeling_lightglue import LightGlueKeypointMatchingOutput
 
 
+if is_vision_available():
+    from PIL import Image, ImageDraw
+
 if is_vision_available():
     import PIL
 
@@ -402,18 +406,88 @@ class LightGlueImageProcessor(BaseImageProcessor):
 
         return results
 
+    def visualize_keypoint_matching(
+        self,
+        images: ImageInput,
+        keypoint_matching_output: list[dict[str, torch.Tensor]],
+    ) -> list["Image.Image"]:
+        """
+        Plots the image pairs side by side with the detected keypoints as well as the matching between them.
+
+        Args:
+            images (`ImageInput`):
+                Image pairs to plot. Same as `LightGlueImageProcessor.preprocess`. Expects either a list of 2
+                images or a list of list of 2 images list with pixel values ranging from 0 to 255.
+            keypoint_matching_output (List[Dict[str, torch.Tensor]]]):
+                A post processed keypoint matching output
+
+        Returns:
+            `List[PIL.Image.Image]`: A list of PIL images, each containing the image pairs side by side with the detected
+            keypoints as well as the matching between them.
+        """
+        images = validate_and_format_image_pairs(images)
+        images = [to_numpy_array(image) for image in images]
+        image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]
+
+        results = []
+        for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
+            height0, width0 = image_pair[0].shape[:2]
+            height1, width1 = image_pair[1].shape[:2]
+            plot_image = np.zeros((max(height0, height1), width0 + width1, 3), dtype=np.uint8)
+            plot_image[:height0, :width0] = image_pair[0]
+            plot_image[:height1, width0:] = image_pair[1]
+
+            plot_image_pil = Image.fromarray(plot_image)
+            draw = ImageDraw.Draw(plot_image_pil)
+
+            keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
+            keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
+            for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
+                keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
+            ):
+                color = self._get_color(matching_score)
+                draw.line(
+                    (keypoint0_x, keypoint0_y, keypoint1_x + width0, keypoint1_y),
+                    fill=color,
+                    width=3,
+                )
+                draw.ellipse((keypoint0_x - 2, keypoint0_y - 2, keypoint0_x + 2, keypoint0_y + 2), fill="black")
+                draw.ellipse(
+                    (keypoint1_x + width0 - 2, keypoint1_y - 2, keypoint1_x + width0 + 2, keypoint1_y + 2),
+                    fill="black",
+                )
+
+            results.append(plot_image_pil)
+        return results
+
+    def _get_color(self, score):
+        """Maps a score to a color."""
+        r = int(255 * (1 - score))
+        g = int(255 * score)
+        b = 0
+        return (r, g, b)
+
     def plot_keypoint_matching(self, images: ImageInput, keypoint_matching_output: LightGlueKeypointMatchingOutput):
         """
         Plots the image pairs side by side with the detected keypoints as well as the matching between them. Requires
         matplotlib to be installed.
 
+        .. deprecated::
+            `plot_keypoint_matching` is deprecated and will be removed in a future version. Use `visualize_keypoint_matching` instead.
+
         Args:
             images (`ImageInput`):
                 Image pairs to plot. Same as `LightGlueImageProcessor.preprocess`. Expects either a list of 2 images or
                 a list of list of 2 images list with pixel values ranging from 0 to 255.
-            outputs ([`LightGlueKeypointMatchingOutput`]):
+            keypoint_matching_output ([`LightGlueKeypointMatchingOutput`]):
                 Raw outputs of the model.
         """
+        warnings.warn(
+            "`plot_keypoint_matching` is deprecated and will be removed in transformers v. "
+            "Use `visualize_keypoint_matching` instead.",
+            FutureWarning,
+        )
+
         if is_matplotlib_available():
             import matplotlib.pyplot as plt
         else:

src/transformers/models/lightglue/modular_lightglue.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import warnings
 from dataclasses import dataclass
 from typing import Callable, Optional, Union
 
@@ -20,7 +21,7 @@ from torch import nn
 from torch.nn.utils.rnn import pad_sequence
 
 from ...configuration_utils import PretrainedConfig
-from ...image_utils import ImageInput, to_numpy_array
+from ...image_utils import ImageInput, is_vision_available, to_numpy_array
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
@@ -35,6 +36,10 @@ from ..superglue.image_processing_superglue import SuperGlueImageProcessor, vali
 from ..superpoint import SuperPointConfig
 
 
+if is_vision_available():
+    from PIL import Image, ImageDraw
+
+
 logger = logging.get_logger(__name__)
 
 
@@ -220,18 +225,90 @@ class LightGlueImageProcessor(SuperGlueImageProcessor):
     ) -> list[dict[str, torch.Tensor]]:
         return super().post_process_keypoint_matching(outputs, target_sizes, threshold)
 
+    # Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor.visualize_keypoint_matching with EfficientLoFTR->LightGlue
+    def visualize_keypoint_matching(
+        self,
+        images: ImageInput,
+        keypoint_matching_output: list[dict[str, torch.Tensor]],
+    ) -> list["Image.Image"]:
+        """
+        Plots the image pairs side by side with the detected keypoints as well as the matching between them.
+
+        Args:
+            images (`ImageInput`):
+                Image pairs to plot. Same as `LightGlueImageProcessor.preprocess`. Expects either a list of 2
+                images or a list of list of 2 images list with pixel values ranging from 0 to 255.
+            keypoint_matching_output (List[Dict[str, torch.Tensor]]]):
+                A post processed keypoint matching output
+
+        Returns:
+            `List[PIL.Image.Image]`: A list of PIL images, each containing the image pairs side by side with the detected
+            keypoints as well as the matching between them.
+        """
+        images = validate_and_format_image_pairs(images)
+        images = [to_numpy_array(image) for image in images]
+        image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]
+
+        results = []
+        for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
+            height0, width0 = image_pair[0].shape[:2]
+            height1, width1 = image_pair[1].shape[:2]
+            plot_image = np.zeros((max(height0, height1), width0 + width1, 3), dtype=np.uint8)
+            plot_image[:height0, :width0] = image_pair[0]
+            plot_image[:height1, width0:] = image_pair[1]
+
+            plot_image_pil = Image.fromarray(plot_image)
+            draw = ImageDraw.Draw(plot_image_pil)
+
+            keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
+            keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
+            for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
+                keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
+            ):
+                color = self._get_color(matching_score)
+                draw.line(
+                    (keypoint0_x, keypoint0_y, keypoint1_x + width0, keypoint1_y),
+                    fill=color,
+                    width=3,
+                )
+                draw.ellipse((keypoint0_x - 2, keypoint0_y - 2, keypoint0_x + 2, keypoint0_y + 2), fill="black")
+                draw.ellipse(
+                    (keypoint1_x + width0 - 2, keypoint1_y - 2, keypoint1_x + width0 + 2, keypoint1_y + 2),
+                    fill="black",
+                )
+
+            results.append(plot_image_pil)
+        return results
+
+    # Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor._get_color
+    def _get_color(self, score):
+        """Maps a score to a color."""
+        r = int(255 * (1 - score))
+        g = int(255 * score)
+        b = 0
+        return (r, g, b)
+
     def plot_keypoint_matching(self, images: ImageInput, keypoint_matching_output: LightGlueKeypointMatchingOutput):
         """
         Plots the image pairs side by side with the detected keypoints as well as the matching between them. Requires
         matplotlib to be installed.
 
+        .. deprecated::
+            `plot_keypoint_matching` is deprecated and will be removed in a future version. Use `visualize_keypoint_matching` instead.
+
         Args:
             images (`ImageInput`):
                 Image pairs to plot. Same as `LightGlueImageProcessor.preprocess`. Expects either a list of 2 images or
                 a list of list of 2 images list with pixel values ranging from 0 to 255.
-            outputs ([`LightGlueKeypointMatchingOutput`]):
+            keypoint_matching_output ([`LightGlueKeypointMatchingOutput`]):
                 Raw outputs of the model.
         """
+        warnings.warn(
+            "`plot_keypoint_matching` is deprecated and will be removed in transformers v. "
+            "Use `visualize_keypoint_matching` instead.",
+            FutureWarning,
+        )
+
         if is_matplotlib_available():
             import matplotlib.pyplot as plt
         else:

src/transformers/models/superglue/image_processing_superglue.py

@@ -47,6 +47,7 @@ if TYPE_CHECKING:
 
 if is_vision_available():
     import PIL
+    from PIL import Image, ImageDraw
 
 logger = logging.get_logger(__name__)
 
@@ -406,5 +407,68 @@ class SuperGlueImageProcessor(BaseImageProcessor):
 
         return results
 
+    # Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor.visualize_keypoint_matching with EfficientLoFTR->SuperGlue
+    def visualize_keypoint_matching(
+        self,
+        images: ImageInput,
+        keypoint_matching_output: list[dict[str, torch.Tensor]],
+    ) -> list["Image.Image"]:
+        """
+        Plots the image pairs side by side with the detected keypoints as well as the matching between them.
+
+        Args:
+            images (`ImageInput`):
+                Image pairs to plot. Same as `SuperGlueImageProcessor.preprocess`. Expects either a list of 2
+                images or a list of list of 2 images list with pixel values ranging from 0 to 255.
+            keypoint_matching_output (List[Dict[str, torch.Tensor]]]):
+                A post processed keypoint matching output
+
+        Returns:
+            `List[PIL.Image.Image]`: A list of PIL images, each containing the image pairs side by side with the detected
+            keypoints as well as the matching between them.
+        """
+        images = validate_and_format_image_pairs(images)
+        images = [to_numpy_array(image) for image in images]
+        image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]
+
+        results = []
+        for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
+            height0, width0 = image_pair[0].shape[:2]
+            height1, width1 = image_pair[1].shape[:2]
+            plot_image = np.zeros((max(height0, height1), width0 + width1, 3), dtype=np.uint8)
+            plot_image[:height0, :width0] = image_pair[0]
+            plot_image[:height1, width0:] = image_pair[1]
+
+            plot_image_pil = Image.fromarray(plot_image)
+            draw = ImageDraw.Draw(plot_image_pil)
+
+            keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
+            keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
+            for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
+                keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
+            ):
+                color = self._get_color(matching_score)
+                draw.line(
+                    (keypoint0_x, keypoint0_y, keypoint1_x + width0, keypoint1_y),
+                    fill=color,
+                    width=3,
+                )
+                draw.ellipse((keypoint0_x - 2, keypoint0_y - 2, keypoint0_x + 2, keypoint0_y + 2), fill="black")
+                draw.ellipse(
+                    (keypoint1_x + width0 - 2, keypoint1_y - 2, keypoint1_x + width0 + 2, keypoint1_y + 2),
+                    fill="black",
+                )
+
+            results.append(plot_image_pil)
+        return results
+
+    # Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor._get_color
+    def _get_color(self, score):
+        """Maps a score to a color."""
+        r = int(255 * (1 - score))
+        g = int(255 * score)
+        b = 0
+        return (r, g, b)
+
 
 __all__ = ["SuperGlueImageProcessor"]