Skip to content

Image Utils

crop_image

Crops the given image based on the given bounding box.

Parameters:

Name Type Description Default
image ImageType

The image to be cropped. ImageType is a flexible type, accepting either numpy.ndarray or PIL.Image.Image.

 required
xyxy Union[ndarray, List[int], Tuple[int, int, int, int]]

A bounding box coordinates in the format (x_min, y_min, x_max, y_max), accepted as either a numpy.ndarray, a list, or a tuple.

 required

Returns:

Type Description
ImageType

The cropped image. The type is determined by the input type and may be either a numpy.ndarray or PIL.Image.Image.

 
import cv2
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
image.shape
# (1080, 1920, 3)

xyxy = [200, 400, 600, 800]
cropped_image = sv.crop_image(image=image, xyxy=xyxy)
cropped_image.shape
# (400, 400, 3)

from PIL import Image
import supervision as sv

image = Image.open(<SOURCE_IMAGE_PATH>)
image.size
# (1920, 1080)

xyxy = [200, 400, 600, 800]
cropped_image = sv.crop_image(image=image, xyxy=xyxy)
cropped_image.size
# (400, 400)

crop_image

Source code in supervision/utils/image.py 
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
@convert_for_image_processing
def crop_image(
    image: ImageType,
    xyxy: Union[npt.NDArray[int], List[int], Tuple[int, int, int, int]],
) -> ImageType:
    """
    Crops the given image based on the given bounding box.

    Args:
        image (ImageType): The image to be cropped. `ImageType` is a flexible type,
            accepting either `numpy.ndarray` or `PIL.Image.Image`.
        xyxy (Union[np.ndarray, List[int], Tuple[int, int, int, int]]): A bounding box
            coordinates in the format `(x_min, y_min, x_max, y_max)`, accepted as either
            a `numpy.ndarray`, a `list`, or a `tuple`.

    Returns:
        (ImageType): The cropped image. The type is determined by the input type and
            may be either a `numpy.ndarray` or `PIL.Image.Image`.

    === "OpenCV"

        ```python
        import cv2
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        image.shape
        # (1080, 1920, 3)

        xyxy = [200, 400, 600, 800]
        cropped_image = sv.crop_image(image=image, xyxy=xyxy)
        cropped_image.shape
        # (400, 400, 3)
        ```

    === "Pillow"

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open(<SOURCE_IMAGE_PATH>)
        image.size
        # (1920, 1080)

        xyxy = [200, 400, 600, 800]
        cropped_image = sv.crop_image(image=image, xyxy=xyxy)
        cropped_image.size
        # (400, 400)
        ```

    ![crop_image](https://media.roboflow.com/supervision-docs/crop-image.png){ align=center width="800" }
    """  # noqa E501 // docs

    if isinstance(xyxy, (list, tuple)):
        xyxy = np.array(xyxy)
    xyxy = np.round(xyxy).astype(int)
    x_min, y_min, x_max, y_max = xyxy.flatten()
    return image[y_min:y_max, x_min:x_max]

crop_image

Scales the given image based on the given scale factor.

Parameters:

Name Type Description Default
image ImageType

The image to be scaled. ImageType is a flexible type, accepting either numpy.ndarray or PIL.Image.Image.

 required
scale_factor float

The factor by which the image will be scaled. Scale factor > 1.0 zooms in, < 1.0 zooms out.

 required

Returns:

Type Description
ImageType

The scaled image. The type is determined by the input type and may be either a numpy.ndarray or PIL.Image.Image.

Raises:

Type Description
ValueError

If the scale factor is non-positive.

 
import cv2
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
image.shape
# (1080, 1920, 3)

scaled_image = sv.scale_image(image=image, scale_factor=0.5)
scaled_image.shape
# (540, 960, 3)

from PIL import Image
import supervision as sv

image = Image.open(<SOURCE_IMAGE_PATH>)
image.size
# (1920, 1080)

scaled_image = sv.scale_image(image=image, scale_factor=0.5)
scaled_image.size
# (960, 540)
Source code in supervision/utils/image.py 
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
@convert_for_image_processing
def scale_image(image: ImageType, scale_factor: float) -> ImageType:
    """
    Scales the given image based on the given scale factor.

    Args:
        image (ImageType): The image to be scaled. `ImageType` is a flexible type,
            accepting either `numpy.ndarray` or `PIL.Image.Image`.
        scale_factor (float): The factor by which the image will be scaled. Scale
            factor > `1.0` zooms in, < `1.0` zooms out.

    Returns:
        (ImageType): The scaled image. The type is determined by the input type and
            may be either a `numpy.ndarray` or `PIL.Image.Image`.

    Raises:
        ValueError: If the scale factor is non-positive.

    === "OpenCV"

        ```python
        import cv2
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        image.shape
        # (1080, 1920, 3)

        scaled_image = sv.scale_image(image=image, scale_factor=0.5)
        scaled_image.shape
        # (540, 960, 3)
        ```

    === "Pillow"

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open(<SOURCE_IMAGE_PATH>)
        image.size
        # (1920, 1080)

        scaled_image = sv.scale_image(image=image, scale_factor=0.5)
        scaled_image.size
        # (960, 540)
        ```
    """
    if scale_factor <= 0:
        raise ValueError("Scale factor must be positive.")

    width_old, height_old = image.shape[1], image.shape[0]
    width_new = int(width_old * scale_factor)
    height_new = int(height_old * scale_factor)
    return cv2.resize(image, (width_new, height_new), interpolation=cv2.INTER_LINEAR)

resize_image

Resizes the given image to a specified resolution. Can maintain the original aspect ratio or resize directly to the desired dimensions.

Parameters:

Name Type Description Default
image ImageType

The image to be resized. ImageType is a flexible type, accepting either numpy.ndarray or PIL.Image.Image.

 required
resolution_wh Tuple[int, int]

The target resolution as (width, height).

 required
keep_aspect_ratio bool

Flag to maintain the image's original aspect ratio. Defaults to False.

 False

Returns:

Type Description
ImageType

The resized image. The type is determined by the input type and may be either a numpy.ndarray or PIL.Image.Image.

 
import cv2
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
image.shape
# (1080, 1920, 3)

resized_image = sv.resize_image(
    image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
)
resized_image.shape
# (562, 1000, 3)

from PIL import Image
import supervision as sv

image = Image.open(<SOURCE_IMAGE_PATH>)
image.size
# (1920, 1080)

resized_image = sv.resize_image(
    image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
)
resized_image.size
# (1000, 562)

resize_image

Source code in supervision/utils/image.py 
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
@convert_for_image_processing
def resize_image(
    image: ImageType,
    resolution_wh: Tuple[int, int],
    keep_aspect_ratio: bool = False,
) -> ImageType:
    """
    Resizes the given image to a specified resolution. Can maintain the original aspect
    ratio or resize directly to the desired dimensions.

    Args:
        image (ImageType): The image to be resized. `ImageType` is a flexible type,
            accepting either `numpy.ndarray` or `PIL.Image.Image`.
        resolution_wh (Tuple[int, int]): The target resolution as
            `(width, height)`.
        keep_aspect_ratio (bool, optional): Flag to maintain the image's original
            aspect ratio. Defaults to `False`.

    Returns:
        (ImageType): The resized image. The type is determined by the input type and
            may be either a `numpy.ndarray` or `PIL.Image.Image`.

    === "OpenCV"

        ```python
        import cv2
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        image.shape
        # (1080, 1920, 3)

        resized_image = sv.resize_image(
            image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
        )
        resized_image.shape
        # (562, 1000, 3)
        ```

    === "Pillow"

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open(<SOURCE_IMAGE_PATH>)
        image.size
        # (1920, 1080)

        resized_image = sv.resize_image(
            image=image, resolution_wh=(1000, 1000), keep_aspect_ratio=True
        )
        resized_image.size
        # (1000, 562)
        ```

    ![resize_image](https://media.roboflow.com/supervision-docs/resize-image.png){ align=center width="800" }
    """  # noqa E501 // docs
    if keep_aspect_ratio:
        image_ratio = image.shape[1] / image.shape[0]
        target_ratio = resolution_wh[0] / resolution_wh[1]
        if image_ratio >= target_ratio:
            width_new = resolution_wh[0]
            height_new = int(resolution_wh[0] / image_ratio)
        else:
            height_new = resolution_wh[1]
            width_new = int(resolution_wh[1] * image_ratio)
    else:
        width_new, height_new = resolution_wh

    return cv2.resize(image, (width_new, height_new), interpolation=cv2.INTER_LINEAR)

letterbox_image

Resizes and pads an image to a specified resolution with a given color, maintaining the original aspect ratio.

Parameters:

Name Type Description Default
image ImageType

The image to be resized. ImageType is a flexible type, accepting either numpy.ndarray or PIL.Image.Image.

 required
resolution_wh Tuple[int, int]

The target resolution as (width, height).

 required
color Union[Tuple[int, int, int], Color]

The color to pad with. If tuple provided it should be in BGR format.

 BLACK

Returns:

Type Description
ImageType

The resized image. The type is determined by the input type and may be either a numpy.ndarray or PIL.Image.Image.

 
import cv2
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
image.shape
# (1080, 1920, 3)

letterboxed_image = sv.letterbox_image(image=image, resolution_wh=(1000, 1000))
letterboxed_image.shape
# (1000, 1000, 3)

from PIL import Image
import supervision as sv

image = Image.open(<SOURCE_IMAGE_PATH>)
image.size
# (1920, 1080)

letterboxed_image = sv.letterbox_image(image=image, resolution_wh=(1000, 1000))
letterboxed_image.size
# (1000, 1000)

letterbox_image

Source code in supervision/utils/image.py 
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
@convert_for_image_processing
def letterbox_image(
    image: ImageType,
    resolution_wh: Tuple[int, int],
    color: Union[Tuple[int, int, int], Color] = Color.BLACK,
) -> ImageType:
    """
    Resizes and pads an image to a specified resolution with a given color, maintaining
    the original aspect ratio.

    Args:
        image (ImageType): The image to be resized. `ImageType` is a flexible type,
            accepting either `numpy.ndarray` or `PIL.Image.Image`.
        resolution_wh (Tuple[int, int]): The target resolution as
            `(width, height)`.
        color (Union[Tuple[int, int, int], Color]): The color to pad with. If tuple
            provided it should be in BGR format.

    Returns:
        (ImageType): The resized image. The type is determined by the input type and
            may be either a `numpy.ndarray` or `PIL.Image.Image`.

    === "OpenCV"

        ```python
        import cv2
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        image.shape
        # (1080, 1920, 3)

        letterboxed_image = sv.letterbox_image(image=image, resolution_wh=(1000, 1000))
        letterboxed_image.shape
        # (1000, 1000, 3)
        ```

    === "Pillow"

        ```python
        from PIL import Image
        import supervision as sv

        image = Image.open(<SOURCE_IMAGE_PATH>)
        image.size
        # (1920, 1080)

        letterboxed_image = sv.letterbox_image(image=image, resolution_wh=(1000, 1000))
        letterboxed_image.size
        # (1000, 1000)
        ```

    ![letterbox_image](https://media.roboflow.com/supervision-docs/letterbox-image.png){ align=center width="800" }
    """  # noqa E501 // docs
    color = unify_to_bgr(color=color)
    resized_image = resize_image(
        image=image, resolution_wh=resolution_wh, keep_aspect_ratio=True
    )
    height_new, width_new = resized_image.shape[:2]
    padding_top = (resolution_wh[1] - height_new) // 2
    padding_bottom = resolution_wh[1] - height_new - padding_top
    padding_left = (resolution_wh[0] - width_new) // 2
    padding_right = resolution_wh[0] - width_new - padding_left
    return cv2.copyMakeBorder(
        resized_image,
        padding_top,
        padding_bottom,
        padding_left,
        padding_right,
        cv2.BORDER_CONSTANT,
        value=color,
    )

overlay_image

Places an image onto a scene at a given anchor point, handling cases where the image's position is partially or completely outside the scene's bounds.

Parameters:

Name Type Description Default
image ndarray

The background scene onto which the image is placed.

 required
overlay ndarray

The image to be placed onto the scene.

 required
anchor Tuple[int, int]

The (x, y) coordinates in the scene where the top-left corner of the image will be placed.

 required

Returns:

Type Description
ndarray

The result image with overlay.

Examples:

import cv2
import numpy as np
import supervision as sv

image = cv2.imread(<SOURCE_IMAGE_PATH>)
overlay = np.zeros((400, 400, 3), dtype=np.uint8)
result_image = sv.overlay_image(image=image, overlay=overlay, anchor=(200, 400))

overlay_image

Source code in supervision/utils/image.py 
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
def overlay_image(
    image: npt.NDArray[np.uint8],
    overlay: npt.NDArray[np.uint8],
    anchor: Tuple[int, int],
) -> npt.NDArray[np.uint8]:
    """
    Places an image onto a scene at a given anchor point, handling cases where
    the image's position is partially or completely outside the scene's bounds.

    Args:
        image (np.ndarray): The background scene onto which the image is placed.
        overlay (np.ndarray): The image to be placed onto the scene.
        anchor (Tuple[int, int]): The `(x, y)` coordinates in the scene where the
            top-left corner of the image will be placed.

    Returns:
        (np.ndarray): The result image with overlay.

    Examples:
        ```python
        import cv2
        import numpy as np
        import supervision as sv

        image = cv2.imread(<SOURCE_IMAGE_PATH>)
        overlay = np.zeros((400, 400, 3), dtype=np.uint8)
        result_image = sv.overlay_image(image=image, overlay=overlay, anchor=(200, 400))
        ```

    ![overlay_image](https://media.roboflow.com/supervision-docs/overlay-image.png){ align=center width="800" }
    """  # noqa E501 // docs
    scene_height, scene_width = image.shape[:2]
    image_height, image_width = overlay.shape[:2]
    anchor_x, anchor_y = anchor

    is_out_horizontally = anchor_x + image_width <= 0 or anchor_x >= scene_width
    is_out_vertically = anchor_y + image_height <= 0 or anchor_y >= scene_height

    if is_out_horizontally or is_out_vertically:
        return image

    x_min = max(anchor_x, 0)
    y_min = max(anchor_y, 0)
    x_max = min(scene_width, anchor_x + image_width)
    y_max = min(scene_height, anchor_y + image_height)

    crop_x_min = max(-anchor_x, 0)
    crop_y_min = max(-anchor_y, 0)
    crop_x_max = image_width - max((anchor_x + image_width) - scene_width, 0)
    crop_y_max = image_height - max((anchor_y + image_height) - scene_height, 0)

    image[y_min:y_max, x_min:x_max] = overlay[
        crop_y_min:crop_y_max, crop_x_min:crop_x_max
    ]

    return image

ImageSink

Source code in supervision/utils/image.py 
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
class ImageSink:
    def __init__(
        self,
        target_dir_path: str,
        overwrite: bool = False,
        image_name_pattern: str = "image_{:05d}.png",
    ):
        """
        Initialize a context manager for saving images.

        Args:
            target_dir_path (str): The target directory where images will be saved.
            overwrite (bool, optional): Whether to overwrite the existing directory.
                Defaults to False.
            image_name_pattern (str, optional): The image file name pattern.
                Defaults to "image_{:05d}.png".

        Examples:
            ```python
            import supervision as sv

            frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>, stride=2)

            with sv.ImageSink(target_dir_path=<TARGET_CROPS_DIRECTORY>) as sink:
                for image in frames_generator:
                    sink.save_image(image=image)
            ```
        """  # noqa E501 // docs

        self.target_dir_path = target_dir_path
        self.overwrite = overwrite
        self.image_name_pattern = image_name_pattern
        self.image_count = 0

    def __enter__(self):
        if os.path.exists(self.target_dir_path):
            if self.overwrite:
                shutil.rmtree(self.target_dir_path)
                os.makedirs(self.target_dir_path)
        else:
            os.makedirs(self.target_dir_path)

        return self

    def save_image(self, image: np.ndarray, image_name: Optional[str] = None):
        """
        Save a given image in the target directory.

        Args:
            image (np.ndarray): The image to be saved. The image must be in BGR color
                format.
            image_name (str, optional): The name to use for the saved image.
                If not provided, a name will be
                generated using the `image_name_pattern`.
        """
        if image_name is None:
            image_name = self.image_name_pattern.format(self.image_count)

        image_path = os.path.join(self.target_dir_path, image_name)
        cv2.imwrite(image_path, image)
        self.image_count += 1

    def __exit__(self, exc_type, exc_value, exc_traceback):
        pass

Functions

__init__(target_dir_path, overwrite=False, image_name_pattern='image_{:05d}.png')

Initialize a context manager for saving images.

Parameters:

Name Type Description Default
target_dir_path str

The target directory where images will be saved.

 required
overwrite bool

Whether to overwrite the existing directory. Defaults to False.

 False
image_name_pattern str

The image file name pattern. Defaults to "image_{:05d}.png".

 'image_{:05d}.png'

Examples:

import supervision as sv

frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>, stride=2)

with sv.ImageSink(target_dir_path=<TARGET_CROPS_DIRECTORY>) as sink:
    for image in frames_generator:
        sink.save_image(image=image)
Source code in supervision/utils/image.py 
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
def __init__(
    self,
    target_dir_path: str,
    overwrite: bool = False,
    image_name_pattern: str = "image_{:05d}.png",
):
    """
    Initialize a context manager for saving images.

    Args:
        target_dir_path (str): The target directory where images will be saved.
        overwrite (bool, optional): Whether to overwrite the existing directory.
            Defaults to False.
        image_name_pattern (str, optional): The image file name pattern.
            Defaults to "image_{:05d}.png".

    Examples:
        ```python
        import supervision as sv

        frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>, stride=2)

        with sv.ImageSink(target_dir_path=<TARGET_CROPS_DIRECTORY>) as sink:
            for image in frames_generator:
                sink.save_image(image=image)
        ```
    """  # noqa E501 // docs

    self.target_dir_path = target_dir_path
    self.overwrite = overwrite
    self.image_name_pattern = image_name_pattern
    self.image_count = 0

save_image(image, image_name=None)

Save a given image in the target directory.

Parameters:

Name Type Description Default
image ndarray

The image to be saved. The image must be in BGR color format.

 required
image_name str

The name to use for the saved image. If not provided, a name will be generated using the image_name_pattern.

 None
Source code in supervision/utils/image.py 
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
def save_image(self, image: np.ndarray, image_name: Optional[str] = None):
    """
    Save a given image in the target directory.

    Args:
        image (np.ndarray): The image to be saved. The image must be in BGR color
            format.
        image_name (str, optional): The name to use for the saved image.
            If not provided, a name will be
            generated using the `image_name_pattern`.
    """
    if image_name is None:
        image_name = self.image_name_pattern.format(self.image_count)

    image_path = os.path.join(self.target_dir_path, image_name)
    cv2.imwrite(image_path, image)
    self.image_count += 1

Comments