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chore: address feedback on google gemini support

This commit is contained in:
soumik12345 2025-07-10 18:32:18 +05:30
parent 98ba121b5c
commit 6448fc7ea1
5 changed files with 95 additions and 62 deletions
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4
docs/detection/utils.md
View File

@ -102,10 +102,10 @@ status: new
:::supervision.detection.utils.xyxy_to_xywh
<div class="md-typeset">
<h2><a href="#supervision.detection.utils.normalized_xyxy_to_absolute_xyxy">normalized_xyxy_to_absolute_xyxy</a></h2>
<h2><a href="#supervision.detection.utils.denormalize_boxes">denormalize_boxes</a></h2>
</div>
:::supervision.detection.utils.normalized_xyxy_to_absolute_xyxy
:::supervision.detection.utils.denormalize_boxes
<h2><a href="#supervision.detection.utils.xyxy_to_xcycarh">xyxy_to_xcycarh</a></h2>
</div>

2
supervision/__init__.py
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@ -70,7 +70,7 @@ from supervision.detection.utils import (
mask_to_xyxy,
move_boxes,
move_masks,
normalized_xyxy_to_absolute_xyxy,
denormalize_boxes,
oriented_box_iou_batch,
pad_boxes,
polygon_to_mask,

100
supervision/detection/core.py
View File

@ -847,39 +847,18 @@ class Detections:
Examples:
```python
from google import genai
from google.genai import types
import supervision as sv
from PIL import Image
IMAGE = Image.open(<SOURCE_IMAGE_PATH>)
GENAI_CLIENT = genai.Client(api_key=<API_KEY>)
gemini_response_text = \"\"\"```json
[
{"box_2d": [543, 40, 728, 200], "label": "Cat", "id": 1},
{"box_2d": [653, 352, 820, 522], "label": "Dog", "id": 2}
]
```\"\"\"
system_instructions = '''
Return bounding boxes as a JSON array with labels and ids. Never return masks or code fencing. Limit to 25 objects.
If an object is present multiple times, name them according to their unique characteristic (colors, size, position, unique characteristics, etc..).
'''
safety_settings = [
types.SafetySetting(
category="HARM_CATEGORY_DANGEROUS_CONTENT",
threshold="BLOCK_ONLY_HIGH",
),
]
response = GENAI_CLIENT.models.generate_content(
model="gemini-2.0-flash-exp",
contents=[prompt, IMAGE],
config = types.GenerateContentConfig(
system_instruction=system_instructions,
temperature=0.5,
safety_settings=safety_settings,
)
)
detections = sv.Detections.from_lmm(
sv.LMM.GOOGLE_GEMINI_2_0,
response.text,
detections = sv.Detections.from_vlm(
sv.VLM.GOOGLE_GEMINI_2_0,
gemini_response_text,
resolution_wh=(IMAGE.size[0], IMAGE.size[1]),
)
@ -890,7 +869,6 @@ class Detections:
detections.data
# {'class_name': ['cat', 'dog']}
```
""" # noqa: E501 // docs
# filler logic mapping old from_lmm to new from_vlm
@ -930,6 +908,66 @@ class Detections:
def from_vlm(
cls, vlm: Union[VLM, str], result: Union[str, dict], **kwargs: Any
) -> Detections:
"""
Creates a Detections object from the given result string based on the specified
Vision Language Model (VLM).
Args:
vlm (Union[VLM, str]): The type of VLM (Large Multimodal Model) to use.
result (str): The result string containing the detection data.
**kwargs (Any): Additional keyword arguments required by the specified VLM.
Returns:
Detections: A new Detections object.
Raises:
ValueError: If the VLM is invalid, required arguments are missing, or
disallowed arguments are provided.
ValueError: If the specified VLM is not supported.
Examples:
```python
import supervision as sv
paligemma_result = "<loc0256><loc0256><loc0768><loc0768> cat"
detections = sv.Detections.from_vlm(
sv.VLM.PALIGEMMA,
paligemma_result,
resolution_wh=(1000, 1000),
classes=['cat', 'dog']
)
detections.xyxy
# array([[250., 250., 750., 750.]])
detections.class_id
# array([0])
```
Examples:
```python
import supervision as sv
gemini_response_text = \"\"\"```json
[
{"box_2d": [543, 40, 728, 200], "label": "Cat", "id": 1},
{"box_2d": [653, 352, 820, 522], "label": "Dog", "id": 2}
]
```\"\"\"
detections = sv.Detections.from_vlm(
sv.VLM.GOOGLE_GEMINI_2_0,
gemini_response_text,
resolution_wh=(IMAGE.size[0], IMAGE.size[1]),
)
detections.xyxy
# array([[250., 250., 750., 750.]])
detections.class_id
# array([0])
detections.data
# {'class_name': ['cat', 'dog']}
```
"""
vlm = validate_vlm_parameters(vlm, result, kwargs)
if vlm == VLM.PALIGEMMA:

6
supervision/detection/utils.py
View File

@ -447,7 +447,7 @@ def xyxy_to_xcycarh(xyxy: np.ndarray) -> np.ndarray:
return result.astype(float)
def normalized_xyxy_to_absolute_xyxy(
def denormalize_boxes(
normalized_xyxy: np.ndarray,
resolution_wh: Tuple[int, int],
normalization_factor: float = 1.0,
@ -477,7 +477,7 @@ def normalized_xyxy_to_absolute_xyxy(
[0.3, 0.4, 0.7, 0.8]
])
resolution_wh = (100, 200)
sv.normalized_xyxy_to_absolute_xyxy(normalized_xyxy, resolution_wh)
sv.denormalize_boxes(normalized_xyxy, resolution_wh)
# array([
# [ 10., 40., 50., 120.],
# [ 30., 80., 70., 160.]
@ -487,7 +487,7 @@ def normalized_xyxy_to_absolute_xyxy(
[10., 20., 50., 60.],
[30., 40., 70., 80.]
])
sv.normalized_xyxy_to_absolute_xyxy(normalized_xyxy, resolution_wh, max_value=100.0)
sv.denormalize_boxes(normalized_xyxy, resolution_wh, max_value=100.0)
# array([
# [ 10., 40., 50., 120.],
# [ 30., 80., 70., 160.]

45
supervision/detection/vlm.py
View File

@ -6,7 +6,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from supervision.detection.utils import (
normalized_xyxy_to_absolute_xyxy,
denormalize_boxes,
polygon_to_mask,
polygon_to_xyxy,
)
@ -22,11 +22,7 @@ class LMM(Enum):
FLORENCE_2 = "florence_2"
QWEN_2_5_VL = "qwen_2_5_vl"
GOOGLE_GEMINI_2_0 = "gemini_2_0"
GOOGLE_GEMINI_2_0_FLASH_LITE = "gemini_2_0_flash_lite"
GOOGLE_GEMINI_2_0_FLASH = "gemini_2_0_flash"
GOOGLE_GEMINI_2_5 = "gemini_2_5"
GOOGLE_GEMINI_2_5_FLASH_PREVIEW = "gemini_2_5_flash_preview"
GOOGLE_GEMINI_2_5_PRO_PREVIEW = "gemini_2_5_pro_preview"
class VLM(Enum):
@ -34,11 +30,7 @@ class VLM(Enum):
FLORENCE_2 = "florence_2"
QWEN_2_5_VL = "qwen_2_5_vl"
GOOGLE_GEMINI_2_0 = "gemini_2_0"
GOOGLE_GEMINI_2_0_FLASH_LITE = "gemini_2_0_flash_lite"
GOOGLE_GEMINI_2_0_FLASH = "gemini_2_0_flash"
GOOGLE_GEMINI_2_5 = "gemini_2_5"
GOOGLE_GEMINI_2_5_FLASH_PREVIEW = "gemini_2_5_flash_preview"
GOOGLE_GEMINI_2_5_PRO_PREVIEW = "gemini_2_5_pro_preview"
RESULT_TYPES: Dict[VLM, type] = {
@ -47,10 +39,6 @@ RESULT_TYPES: Dict[VLM, type] = {
VLM.QWEN_2_5_VL: str,
VLM.GOOGLE_GEMINI_2_0: str,
VLM.GOOGLE_GEMINI_2_5: str,
VLM.GOOGLE_GEMINI_2_0_FLASH_LITE: str,
VLM.GOOGLE_GEMINI_2_0_FLASH: str,
VLM.GOOGLE_GEMINI_2_5_FLASH_PREVIEW: str,
VLM.GOOGLE_GEMINI_2_5_PRO_PREVIEW: str,
}
REQUIRED_ARGUMENTS: Dict[VLM, List[str]] = {
@ -59,10 +47,6 @@ REQUIRED_ARGUMENTS: Dict[VLM, List[str]] = {
VLM.QWEN_2_5_VL: ["input_wh", "resolution_wh"],
VLM.GOOGLE_GEMINI_2_0: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_5: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_0_FLASH_LITE: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_0_FLASH: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_5_FLASH_PREVIEW: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_5_PRO_PREVIEW: ["resolution_wh"],
}
ALLOWED_ARGUMENTS: Dict[VLM, List[str]] = {
@ -71,10 +55,6 @@ ALLOWED_ARGUMENTS: Dict[VLM, List[str]] = {
VLM.QWEN_2_5_VL: ["input_wh", "resolution_wh", "classes"],
VLM.GOOGLE_GEMINI_2_0: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_5: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_0_FLASH_LITE: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_0_FLASH: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_5_FLASH_PREVIEW: ["resolution_wh"],
VLM.GOOGLE_GEMINI_2_5_PRO_PREVIEW: ["resolution_wh"],
}
SUPPORTED_TASKS_FLORENCE_2 = [
@ -357,9 +337,24 @@ def from_google_gemini(
resolution_wh: Tuple[int, int],
) -> Tuple[np.ndarray, np.ndarray]:
"""
Parse and scale bounding boxes from Google Gemini style JSON output.
https://aistudio.google.com/
https://ai.google.dev/gemini-api/docs/vision?lang=python
Parse and scale bounding boxes from Google Gemini style
[JSON output](https://ai.google.dev/gemini-api/docs/vision?lang=python).
The JSON is expected to be enclosed in triple backticks with the format:
```json
[
{"box_2d": [x1, y1, x2, y2], "label": "some class name"},
...
]
```
For example:
```json
[
{"box_2d": [10, 20, 110, 120], "label": "cat"},
{"box_2d": [50, 100, 150, 200], "label": "dog"}
]
```
Args:
result: String containing the JSON snippet enclosed by triple backticks.
@ -400,7 +395,7 @@ def from_google_gemini(
box = item["box_2d"]
# Gemini bbox order is [y_min, x_min, y_max, x_max]
xyxy.append(
normalized_xyxy_to_absolute_xyxy(
denormalize_boxes(
np.array([box[1], box[0], box[3], box[2]]).astype(np.float64),
resolution_wh=(w, h),
normalization_factor=1000,