use load image, upadte readme eaxmple

.github/workflows/publish.yml

@@ -0,0 +1,32 @@
+name: Publish WorkFlow
+
+on:
+  release:
+    types: [created]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.8]
+    steps:
+      - name: 🛎️ Checkout
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.head_ref }}
+      - name: 🐍 Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: 🦾 Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install ".[dev]"
+      - name: 🚀 Publish to PyPi
+        env: 
+          PYPI_USERNAME: ${{ secrets.PYPI_USERNAME }}
+          PYPI_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+          PYPI_TEST_PASSWORD: ${{ secrets.PYPI_TEST_PASSWORD }}
+        run: |
+          make publish -e PYPI_USERNAME=$PYPI_USERNAME -e PYPI_PASSWORD=$PYPI_PASSWORD -e PYPI_TEST_PASSWORD=$PYPI_TEST_PASSWORD

.github/workflows/test.yml

@@ -0,0 +1,30 @@
+name: Test WorkFlow
+
+on:
+  pull_request:
+    branches: [main]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8, 3.9]
+    steps:
+      - name: 🛎️ Checkout
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.head_ref }}
+      - name: 🐍 Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: 🦾 Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install ".[dev]"
+      - name: 🧹 Lint with flake8
+        run: |
+          make check_code_quality
+      - name: 🧪 Test
+        run: "python -m pytest ./test"

.github/workflows/welcome.yml

@@ -0,0 +1,16 @@
+on:
+  issues:
+    types: [opened]
+  pull_request_target:
+    types: [opened]
+
+jobs:
+  build:
+    name: 👋 Welcome
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/[email protected]
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you soon."
+          pr-message:  "Hello there, thank you for opening an PR ! 🙏🏻 The team was notified and they will get back to you soon."

README.md

@@ -33,16 +33,32 @@ You can find a full list of `detection` Target Models on [the main autodistill r
 
 ```python
 from autodistill_grounded_sam import GroundedSAM
-from autodistill_yolov8 import YOLOv8
+from autodistill.detection import CaptionOntology
+from autodistill.utils import plot
+import cv2
 
-
-# define an ontology to map class names to our GroundingDINO prompt
+# define an ontology to map class names to our GroundedSAM prompt
 # the ontology dictionary has the format {caption: class}
 # where caption is the prompt sent to the base model, and class is the label that will
 # be saved for that caption in the generated annotations
 # then, load the model
-base_model = GroundedSAM(ontology=CaptionOntology({"shipping container": "container"}))
-
+base_model = GroundedSAM(
+    ontology=CaptionOntology(
+        {
+            "person": "person",
+            "shipping container": "shipping container",
+        }
+    )
+)
+
+# run inference on a single image
+results = base_model.predict("logistics.jpeg")
+
+plot(
+    image=cv2.imread("logistics.jpeg"),
+    classes=base_model.ontology.classes(),
+    detections=results
+)
 # label all images in a folder called `context_images`
 base_model.label("./context_images", extension=".jpeg")
 ```

autodistill_grounded_sam/grounded_sam.py

@@ -5,26 +5,26 @@
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 import torch
-import cv2
 
 torch.use_deterministic_algorithms(False)
 
+from typing import Any
+
+import numpy as np
 import supervision as sv
+from autodistill_grounded_sam.helpers import (combine_detections,
+                                              load_grounding_dino,
+                                              load_SAM)
+from autodistill.helpers import load_image
 from groundingdino.util.inference import Model
 from segment_anything import SamPredictor
 
-import numpy as np
 from autodistill.detection import CaptionOntology, DetectionBaseModel
 
-from autodistill_grounded_sam.helpers import (
-    combine_detections,
-    load_grounding_dino,
-    load_SAM
-)
-
 HOME = os.path.expanduser("~")
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+
 @dataclass
 class GroundedSAM(DetectionBaseModel):
     ontology: CaptionOntology
@@ -33,15 +33,17 @@ class GroundedSAM(DetectionBaseModel):
     box_threshold: float
     text_threshold: float
 
-    def __init__(self, ontology: CaptionOntology, box_threshold=0.35, text_threshold=0.25):
+    def __init__(
+        self, ontology: CaptionOntology, box_threshold=0.35, text_threshold=0.25
+    ):
         self.ontology = ontology
         self.grounding_dino_model = load_grounding_dino()
         self.sam_predictor = load_SAM()
         self.box_threshold = box_threshold
         self.text_threshold = text_threshold
 
-    def predict(self, input: str) -> sv.Detections:
-        image = cv2.imread(input)
+    def predict(self, input: Any) -> sv.Detections:
+        image = load_image(input, return_format="cv2")
 
         # GroundingDINO predictions
         detections_list = []
@@ -76,4 +78,4 @@ def predict(self, input: str) -> sv.Detections:
         detections.mask = np.array(result_masks)
 
         # separate in supervision to combine detections and override class_ids
-        return detections
+        return detections

autodistill_grounded_sam/helpers.py

@@ -2,12 +2,11 @@
 import urllib.request
 
 import numpy as np
+import supervision as sv
 import torch
 from groundingdino.util.inference import Model
 from segment_anything import SamPredictor, sam_model_registry
 
-import supervision as sv
-
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 if not torch.cuda.is_available():