add github action, use load image

.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

@@ -32,6 +32,9 @@ pip3 install autodistill-owlv2
 
 ```python
 from autodistill_owlv2 import OWLv2
+from autodistill.detection import CaptionOntology
+from autodistill.utils import plot
+import cv2
 
 # define an ontology to map class names to our OWLv2 prompt
 # the ontology dictionary has the format {caption: class}
@@ -42,10 +45,21 @@ base_model = OWLv2(
     ontology=CaptionOntology(
         {
             "person": "person",
-            "a forklift": "forklift"
+            "dog": "dog"
         }
     )
 )
+
+# run inference on a single image
+results = base_model.predict("dog.jpeg")
+
+plot(
+    image=cv2.imread("dog.jpeg"),
+    classes=base_model.ontology.classes(),
+    detections=results
+)
+
+# label a folder of images
 base_model.label("./context_images", extension=".jpeg")
 ```
 

autodistill_owlv2/__init__.py

@@ -1,3 +1,3 @@
 from autodistill_owlv2.owlv2_model import OWLv2
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"

autodistill_owlv2/owlv2_model.py

@@ -1,50 +1,63 @@
 import os
+import subprocess
 from dataclasses import dataclass
+from typing import Any
 
-from PIL import Image
 import numpy as np
+import supervision as sv
 import torch
 
-import subprocess
-
-import supervision as sv
 from autodistill.detection import CaptionOntology, DetectionBaseModel
+from autodistill.helpers import load_image
 
 HOME = os.path.expanduser("~")
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+
 @dataclass
 class OWLv2(DetectionBaseModel):
     ontology: CaptionOntology
-    
+
     def __init__(self, ontology: CaptionOntology):
         # install transformers from source, since OWLv2 is not yet in a release
         # (as of October 26th, 2023)
         try:
-            from transformers import Owlv2Processor, Owlv2ForObjectDetection
+            from transformers import Owlv2ForObjectDetection, Owlv2Processor
         except:
-            subprocess.run(["pip3", "install", "git+https://github.com/huggingface/transformers"])
-            from transformers import Owlv2Processor, Owlv2ForObjectDetection
-
-        self.processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
-        self.model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
+            subprocess.run(
+                ["pip3", "install", "git+https://github.com/huggingface/transformers"]
+            )
+            from transformers import Owlv2ForObjectDetection, Owlv2Processor
+
+        self.processor = Owlv2Processor.from_pretrained(
+            "google/owlv2-base-patch16-ensemble"
+        )
+        self.model = Owlv2ForObjectDetection.from_pretrained(
+            "google/owlv2-base-patch16-ensemble"
+        )
         self.ontology = ontology
 
-    def predict(self, input: str, confidence: int = 0.1) -> sv.Detections:
-        image = Image.open(input)
+    def predict(self, input: Any, confidence: int = 0.1) -> sv.Detections:
+        image = load_image(input, return_format="PIL")
         texts = [self.ontology.prompts()]
 
         inputs = self.processor(text=texts, images=image, return_tensors="pt")
         outputs = self.model(**inputs)
 
         target_sizes = torch.Tensor([image.size[::-1]])
 
-        results = self.processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=0.1)
+        results = self.processor.post_process_object_detection(
+            outputs=outputs, target_sizes=target_sizes, threshold=0.1
+        )
 
         i = 0
         text = texts[i]
 
-        boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
+        boxes, scores, labels = (
+            results[i]["boxes"],
+            results[i]["scores"],
+            results[i]["labels"],
+        )
 
         final_boxes, final_scores, final_labels = [], [], []
 
@@ -57,13 +70,12 @@ def predict(self, input: str, confidence: int = 0.1) -> sv.Detections:
             final_boxes.append(box)
             final_scores.append(score.item())
             final_labels.append(label.item())
-            print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
 
         if len(final_boxes) == 0:
             return sv.Detections.empty()
-        
+
         return sv.Detections(
             xyxy=np.array(final_boxes),
             class_id=np.array(final_labels),
             confidence=np.array(final_scores),
-        )
+        )