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| # AUTOGENERATED! DO NOT EDIT! File to edit: telecom_object_detection-Iteration_2.ipynb. | |
| # %% auto 0 | |
| __all__ = ['title', 'css', 'urls', 'imgs', 'img_samples', 'fig2img', 'custom_vision_detect_objects', 'set_example_url', | |
| 'set_example_image', 'detect_objects'] | |
| # %% telecom_object_detection-Iteration_2.ipynb 2 | |
| import gradio as gr | |
| import numpy as np | |
| import os | |
| import io | |
| import requests, validators | |
| from pathlib import Path | |
| # %% telecom_object_detection-Iteration_2.ipynb 4 | |
| ############################################ | |
| ### This code is based and adapted from: | |
| # https://github.com/MicrosoftLearning/AI-102-AIEngineer/blob/master/18-object-detection/Python/test-detector/test-detector.py | |
| # https://huggingface.co/spaces/Gradio-Blocks/Object-Detection-With-DETR-and-YOLOS/blob/main/app.py | |
| ############################################ | |
| # %% telecom_object_detection-Iteration_2.ipynb 7 | |
| from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient | |
| from msrest.authentication import ApiKeyCredentials | |
| from matplotlib import pyplot as plt | |
| from PIL import Image, ImageDraw, ImageFont | |
| from dotenv import load_dotenv | |
| # %% telecom_object_detection-Iteration_2.ipynb 12 | |
| def fig2img(fig): | |
| buf = io.BytesIO() | |
| fig.savefig(buf) | |
| buf.seek(0) | |
| img = Image.open(buf) | |
| return img | |
| def custom_vision_detect_objects(image_file: Path): | |
| dpi = 100 | |
| # Get Configuration Settings | |
| load_dotenv() | |
| prediction_endpoint = os.getenv('PredictionEndpoint') | |
| prediction_key = os.getenv('PredictionKey') | |
| project_id = os.getenv('ProjectID') | |
| model_name = os.getenv('ModelName') | |
| # Authenticate a client for the training API | |
| credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key}) | |
| prediction_client = CustomVisionPredictionClient(endpoint=prediction_endpoint, credentials=credentials) | |
| # Load image and get height, width and channels | |
| #image_file = 'produce.jpg' | |
| print('Detecting objects in', image_file) | |
| image = Image.open(image_file) | |
| h, w, ch = np.array(image).shape | |
| # Detect objects in the test image | |
| with open(image_file, mode="rb") as image_data: | |
| results = prediction_client.detect_image(project_id, model_name, image_data) | |
| # Create a figure for the results | |
| fig = plt.figure(figsize=(w/dpi, h/dpi)) | |
| plt.axis('off') | |
| # Display the image with boxes around each detected object | |
| draw = ImageDraw.Draw(image) | |
| lineWidth = int(w/800) | |
| color = 'cyan' | |
| for prediction in results.predictions: | |
| # Only show objects with a > 50% probability | |
| if (prediction.probability*100) > 50: | |
| # Box coordinates and dimensions are proportional - convert to absolutes | |
| left = prediction.bounding_box.left * w | |
| top = prediction.bounding_box.top * h | |
| height = prediction.bounding_box.height * h | |
| width = prediction.bounding_box.width * w | |
| # Draw the box | |
| points = ((left,top), (left+width,top), (left+width,top+height), (left,top+height), (left,top)) | |
| draw.line(points, fill=color, width=lineWidth) | |
| # Add the tag name and probability | |
| #plt.annotate(prediction.tag_name + ": {0:.2f}%".format(prediction.probability * 100),(left,top), backgroundcolor=color) | |
| plt.annotate( | |
| prediction.tag_name + ": {0:.0f}%".format(prediction.probability * 100), | |
| (left, top-1.372*h/dpi), | |
| backgroundcolor=color, | |
| fontsize=max(w/dpi, h/dpi), | |
| fontfamily='monospace' | |
| ) | |
| plt.imshow(image) | |
| plt.tight_layout(pad=0) | |
| return fig2img(fig) | |
| outputfile = 'output.jpg' | |
| fig.savefig(outputfile) | |
| print('Resulabsts saved in ', outputfile) | |
| # %% telecom_object_detection-Iteration_2.ipynb 17 | |
| title = """<h1 id="title">Telecom Object Detection with Azure Custom Vision</h1>""" | |
| css = ''' | |
| h1#title { | |
| text-align: center; | |
| } | |
| ''' | |
| # %% telecom_object_detection-Iteration_2.ipynb 18 | |
| urls = ["https://www.dropbox.com/s/y5bk8om5ucu46d3/747.jpg?dl=1"] | |
| imgs = [path.as_posix() for path in sorted(Path('images').rglob('*.jpg'))] | |
| img_samples = [[path.as_posix()] for path in sorted(Path('images').rglob('*.jpg'))] | |
| # %% telecom_object_detection-Iteration_2.ipynb 21 | |
| def set_example_url(example: list) -> dict: | |
| print(gr.Textbox.update(value=example[0])) | |
| return gr.Textbox.update(value=example[0]) | |
| def set_example_image(example: list) -> dict: | |
| return gr.Image.update(value=example[0]) | |
| def detect_objects(url_input:str, image_input:Image): | |
| print(f"{url_input=}") | |
| if validators.url(url_input): | |
| image = Image.open(requests.get(url_input, stream=True).raw) | |
| elif image_input: | |
| image = image_input | |
| print(image) | |
| print(image.size) | |
| w, h = image.size | |
| if max(w, h) > 1_200: | |
| factor = 1_200 / max(w, h) | |
| factor = 1 | |
| size = (int(w*factor), int(h*factor)) | |
| image = image.resize(size, resample=Image.Resampling.BILINEAR) | |
| resized_image_path = "input_object_detection.jpg" | |
| image.save(resized_image_path) | |
| return custom_vision_detect_objects(resized_image_path) | |
| # %% telecom_object_detection-Iteration_2.ipynb 23 | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown(title) | |
| with gr.Tabs(): | |
| with gr.TabItem("Image Upload"): | |
| with gr.Row(): | |
| image_input = gr.Image(type='pil') | |
| image_output = gr.Image(shape=(650,650)) | |
| with gr.Row(): | |
| """example_images = gr.Dataset(components=[img_input], | |
| samples=[[path.as_posix()] for path in sorted(Path('images').rglob('*.jpg'))] | |
| )""" | |
| #example_images = gr.Examples(examples=imgs, inputs=image_input) | |
| example_images = gr.Dataset(components=[image_input], samples=img_samples) | |
| image_button = gr.Button("Detect") | |
| with gr.TabItem("Image URL"): | |
| with gr.Row(): | |
| url_input = gr.Textbox(lines=2, label='Enter valid image URL here..') | |
| img_output_from_url = gr.Image(shape=(650,650)) | |
| with gr.Row(): | |
| example_url = gr.Dataset(components=[url_input], samples=[[str(url)] for url in urls]) | |
| url_button = gr.Button("Detect") | |
| url_button.click(detect_objects, inputs=[url_input,image_input], outputs=img_output_from_url) | |
| image_button.click(detect_objects, inputs=[url_input,image_input], outputs=image_output) | |
| #image_button.click(detect_objects, inputs=[example_images], outputs=image_output) | |
| example_url.click(fn=set_example_url, inputs=[example_url], outputs=[url_input]) | |
| example_images.click(fn=set_example_image, inputs=[example_images], outputs=[image_input]) | |
| demo.launch() | |