app.py

# 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()