copy_app.py

import gradio as gr
from PIL import Image, ImageDraw, ImageFont
import scipy.io.wavfile as wavfile


# Use a pipeline as a high-level helper
from transformers import pipeline

narrator = pipeline("text-to-speech",
                    model="kakao-enterprise/vits-ljs")

object_detector = pipeline("object-detection",
                model="facebook/detr-resnet-50")


# Define the function to generate audio from text
def generate_audio(text):
    # Generate the narrated text
    narrated_text = narrator(text)

    # Save the audio to a WAV file
    wavfile.write("output.wav", rate=narrated_text["sampling_rate"],
                  data=narrated_text["audio"][0])

    # Return the path to the saved audio file
    return "output.wav"

# Could you please write me a python code that will take list of detection object as an input and it will give the response that will include all the objects (labels) provided in the input. For example if the input is like this: [{'score': 0.9996405839920044, 'label': 'person', 'box': {'xmin': 435, 'ymin': 282, 'xmax': 636, 'ymax': 927}}, {'score': 0.9995879530906677, 'label': 'dog', 'box': {'xmin': 570, 'ymin': 694, 'xmax': 833, 'ymax': 946}}]
# The output should be, This pictuture contains 1 person and 1 dog. If there are multiple objects, do not add 'and' between every objects but 'and' should be at the end only


def read_objects(detection_objects):
    # Initialize counters for each object label
    object_counts = {}

    # Count the occurrences of each label
    for detection in detection_objects:
        label = detection['label']
        if label in object_counts:
            object_counts[label] += 1
        else:
            object_counts[label] = 1

    # Generate the response string
    response = "This picture contains"
    labels = list(object_counts.keys())
    for i, label in enumerate(labels):
        response += f" {object_counts[label]} {label}"
        if object_counts[label] > 1:
            response += "s"
        if i < len(labels) - 2:
            response += ","
        elif i == len(labels) - 2:
            response += " and"

    response += "."

    return response



def draw_bounding_boxes(image, detections, font_path=None, font_size=20):
    """
    Draws bounding boxes on the given image based on the detections.
    :param image: PIL.Image object
    :param detections: List of detection results, where each result is a dictionary containing
                       'score', 'label', and 'box' keys. 'box' itself is a dictionary with 'xmin',
                       'ymin', 'xmax', 'ymax'.
    :param font_path: Path to the TrueType font file to use for text.
    :param font_size: Size of the font to use for text.
    :return: PIL.Image object with bounding boxes drawn.
    """
    # Make a copy of the image to draw on
    draw_image = image.copy()
    draw = ImageDraw.Draw(draw_image)

    # Load custom font or default font if path not provided
    if font_path:
        font = ImageFont.truetype(font_path, font_size)
    else:
        # When font_path is not provided, load default font but it's size is fixed
        font = ImageFont.load_default()
        # Increase font size workaround by using a TTF font file, if needed, can download and specify the path

    for detection in detections:
        box = detection['box']
        xmin = box['xmin']
        ymin = box['ymin']
        xmax = box['xmax']
        ymax = box['ymax']

        # Draw the bounding box
        draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=3)

        # Optionally, you can also draw the label and score
        label = detection['label']
        score = detection['score']
        text = f"{label} {score:.2f}"

        # Draw text with background rectangle for visibility
        if font_path:  # Use the custom font with increased size
            text_size = draw.textbbox((xmin, ymin), text, font=font)
        else:
            # Calculate text size using the default font
            text_size = draw.textbbox((xmin, ymin), text)

        draw.rectangle([(text_size[0], text_size[1]), (text_size[2], text_size[3])], fill="red")
        draw.text((xmin, ymin), text, fill="white", font=font)

    return draw_image


def detect_object(image):
    raw_image = image
    output = object_detector(raw_image)
    processed_image = draw_bounding_boxes(raw_image, output)
    natural_text = read_objects(output)
    processed_audio = generate_audio(natural_text)
    return processed_image, processed_audio


demo = gr.Interface(fn=detect_object,
                    inputs=[gr.Image(label="Select Image",type="pil")],
                    outputs=[gr.Image(label="Processed Image", type="pil"), gr.Audio(label="Generated Audio")],
                    title="AI-Powered Object Detection with Audio Feedback",
                    description="Upload an image and get object detection results using the DETR model with a ResNet-50 backbone with Audio Feedback")
demo.launch()