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import gradio as gr
from PIL import Image, ImageDraw, ImageFont
import scipy.io.wavfile as wavfile
from transformers import pipeline
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
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 "output.wav"
def read_objects(detection_objects):
object_counts = {}
for detection in detection_objects:
label = detection['label']
object_counts[label] = object_counts.get(label, 0) + 1
response = "This picture contains"
labels = list(object_counts.keys())
for i, label in enumerate(labels):
response += f" {object_counts[label]} {label}" + ("s" if object_counts[label] > 1 else "")
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):
draw_image = image.copy()
draw = ImageDraw.Draw(draw_image)
if font_path:
font = ImageFont.truetype(font_path, font_size)
else:
font = ImageFont.load_default()
for detection in detections:
box = detection['box']
xmin, ymin, xmax, ymax = box['xmin'], box['ymin'], box['xmax'], box['ymax']
draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=3)
label = detection['label']
score = detection['score']
text = f"{label} {score:.2f}"
if font_path:
text_size = draw.textbbox((xmin, ymin), text, font=font)
else:
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
examples = [
["dogs.jpg"]
]
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="Audio Described Object Detector",
description="This application highlights objects in the provided image and generates an audio description.",
examples=examples
)
demo.launch()
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