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import gradio as gr
from PIL import Image, ImageDraw, ImageFont
import scipy.io.wavfile as wavfile
from transformers import pipeline
# Initialize pipelines
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
object_detector = pipeline("object-detection", model="facebook/detr-resnet-101")
# Constants
FONT_PATH = None # Update this with the path to your custom font if needed
FONT_SIZE = 20
BOX_COLOR = "red"
TEXT_BACKGROUND_COLOR = "red"
TEXT_COLOR = "white"
def generate_audio(text):
try:
# 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"
except Exception as e:
print(f"Error generating audio: {e}")
return None
def count_objects(detection_objects):
object_counts = {}
for detection in detection_objects:
label = detection['label']
if label in object_counts:
object_counts[label] += 1
else:
object_counts[label] = 1
return object_counts
def generate_text_from_objects(object_counts):
response = "This picture contains"
labels = list(object_counts.keys())
for i, label in enumerate(labels):
count = object_counts[label]
response += f" {count} {label}"
if count > 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=FONT_PATH, font_size=FONT_SIZE):
draw_image = image.copy()
draw = ImageDraw.Draw(draw_image)
font = ImageFont.truetype(font_path, font_size) if font_path else 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=BOX_COLOR, width=3)
label = detection['label']
score = detection['score']
text = f"{label} {score:.2f}"
text_size = draw.textbbox((xmin, ymin), text, font=font)
draw.rectangle([(text_size[0], text_size[1]), (text_size[2], text_size[3])], fill=TEXT_BACKGROUND_COLOR)
draw.text((xmin, ymin), text, fill=TEXT_COLOR, font=font)
return draw_image
def detect_object(image):
try:
detections = object_detector(image)
processed_image = draw_bounding_boxes(image, detections)
object_counts = count_objects(detections)
natural_text = generate_text_from_objects(object_counts)
processed_audio = generate_audio(natural_text)
return processed_image, processed_audio
except Exception as e:
print(f"Error in object detection: {e}")
return None, None
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-101 backbone with Audio Feedback"
)
demo.launch()
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