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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
model_path = ("../Model/models--facebook--detr-resnet-50/snapshots"
"/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b")
tts_model_path = ("../Model/models--kakao-enterprise--vits-ljs/snapshots"
"/3bcb8321394f671bd948ebf0d086d694dda95464")
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
# object_detector = pipeline("object-detection", model=model_path)
# narrator = pipeline("text-to-speech", model=tts_model_path)
def generate_audio(text):
narrated_text = narrator(text)
wavfile.write("finetuned_output.wav",
rate=narrated_text["sampling_rate"],
data=narrated_text["audio"][0])
return "finetuned_output.wav";
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, detection_results):
"""
Draws bounding boxes on the provided image based on the detection results.
Parameters:
image (PIL.Image): The input image to be annotated.
detection_results (list): A list of dictionaries, each containing the detected object details.
Returns:
PIL.Image: The image with bounding boxes drawn around the detected objects.
"""
# Convert the input image to ImageDraw object to draw on it
draw = ImageDraw.Draw(image)
# Iterate through each detection result
for result in detection_results:
# Extract the bounding box coordinates and label
box = result['box']
label = result['label']
score = result['score']
# Define coordinates for the bounding box
xmin, ymin, xmax, ymax = box['xmin'], box['ymin'], box['xmax'], box['ymax']
# Draw the bounding box (with a red outline)
draw.rectangle([xmin, ymin, xmax, ymax], outline="red", width=3)
# Optionally, add label with score near the bounding box
text = f"{label} ({score * 100:.1f}%)"
draw.text((xmin, ymin - 10), text, fill="red")
return image
def detect_objects(image):
raw_image = image
output = object_detector(raw_image)
processed_image = draw_bounding_boxes(raw_image, output)
naturalized_text = read_objects(output)
processed_audio = generate_audio(naturalized_text)
return processed_image, processed_audio
demo = gr.Interface(fn = detect_objects,
inputs=[gr.Image(label="Select Image",type="pil")],
outputs=[gr.Image(label="Summarized Text ",type="pil"), gr.Audio(label="Generated Audio")],
title="@SherryAhuja Project : Object Detection with Audio",
description="This AI application will be used to Detect objects in an image and generate audio.",)
demo.launch() |