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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 = ("../Models/models--facebook--detr-resnet-50/snapshots"
# "/1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b")
#
# tts_model_path = ("../Models/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)
# [{'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}}]
# 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="Object Detector with Audio",
description="THIS APPLICATION WILL BE USED TO HIGHLIGHT OBJECTS AND GIVES AUDIO DESCRIPTION FOR THE PROVIDED INPUT IMAGE.")
demo.launch()
# print(output)
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