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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") | |
| # object_detector = pipeline("object-detection", | |
| # model=model_path) | |
| object_detector = pipeline("object-detection", | |
| model="facebook/detr-resnet-50") | |
| narrator = pipeline("text-to-speech", | |
| model="kakao-enterprise/vits-ljs") | |
| # 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" | |
| 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=50): | |
| # 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 its 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=5) | |
| # 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 | |
| examples = [["example{}.jpg".format(i)] for i in range(1, 4)] | |
| demo = gr.Interface(fn=detect_object, | |
| inputs=[gr.Image(label="Select Image",type="pil")], | |
| theme='freddyaboulton/dracula_revamped', | |
| outputs=[gr.Image(label="Processed Image", type="pil"), gr.Audio(label="Generated Audio")], | |
| examples = examples, | |
| title="Object Detector", | |
| description="Detect objects in the input image with bounding boxes and audio description.") | |
| demo.launch() | |