Nikhil-aka-Nick/florence2-finalV13

from transformers import AutoProcessor, AutoModelForCausalLM import matplotlib.pyplot as plt import matplotlib.patches as patches

model_id = "Nikhil-aka-Nick/florence2-finalV13" model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, device_map="cuda") # load the model on GPU processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

def run_example(task_prompt, image, max_new_tokens=128): prompt = task_prompt inputs = processor(text=prompt, images=image, return_tensors="pt") generated_ids = model.generate( input_ids=inputs["input_ids"].cuda(), pixel_values=inputs["pixel_values"].cuda(), max_new_tokens=max_new_tokens, early_stopping=False, do_sample=False, num_beams=3, ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] parsed_answer = processor.post_process_generation( generated_text, task=task_prompt, image_size=(image.width, image.height) ) return parsed_answer

import matplotlib.pyplot as plt import matplotlib.patches as patches

def plot_bbox(image, data, figsize=(12, 12)): # Add figsize as a parameter with default size fig, ax = plt.subplots(figsize=figsize)

# Display the image
ax.imshow(image)

# Plot each bounding box
for bbox, label in zip(data['bboxes'], data['labels']):
    # Unpack the bounding box coordinates
    x1, y1, x2, y2 = bbox
    # Create a Rectangle patch
    rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
    # Add the rectangle to the Axes
    ax.add_patch(rect)
    # Annotate the label
    plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))

# Remove the axis ticks and labels
ax.axis('off')

# Show the plot
plt.show()

from datasets import load_dataset

dataset = load_dataset("Nikhil-aka-Nick/My_data_for_test")

example_id = 5 image = dataset["train"][example_id]["image"]

parsed_answer = run_example("", image=image) plot_bbox(image, parsed_answer[""])