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Running
on
Zero
File size: 12,733 Bytes
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import os
from unittest.mock import patch
import spaces
import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
from transformers.dynamic_module_utils import get_imports
import torch
import requests
from PIL import Image, ImageDraw
import random
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import cv2
import io
def workaround_fixed_get_imports(filename: str | os.PathLike) -> list[str]:
if not str(filename).endswith("/modeling_florence2.py"):
return get_imports(filename)
imports = get_imports(filename)
imports.remove("flash_attn")
return imports
with patch("transformers.dynamic_module_utils.get_imports", workaround_fixed_get_imports):
model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True).to("cuda").eval()
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True)
colormap = ['blue', 'orange', 'green', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan', 'red',
'lime', 'indigo', 'violet', 'aqua', 'magenta', 'coral', 'gold', 'tan', 'skyblue']
def fig_to_pil(fig):
buf = io.BytesIO()
fig.savefig(buf, format='png')
buf.seek(0)
return Image.open(buf)
@spaces.GPU
def run_example(task_prompt, image, text_input=None):
if text_input is None:
prompt = task_prompt
else:
prompt = task_prompt + text_input
inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
with torch.inference_mode():
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
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.size[0], image.size[1])
)
return parsed_answer
def plot_bbox(image, data):
fig, ax = plt.subplots()
ax.imshow(image)
for bbox, label in zip(data['bboxes'], data['labels']):
x1, y1, x2, y2 = bbox
rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(rect)
plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='indigo', alpha=0.5))
ax.axis('off')
return fig_to_pil(fig)
def draw_polygons(image, prediction, fill_mask=False):
fig, ax = plt.subplots()
ax.imshow(image)
scale = 1
for polygons, label in zip(prediction['polygons'], prediction['labels']):
color = random.choice(colormap)
fill_color = random.choice(colormap) if fill_mask else None
for _polygon in polygons:
_polygon = np.array(_polygon).reshape(-1, 2)
if _polygon.shape[0] < 3:
continue
_polygon = (_polygon * scale).reshape(-1).tolist()
if len(_polygon) % 2 != 0:
continue
polygon_points = np.array(_polygon).reshape(-1, 2)
if fill_mask:
polygon = patches.Polygon(polygon_points, edgecolor=color, facecolor=fill_color, linewidth=2)
else:
polygon = patches.Polygon(polygon_points, edgecolor=color, fill=False, linewidth=2)
ax.add_patch(polygon)
plt.text(polygon_points[0, 0], polygon_points[0, 1], label, color='white', fontsize=8, bbox=dict(facecolor=color, alpha=0.5))
ax.axis('off')
return fig_to_pil(fig)
def draw_ocr_bboxes(image, prediction):
fig, ax = plt.subplots()
ax.imshow(image)
scale = 1
bboxes, labels = prediction['quad_boxes'], prediction['labels']
for box, label in zip(bboxes, labels):
color = random.choice(colormap)
new_box = np.array(box) * scale
if new_box.ndim == 1:
new_box = new_box.reshape(-1, 2)
polygon = patches.Polygon(new_box, edgecolor=color, fill=False, linewidth=3)
ax.add_patch(polygon)
plt.text(new_box[0, 0], new_box[0, 1], label, color='white', fontsize=8, bbox=dict(facecolor=color, alpha=0.5))
ax.axis('off')
return fig_to_pil(fig)
@spaces.GPU(duration=120)
def process_video(input_video_path, task_prompt):
cap = cv2.VideoCapture(input_video_path)
if not cap.isOpened():
return None
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
if frame_width <= 0 or frame_height <= 0 or fps <= 0 or total_frames <= 0:
cap.release()
return None
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter("output_vid.mp4", fourcc, fps, (frame_width, frame_height))
if not out.isOpened():
cap.release()
return None
processed_frames = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pil_image = Image.fromarray(frame_rgb)
result = run_example(task_prompt, pil_image)
processed_image = pil_image
if task_prompt == "<OD>":
if "<OD>" in result and "bboxes" in result["<OD>"] and "labels" in result["<OD>"]:
processed_image = plot_bbox(pil_image, result['<OD>'])
elif task_prompt == "<DENSE_REGION_CAPTION>":
if "<DENSE_REGION_CAPTION>" in result and "polygons" in result["<DENSE_REGION_CAPTION>"] and "labels" in result["<DENSE_REGION_CAPTION>"]:
processed_image = draw_polygons(pil_image, result['<DENSE_REGION_CAPTION>'], fill_mask=True)
processed_frame = cv2.cvtColor(np.array(processed_image), cv2.COLOR_RGB2BGR)
out.write(processed_frame)
processed_frames += 1
cap.release()
out.release()
cv2.destroyAllWindows()
if processed_frames == 0:
return None
return "output_vid.mp4"
css = """
#output {
min-height: 100px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML("<h1><center>Microsoft Florence-2-large-ft</center></h1>")
with gr.Tab(label="Image"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Picture", type="pil")
task_dropdown = gr.Dropdown(
choices=["Caption", "Detailed Caption", "More Detailed Caption", "Caption to Phrase Grounding",
"Object Detection", "Dense Region Caption", "Region Proposal", "Referring Expression Segmentation",
"Region to Segmentation", "Open Vocabulary Detection", "Region to Category", "Region to Description",
"OCR", "OCR with Region"],
label="Task", value="Caption"
)
text_input = gr.Textbox(label="Text Input (is Optional)", visible=False)
gr.Examples(
examples=[
[
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true",
"Detailed Caption",
"",
],
[
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true",
"Object Detection",
"",
],
[
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true",
"Caption to Phrase Grounding",
"A green car parked in front of a yellow building."
],
[
"https://datasets-server.huggingface.co/assets/huggingface/documentation-images/--/566a43334e8b6331dddd8142495bc2f3209f32b0/--/default/validation/3/image/image.jpg?Expires=1718892641&Signature=GFpkyFBNrVf~Mq0jFjbpXWQLCOQblOm6Y1R57zl0tZOKWg5lfK8Jv1Tkxv35sMOARYDiJEE7C0hIp0fKazo1lYbv0ZTAKkwHUY2RroifVea4JRCyovJVptsmIZnlXkJU68N7bJhh8K07cu04G5mqaLRRehqDABKqEqgIdtBS5WcUXdoqkl0Fh2c8KN3GK9hZba9E6ZouBXhuffEEzykss1pIm6MW-WLx5l7~RXKu6BwcFq~6--3KoYVM4U~aEQdgTJg6P2ESH4DkEWN8Qpf~vaHBi2CZQSGurM1U0sZqIYrSLPaUov1h00MQMmnNEzMDZUeIq7~j07hVmwWgflQZeA__&Key-Pair-Id=K3EI6M078Z3AC3",
"OCR",
""
]
],
inputs=[input_image, task_dropdown, text_input],
)
submit_btn = gr.Button(value="Submit")
with gr.Column():
output_text = gr.Textbox(label="Results")
output_image = gr.Image(label="Image", type="pil")
with gr.Tab(label="Video"):
with gr.Row():
with gr.Column():
input_video = gr.Video(label="Video")
video_task_dropdown = gr.Dropdown(
choices=["Object Detection", "Dense Region Caption"],
label="Video Task", value="Object Detection"
)
video_submit_btn = gr.Button(value="Process Video")
with gr.Column():
output_video = gr.Video(label="Video")
def update_text_input(task):
return gr.update(visible=task in ["Caption to Phrase Grounding", "Referring Expression Segmentation",
"Region to Segmentation", "Open Vocabulary Detection", "Region to Category",
"Region to Description"])
task_dropdown.change(fn=update_text_input, inputs=task_dropdown, outputs=text_input)
def process_image(image, task, text):
task_mapping = {
"Caption": ("<CAPTION>", lambda result: (result['<CAPTION>'], image)),
"Detailed Caption": ("<DETAILED_CAPTION>", lambda result: (result['<DETAILED_CAPTION>'], image)),
"More Detailed Caption": ("<MORE_DETAILED_CAPTION>", lambda result: (result['<MORE_DETAILED_CAPTION>'], image)),
"Caption to Phrase Grounding": ("<CAPTION_TO_PHRASE_GROUNDING>", lambda result: (str(result['<CAPTION_TO_PHRASE_GROUNDING>']), plot_bbox(image, result['<CAPTION_TO_PHRASE_GROUNDING>']))),
"Object Detection": ("<OD>", lambda result: (str(result['<OD>']), plot_bbox(image, result['<OD>']))),
"Dense Region Caption": ("<DENSE_REGION_CAPTION>", lambda result: (str(result['<DENSE_REGION_CAPTION>']), plot_bbox(image, result['<DENSE_REGION_CAPTION>']))),
"Region Proposal": ("<REGION_PROPOSAL>", lambda result: (str(result['<REGION_PROPOSAL>']), plot_bbox(image, result['<REGION_PROPOSAL>']))),
"Referring Expression Segmentation": ("<REFERRING_EXPRESSION_SEGMENTATION>", lambda result: (str(result['<REFERRING_EXPRESSION_SEGMENTATION>']), draw_polygons(image, result['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True))),
"Region to Segmentation": ("<REGION_TO_SEGMENTATION>", lambda result: (str(result['<REGION_TO_SEGMENTATION>']), draw_polygons(image, result['<REGION_TO_SEGMENTATION>'], fill_mask=True))),
"Open Vocabulary Detection": ("<OPEN_VOCABULARY_DETECTION>", lambda result: (str(convert_to_od_format(result['<OPEN_VOCABULARY_DETECTION>'])), plot_bbox(image, convert_to_od_format(result['<OPEN_VOCABULARY_DETECTION>'])))),
"Region to Category": ("<REGION_TO_CATEGORY>", lambda result: (result['<REGION_TO_CATEGORY>'], image)),
"Region to Description": ("<REGION_TO_DESCRIPTION>", lambda result: (result['<REGION_TO_DESCRIPTION>'], image)),
"OCR": ("<OCR>", lambda result: (result['<OCR>'], image)),
"OCR with Region": ("<OCR_WITH_REGION>", lambda result: (str(result['<OCR_WITH_REGION>']), draw_ocr_bboxes(image, result['<OCR_WITH_REGION>']))),
}
if task in task_mapping:
prompt, process_func = task_mapping[task]
result = run_example(prompt, image, text)
return process_func(result)
else:
return "", image
submit_btn.click(fn=process_image, inputs=[input_img, task_dropdown, text_input], outputs=[output_text, output_image])
video_submit_btn.click(fn=process_video, inputs=[input_video, video_task_dropdown], outputs=output_video)
demo.launch() |