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from typing import List, Union
import numpy as np
import torch
from diffusers.modular_pipelines import (
ComponentSpec,
InputParam,
ModularPipelineBlocks,
OutputParam,
PipelineState,
)
from PIL import Image, ImageDraw
from transformers import AutoProcessor, Florence2ForConditionalGeneration
class Florence2ImageAnnotatorBlock(ModularPipelineBlocks):
@property
def expected_components(self):
return [
ComponentSpec(
name="image_annotator",
type_hint=Florence2ForConditionalGeneration,
repo="florence-community/Florence-2-base-ft",
),
ComponentSpec(
name="image_annotator_processor",
type_hint=AutoProcessor,
repo="florence-community/Florence-2-base-ft",
),
]
@property
def inputs(self) -> List[InputParam]:
return [
InputParam(
"image",
type_hint=Union[Image.Image, List[Image.Image]],
required=True,
description="Image(s) to annotate",
metadata={"mellon":"image"},
),
InputParam(
"annotation_task",
type_hint=Union[str, List[str]],
default="<REFERRING_EXPRESSION_SEGMENTATION>",
metadata={"mellon":"dropdown"},
description="""Annotation Task to perform on the image.
Supported Tasks:
<OD>
<REFERRING_EXPRESSION_SEGMENTATION>
<CAPTION>
<DETAILED_CAPTION>
<MORE_DETAILED_CAPTION>
<DENSE_REGION_CAPTION>
<REGION_PROPOSAL>
<CAPTION_TO_PHRASE_GROUNDING>
<OPEN_VOCABULARY_DETECTION>
<OCR>
<OCR_WITH_REGION>
""",
),
InputParam(
"annotation_prompt",
type_hint=Union[str, List[str]],
required=True,
metadata={"mellon":"textbox"},
description="""Annotation Prompt to provide more context to the task.
Can be used to detect or segment out specific elements in the image
""",
),
InputParam(
"annotation_output_type",
type_hint=str,
default="mask_image",
metadata={"mellon":"dropdown"},
description="""Output type from annotation predictions. Availabe options are
annotation:
- raw annotation predictions from the model based on task type.
mask_image:
-black and white mask image for the given image based on the task type
mask_overlay:
- white mask overlayed on the original image
bounding_box:
- bounding boxes drawn on the original image
""",
),
InputParam(
"annotation_overlay",
type_hint=bool,
required=True,
default=False,
description="",
metadata={"mellon":"checkbox"},
),
InputParam(
"fill",
type_hint=str,
default="white",
description="",
),
]
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [
OutputParam(
"annotations",
type_hint=dict,
description="Annotations Predictions for input Image(s)",
),
OutputParam(
"images",
type_hint=Image,
description="Annotated input Image(s)",
metadata={"mellon":"image"},
),
]
def get_annotations(self, components, images, prompts, task):
task_prompts = [task + prompt for prompt in prompts]
inputs = components.image_annotator_processor(
text=task_prompts, images=images, return_tensors="pt"
).to(components.image_annotator.device, components.image_annotator.dtype)
generated_ids = components.image_annotator.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
annotations = components.image_annotator_processor.batch_decode(
generated_ids, skip_special_tokens=False
)
outputs = []
for image, annotation in zip(images, annotations):
outputs.append(
components.image_annotator_processor.post_process_generation(
annotation, task=task, image_size=(image.width, image.height)
)
)
return outputs
def _iter_polygon_point_sets(self, poly):
"""
Yields lists of (x, y) points for all simple polygons found in `poly`.
Supports formats:
- [x1, y1, x2, y2, ...]
- [[x, y], [x, y], ...]
- [xs, ys]
- dict {'x': xs, 'y': ys}
- nested lists containing any of the above
"""
if poly is None:
return
def is_num(v):
return isinstance(v, (int, float, np.number))
# dict {'x': [...], 'y': [...]}
if isinstance(poly, dict) and "x" in poly and "y" in poly:
xs, ys = poly["x"], poly["y"]
if (
isinstance(xs, (list, tuple))
and isinstance(ys, (list, tuple))
and len(xs) == len(ys)
):
pts = list(zip(xs, ys))
if len(pts) >= 3:
yield pts
return
if isinstance(poly, (list, tuple)):
# flat numeric [x1, y1, ...]
if all(is_num(v) for v in poly):
coords = list(poly)
if len(coords) >= 6 and len(coords) % 2 == 0:
yield list(zip(coords[0::2], coords[1::2]))
return
# list of pairs [[x, y], ...]
if all(
isinstance(v, (list, tuple))
and len(v) == 2
and all(is_num(n) for n in v)
for v in poly
):
if len(poly) >= 3:
yield [tuple(v) for v in poly]
return
# [xs, ys]
if len(poly) == 2 and all(isinstance(v, (list, tuple)) for v in poly):
xs, ys = poly
try:
if len(xs) == len(ys) and len(xs) >= 3:
yield list(zip(xs, ys))
return
except TypeError:
pass
# nested: recurse into parts
for part in poly:
yield from self._iter_polygon_point_sets(part)
# other types are ignored
def prepare_mask(self, images, annotations, overlay=False, fill="white"):
masks = []
for image, annotation in zip(images, annotations):
mask_image = image.copy() if overlay else Image.new("L", image.size, 0)
draw = ImageDraw.Draw(mask_image)
# use a safe fill for grayscale masks
mask_fill = fill
if not overlay and isinstance(fill, str):
# for "L" mode, white -> 255
mask_fill = 255
for _, _annotation in annotation.items():
if "polygons" in _annotation:
for poly in _annotation["polygons"]:
for pts in self._iter_polygon_point_sets(poly):
if len(pts) < 3:
continue
# clip to image bounds and flatten
flat = []
for x, y in pts:
xi = int(round(max(0, min(image.width - 1, x))))
yi = int(round(max(0, min(image.height - 1, y))))
flat.extend([xi, yi])
draw.polygon(flat, fill=mask_fill)
elif "bboxes" in _annotation:
for bbox in _annotation["bboxes"]:
flat = np.array(bbox).flatten().tolist()
if len(flat) == 4:
x0, y0, x1, y1 = flat
draw.rectangle(
(
int(round(x0)),
int(round(y0)),
int(round(x1)),
int(round(y1)),
),
fill=mask_fill,
)
elif "quad_boxes" in _annotation:
for quad in _annotation["quad_boxes"]:
for pts in self._iter_polygon_point_sets(quad):
if len(pts) < 3:
continue
flat = []
for x, y in pts:
xi = int(round(max(0, min(image.width - 1, x))))
yi = int(round(max(0, min(image.height - 1, y))))
flat.extend([xi, yi])
draw.polygon(flat, fill=mask_fill)
masks.append(mask_image)
return masks
def prepare_bounding_boxes(self, images, annotations):
outputs = []
for image, annotation in zip(images, annotations):
image_copy = image.copy()
draw = ImageDraw.Draw(image_copy)
for _, _annotation in annotation.items():
# Standard axis-aligned boxes
bboxes = _annotation.get("bboxes", [])
labels = _annotation.get("labels", [])
if len(labels) == 0:
labels = _annotation.get("bboxes_labels", [])
for i, bbox in enumerate(bboxes):
flat = np.array(bbox).flatten().tolist()
if len(flat) != 4:
continue
x0, y0, x1, y1 = flat
draw.rectangle(
(
int(round(x0)),
int(round(y0)),
int(round(x1)),
int(round(y1)),
),
outline="red",
width=3,
)
label = labels[i] if i < len(labels) else ""
if label:
text_y = max(0, int(y0) - 20)
draw.text((int(x0), text_y), label, fill="red")
# Quadrilateral boxes (draw as polygons)
quad_boxes = _annotation.get("quad_boxes", [])
qlabels = _annotation.get("labels", [])
for i, quad in enumerate(quad_boxes):
for pts in self._iter_polygon_point_sets(quad):
if len(pts) < 3:
continue
flat = []
xs, ys = [], []
for x, y in pts:
xi = int(round(max(0, min(image.width - 1, x))))
yi = int(round(max(0, min(image.height - 1, y))))
flat.extend([xi, yi])
xs.append(xi)
ys.append(yi)
# Outline polygon
try:
draw.polygon(flat, outline="red", width=3)
except TypeError:
# Pillow without width for polygon
draw.polygon(flat, outline="red")
# Optional label at centroid (inside the quad)
label = qlabels[i] if i < len(qlabels) else ""
if label:
cx = int(round(sum(xs) / len(xs)))
cy = int(round(sum(ys) / len(ys)))
cx = max(0, min(image.width - 1, cx))
cy = max(0, min(image.height - 1, cy))
draw.text((cx, cy), label, fill="red")
outputs.append(image_copy)
return outputs
def prepare_inputs(self, images, prompts):
prompts = prompts or ""
if isinstance(images, Image.Image):
images = [images]
if isinstance(prompts, str):
prompts = [prompts]
if len(images) != len(prompts):
raise ValueError("Number of images and annotation prompts must match.")
return images, prompts
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
skip_image = False
# these don't require a prompt and fail if one is given
if (
block_state.annotation_task == "<OD>"
or block_state.annotation_task == "<DENSE_REGION_CAPTION>"
or block_state.annotation_task == "<REGION_PROPOSAL>"
or block_state.annotation_task == "<OCR_WITH_REGION>"
):
block_state.annotation_prompt = ""
block_state.annotation_output_type = "bounding_box"
# these don't require a prompt and doesn't ouput an image
elif (
block_state.annotation_task == "<CAPTION>"
or block_state.annotation_task == "<DETAILED_CAPTION>"
or block_state.annotation_task == "<MORE_DETAILED_CAPTION>"
or block_state.annotation_task == "<OCR>"
):
block_state.annotation_prompt = ""
skip_image = True
images, annotation_task_prompt = self.prepare_inputs(
block_state.image, block_state.annotation_prompt
)
task = block_state.annotation_task
fill = block_state.fill
annotations = self.get_annotations(
components, images, annotation_task_prompt, task
)
block_state.annotations = annotations
block_state.images = None
if not skip_image:
if block_state.annotation_output_type == "mask_image":
block_state.images = self.prepare_mask(images, annotations)
if block_state.annotation_output_type == "mask_overlay":
block_state.images = self.prepare_mask(
images, annotations, overlay=True, fill=fill
)
elif block_state.annotation_output_type == "bounding_box":
block_state.images = self.prepare_bounding_boxes(images, annotations)
self.set_block_state(state, block_state)
return components, state
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