File size: 7,898 Bytes
fac52b8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
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 Florence2ForConditionalGeneration, AutoProcessor
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",
),
InputParam(
"annotation_task",
type_hint=Union[str, List[str]],
required=True,
default="<REFERRING_EXPRESSION_SEGMENTATION>",
description="""Annotation Task to perform on the image.
Supported Tasks:
<OD>
<REFERRING_EXPRESSION_SEGMENTATION>
<CAPTION>
<DETAILED_CAPTION>
<MORE_DETAILED_CAPTION>
<DENSE_REGION_CAPTION>
<CAPTION_TO_PHRASE_GROUNDING>
<OPEN_VOCABULARY_DETECTION>
""",
),
InputParam(
"annotation_prompt",
type_hint=Union[str, List[str]],
required=True,
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,
required=True,
default="mask_image",
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="",
),
InputParam(
"fill",
type_hint=str,
default="white",
description="",
),
]
@property
def intermediate_outputs(self) -> List[OutputParam]:
return [
OutputParam(
"mask_image",
type_hint=Image,
description="Inpainting Mask for input Image(s)",
),
OutputParam(
"annotations",
type_hint=dict,
description="Annotations Predictions for input Image(s)",
),
OutputParam(
"image",
type_hint=Image,
description="Annotated input Image(s)",
),
]
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 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)
for _, _annotation in annotation.items():
if "polygons" in _annotation:
for polygon in _annotation["polygons"]:
polygon = np.array(polygon).reshape(-1, 2)
if len(polygon) < 3:
continue
polygon = polygon.reshape(-1).tolist()
draw.polygon(polygon, fill=fill)
elif "bbox" in _annotation:
bbox = _annotation["bbox"]
draw.rectangle(bbox, fill="white")
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():
bbox = _annotation["bbox"]
label = _annotation["label"]
draw.rectangle(bbox, outline="red", width=3)
draw.text((bbox[0], bbox[1] - 20), 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)
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
if block_state.annotation_output_type == "mask_image":
block_state.mask_image = self.prepare_mask(images, annotations)
else:
block_state.mask_image = None
if block_state.annotation_output_type == "mask_overlay":
block_state.image = self.prepare_mask(
images, annotations, overlay=True, fill=fill
)
elif block_state.annotation_output_type == "bounding_box":
block_state.image = self.prepare_bounding_boxes(images, annotations)
self.set_block_state(state, block_state)
return components, state
|