File size: 3,512 Bytes
0070fce | 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 | from modules.api import api
from fastapi.exceptions import HTTPException
from fastapi import FastAPI, Body
from typing import List
from PIL import Image
import gradio as gr
import numpy as np
from .utils import judge_image_type
from .logging import logger
from .global_state import (
get_all_preprocessor_names,
get_all_controlnet_names,
get_preprocessor,
)
def encode_to_base64(image):
if isinstance(image, str):
return image
elif not judge_image_type(image):
return "Detect result is not image"
elif isinstance(image, Image.Image):
return api.encode_pil_to_base64(image)
elif isinstance(image, np.ndarray):
return encode_np_to_base64(image)
else:
logger.warning("Unable to encode image...")
return ""
def encode_np_to_base64(image):
pil = Image.fromarray(image)
return api.encode_pil_to_base64(pil)
def controlnet_api(_: gr.Blocks, app: FastAPI):
@app.get("/controlnet/model_list")
async def model_list():
up_to_date_model_list = get_all_controlnet_names()
logger.debug(up_to_date_model_list)
return {"model_list": up_to_date_model_list}
@app.get("/controlnet/module_list")
async def module_list():
module_list = get_all_preprocessor_names()
logger.debug(module_list)
return {
"module_list": module_list,
# TODO: Add back module detail.
# "module_detail": external_code.get_modules_detail(alias_names),
}
@app.post("/controlnet/detect")
async def detect(
controlnet_module: str = Body("none", title="Module"),
controlnet_input_images: List[str] = Body([], title="Input Images"),
controlnet_processor_res: int = Body(512, title="Processor Resolution"),
controlnet_threshold_a: float = Body(64, title="Threshold a"),
controlnet_threshold_b: float = Body(64, title="Threshold b"),
):
processor_module = get_preprocessor(controlnet_module)
if processor_module is None:
raise HTTPException(status_code=422, detail="Module not available")
if len(controlnet_input_images) == 0:
raise HTTPException(status_code=422, detail="No image selected")
logger.debug(
f"Detecting {str(len(controlnet_input_images))} images with the {controlnet_module} module."
)
results = []
poses = []
for input_image in controlnet_input_images:
img = np.array(api.decode_base64_to_image(input_image)).astype("uint8")
class JsonAcceptor:
def __init__(self) -> None:
self.value = None
def accept(self, json_dict: dict) -> None:
self.value = json_dict
json_acceptor = JsonAcceptor()
results.append(
processor_module(
img,
resolution=controlnet_processor_res,
slider_1=controlnet_threshold_a,
slider_2=controlnet_threshold_b,
json_pose_callback=json_acceptor.accept,
)
)
if "openpose" in controlnet_module:
assert json_acceptor.value is not None
poses.append(json_acceptor.value)
results64 = [encode_to_base64(img) for img in results]
res = {"images": results64, "info": "Success"}
if poses:
res["poses"] = poses
return res
|