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import os
from uuid import uuid4
import pytesseract
import torch
from PIL import Image
from controlnet_aux import HEDdetector
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
from flask import Flask, request, send_file
from transformers import BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
from transformers import pipeline
app = Flask('chatgpt-plugin-extras')
class VitGPT2:
def __init__(self, device):
print(f"Initializing VitGPT2 ImageCaptioning to {device}")
self.pipeline = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
def inference(self, image_path):
captions = self.pipeline(image_path)[0]['generated_text']
print(f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text: {captions}")
return captions
class ImageCaptioning:
def __init__(self, device):
print(f"Initializing ImageCaptioning to {device}")
self.device = device
self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
self.model = BlipForConditionalGeneration.from_pretrained(
"Salesforce/blip-image-captioning-large", torch_dtype=self.torch_dtype).to(self.device)
def inference(self, image_path):
inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device, self.torch_dtype)
out = self.model.generate(**inputs)
captions = self.processor.decode(out[0], skip_special_tokens=True)
print(f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text: {captions}")
return captions
class VQA:
def __init__(self, device):
print(f"Initializing Visual QA to {device}")
self.device = device
self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
self.model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base",
torch_dtype=self.torch_dtype).to(self.device)
def inference(self, image_path, question):
inputs = self.processor(Image.open(image_path), question, return_tensors="pt").to(self.device, self.torch_dtype)
out = self.model.generate(**inputs)
answers = self.processor.decode(out[0], skip_special_tokens=True)
print(f"\nProcessed Visual QA, Input Image: {image_path}, Output Text: {answers}")
return answers
class Image2Hed:
def __init__(self, device):
print("Initializing Image2Hed")
self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')
def inference(self, inputs, output_filename):
output_path = os.path.join('data', output_filename)
image = Image.open(inputs)
hed = self.detector(image)
hed.save(output_path)
print(f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed: {output_path}")
return '/result/' + output_filename
class Image2Scribble:
def __init__(self, device):
print("Initializing Image2Scribble")
self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')
def inference(self, inputs, output_filename):
output_path = os.path.join('data', output_filename)
image = Image.open(inputs)
hed = self.detector(image, scribble=True)
hed.save(output_path)
print(f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed: {output_path}")
return '/result/' + output_filename
class InstructPix2Pix:
def __init__(self, device):
print(f"Initializing InstructPix2Pix to {device}")
self.device = device
self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix",
safety_checker=None,
torch_dtype=self.torch_dtype).to(device)
self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
def inference(self, image_path, text, output_filename):
"""Change style of image."""
print("===>Starting InstructPix2Pix Inference")
original_image = Image.open(image_path)
image = self.pipe(text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2).images[0]
output_path = os.path.join('data', output_filename)
image.save(output_path)
print(f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, "
f"Output Image: {output_path}")
return '/result/' + output_path
@app.route('/result/<filename>')
def get_result(filename):
file_path = os.path.join('data', filename)
return send_file(file_path, mimetype='image/png')
ic = ImageCaptioning("cpu")
vqa = VQA("cpu")
i2h = Image2Hed("cpu")
i2s = Image2Scribble("cpu")
# vgic = VitGPT2("cpu")
# ip2p = InstructPix2Pix("cpu")
@app.route('/image2hed', methods=['POST'])
def imag2hed():
file = request.files['file'] # 获取上传的文件
filename = str(uuid4()) + '.png'
filepath = os.path.join('data', 'upload', filename)
file.save(filepath)
output_filename = str(uuid4()) + '.png'
result = i2h.inference(filepath, output_filename)
return result
@app.route('/image2Scribble', methods=['POST'])
def image2Scribble():
file = request.files['file'] # 获取上传的文件
filename = str(uuid4()) + '.png'
filepath = os.path.join('data', 'upload', filename)
file.save(filepath)
output_filename = str(uuid4()) + '.png'
result = i2s.inference(filepath, output_filename)
return result
@app.route('/image-captioning', methods=['POST'])
def image_caption():
file = request.files['file'] # 获取上传的文件
filename = str(uuid4()) + '.png'
filepath = os.path.join('data', 'upload', filename)
file.save(filepath)
# result1 = vgic.inference(filepath)
result2 = ic.inference(filepath)
return result2
@app.route('/visual-qa', methods=['POST'])
def visual_qa():
file = request.files['file'] # 获取上传的文件
filename = str(uuid4()) + '.png'
filepath = os.path.join('data', 'upload', filename)
file.save(filepath)
question = request.args.get('q')
result = vqa.inference(filepath, question=question)
return result
# @app.route('/instruct-pix2pix', methods=['POST'])
# def InstructPix2Pix():
# file = request.files['file'] # 获取上传的文件
# filename = str(uuid4()) + '.png'
# filepath = os.path.join('data', 'upload', filename)
# file.save(filepath)
# output_filename = str(uuid4()) + '.png'
# question = request.args.get('t')
# result = ip2p.inference(filepath, question, output_filename)
# return result
@app.route('/ocr', methods=['POST'])
def ocr():
file = request.files['file'] # 获取上传的文件
lang = request.args.get('lang')
if lang is None:
lang = 'chi_sim+eng'
filename = str(uuid4()) + '.png'
filepath = os.path.join('data', 'upload', filename)
file.save(filepath)
# output_filename = str(uuid4()) + '.png'
# pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract'
result = pytesseract.image_to_string(Image.open(filepath), lang=lang)
return result
if __name__ == '__main__':
app.run(host='0.0.0.0')
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