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import gradio as gr
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
import os
import onnxruntime
import numpy as np
def predict_fault(image, model):
image = image.detach().cpu().numpy()
input = {model.get_inputs()[0].name: image}
output = model.run(None, input)
preds = np.argmax(output[0], 1)
return preds.item()
def detect(image, writing_type, post_it, corner, empty):
"""
Detect features in the image: writing type, post-it notes, corners, and empty.
Args:
image (Union[PIL.Image.Image, str]): The input image, either as a PIL image object or a URL string.
writing_type (bool): Whether to detect handwriting type.
post_it (bool): Whether to detect post-it notes.
corner (bool): Whether to detect corners in the image.
empty (bool): Whether to check if the image is empty.
Returns:
dict: A dictionary containing detection results for each enabled option.
"""
writing_type_model, post_it_model, corner_model, empty_model = models
res_dict = {}
if writing_type:
input_image = writing_type_transforms(image).unsqueeze(0)
label = predict_fault(input_image, writing_type_model)
res_dict['writing_type'] = label
if post_it:
input_image = data_transforms(image).unsqueeze(0)
label = predict_fault(input_image, post_it_model)
res_dict['post_it'] = label
if corner:
input_image = data_transforms(image).unsqueeze(0)
label = predict_fault(input_image, corner_model)
res_dict['corner'] = label
if empty:
input_image = empty_transforms(image).unsqueeze(0)
label = predict_fault(input_image, empty_model)
res_dict['empty'] = 1 - label
return res_dict
def load_models():
try:
MODEL_PATH = os.environ.get("MODEL_PATH", './models/')
POST_IT_MODEL = os.environ.get("POST_IT_MODEL", 'post_it_model.onnx')
CORNER_MODEL = os.environ.get("CORNER_MODEL", 'corner_model.onnx')
EMPTY_MODEL = os.environ.get("EMPTY_MODEL", 'empty_v5_24_08_23.onnx')
WRITING_TYPE_MODEL = os.environ.get("WRITING_TYPE_MODEL", 'writing_type_v1.onnx')
print(f"ORT device: {onnxruntime.get_device()}")
# Load the models and the trained weights
writing_type_model = onnxruntime.InferenceSession(os.path.join(MODEL_PATH, WRITING_TYPE_MODEL))
post_it_model = onnxruntime.InferenceSession(os.path.join(MODEL_PATH, POST_IT_MODEL))
corner_model = onnxruntime.InferenceSession(os.path.join(MODEL_PATH, CORNER_MODEL))
empty_model = onnxruntime.InferenceSession(os.path.join(MODEL_PATH, EMPTY_MODEL))
return writing_type_model, post_it_model, corner_model, empty_model
except Exception as e:
print("Failed to load pretrained models: {}".format(e))
# Load the models
models = load_models()
# Transform methods for corner & post-it model inputs
data_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
# Transform methods for empty model inputs
empty_transforms = transforms.Compose([
transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Transform methods for writing-type model inputs
writing_type_transforms = transforms.Compose([
transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize([0.882, 0.883, 0.899], [0.088, 0.089, 0.094])
])
with gr.Blocks(title="Image Faulty Demo") as demo:
gr.Markdown("""
# Image Faulty
Find the project [here](https://github.com/xiaoyao9184/image-faulty).
""")
with gr.Row():
with gr.Column():
detecting_img = gr.Image(label="Input Image", type="pil", height=512)
with gr.Column():
writing_ckb = gr.Checkbox(label="Writing type", value=True)
postit_ckb = gr.Checkbox(label="Post it", value=True)
corner_ckb = gr.Checkbox(label="Folded corner", value=True)
empty_ckb = gr.Checkbox(label="Parper empty", value=True)
detecting_btn = gr.Button("Detect")
predicted_messages = gr.JSON(label="Detected Messages")
detecting_btn.click(
fn=detect,
inputs=[detecting_img, writing_ckb, postit_ckb, corner_ckb, empty_ckb],
outputs=[predicted_messages]
)
if __name__ == '__main__':
demo.launch(server_name="0.0.0.0", server_port=7860, mcp_server=True, ssr_mode=False)
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