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import cv2
import numpy as np
import onnxruntime as ort
import gradio as gr
import os
from huggingface_hub import hf_hub_download
# Model info
REPO_ID = "tech4humans/yolov8s-signature-detector"
FILENAME = "tune/trial_10/weights/best.onnx"
MODEL_DIR = "model"
MODEL_PATH = os.path.join(MODEL_DIR, "model.onnx")
def download_model():
"""Download the model using Hugging Face Hub"""
# Ensure model directory exists
os.makedirs(MODEL_DIR, exist_ok=True)
try:
print(f"Downloading model from {REPO_ID}...")
# Download the model file from Hugging Face Hub
model_path = hf_hub_download(
repo_id=REPO_ID,
filename=FILENAME,
local_dir=MODEL_DIR,
local_dir_use_symlinks=False,
force_download=True,
cache_dir=None
)
# Move the file to the correct location if it's not there already
if os.path.exists(model_path) and model_path != MODEL_PATH:
os.rename(model_path, MODEL_PATH)
# Remove empty directories if they exist
empty_dir = os.path.join(MODEL_DIR, "tune")
if os.path.exists(empty_dir):
import shutil
shutil.rmtree(empty_dir)
print("Model downloaded successfully!")
return MODEL_PATH
except Exception as e:
print(f"Error downloading model: {str(e)}")
raise e
class SignatureDetector:
def __init__(self, model_path):
self.model_path = model_path
self.classes = ["signature"]
self.color_palette = np.random.uniform(0, 255, size=(len(self.classes), 3))
self.input_width = 640
self.input_height = 640
# Initialize ONNX Runtime session
self.session = ort.InferenceSession(MODEL_PATH, providers=["CPUExecutionProvider"])
def preprocess(self, img):
# Convert PIL Image to cv2 format
img_cv2 = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
# Get image dimensions
self.img_height, self.img_width = img_cv2.shape[:2]
# Convert back to RGB for processing
img_rgb = cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB)
# Resize
img_resized = cv2.resize(img_rgb, (self.input_width, self.input_height))
# Normalize and transpose
image_data = np.array(img_resized) / 255.0
image_data = np.transpose(image_data, (2, 0, 1))
image_data = np.expand_dims(image_data, axis=0).astype(np.float32)
return image_data, img_cv2
def draw_detections(self, img, box, score, class_id):
x1, y1, w, h = box
color = self.color_palette[class_id]
cv2.rectangle(img, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2)
label = f"{self.classes[class_id]}: {score:.2f}"
(label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
label_x = x1
label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10
cv2.rectangle(
img,
(int(label_x), int(label_y - label_height)),
(int(label_x + label_width), int(label_y + label_height)),
color,
cv2.FILLED
)
cv2.putText(img, label, (int(label_x), int(label_y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
def postprocess(self, input_image, output, conf_thres, iou_thres):
outputs = np.transpose(np.squeeze(output[0]))
rows = outputs.shape[0]
boxes = []
scores = []
class_ids = []
x_factor = self.img_width / self.input_width
y_factor = self.img_height / self.input_height
for i in range(rows):
classes_scores = outputs[i][4:]
max_score = np.amax(classes_scores)
if max_score >= conf_thres:
class_id = np.argmax(classes_scores)
x, y, w, h = outputs[i][0], outputs[i][1], outputs[i][2], outputs[i][3]
left = int((x - w / 2) * x_factor)
top = int((y - h / 2) * y_factor)
width = int(w * x_factor)
height = int(h * y_factor)
class_ids.append(class_id)
scores.append(max_score)
boxes.append([left, top, width, height])
indices = cv2.dnn.NMSBoxes(boxes, scores, conf_thres, iou_thres)
for i in indices:
box = boxes[i]
score = scores[i]
class_id = class_ids[i]
self.draw_detections(input_image, box, score, class_id)
return cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
def detect(self, image, conf_thres, iou_thres):
# Preprocess the image
img_data, original_image = self.preprocess(image)
# Run inference
outputs = self.session.run(None, {self.session.get_inputs()[0].name: img_data})
# Postprocess the results
output_image = self.postprocess(original_image, outputs, conf_thres, iou_thres)
return output_image
def create_gradio_interface():
# Download model if it doesn't exist
if not os.path.exists(MODEL_PATH):
download_model()
# Initialize the detector
detector = SignatureDetector(MODEL_PATH)
# Create Gradio interface
iface = gr.Interface(
fn=detector.detect,
inputs=[
gr.Image(label="Upload your Document", type="pil"),
gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.05,
label="Confidence Threshold",
info="Adjust the minimum confidence score required for detection"),
gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05,
label="IoU Threshold",
info="Adjust the Intersection over Union threshold for NMS")
],
outputs=gr.Image(label="Detection Results"),
title="Signature Detector",
description="Upload an image to detect signatures using YOLOv8. Use the sliders to adjust detection sensitivity.",
examples=[
["assets/images/example_1.jpg", 0.2, 0.5],
["assets/images/example_2.jpg", 0.2, 0.5],
["assets/images/example_3.jpg", 0.2, 0.5],
["assets/images/example_4.jpg", 0.2, 0.5]
]
)
return iface
if __name__ == "__main__":
iface = create_gradio_interface()
iface.launch() |