Step_3_Usage

Deployment

To use it, you need to install all the libraries from the requirements.txt file, as well as move the signature_detector folder to the root of your project. An example of a simple use can be seen below:

import cv2
from signature_detector import *


image = cv2.imread("001.png")
signature_detector = YoloSignatureDetector(path_to_model='*path to model*.pt')
# predicted = signature_detector.predict(images=cv2.imread("001.png")])  # Single image prediction
predicted = signature_detector.predict(images=[cv2.imread("001.png"), cv2.imread("002.png"), cv2.imread("003.png")])  # Multi image preditcion

To find captions on an image (images), pass images=*your image* or images=[*your images*] as an argument. The result of the method will be List[List[YoloObjectClass]]. YoloObjectClass contains the following properties:

• name - Name of the class label
• confidence - The confidence of the model in a particular answer
• class_id - Id of the class the model is leaning towards
• scaling - The scaling factor of the original image to the images that YOLOv5x works with [640 x 480]
• x_min - The left border of the frame around the signature
• y_min - The top border of the frame around the signature
• x_max - The right border of the frame around the signature
• y_max - The bottom border of the frame around the signature

An example of the simplest Python code for highlighting areas where, according to YOLOv5x, signatures may be located.

import cv2
from signature_detector import *


picture = cv2.imread("001.png")
signature_detector = YoloSignatureDetector(path_to_model='*path to model*.pt')
predicted = signature_detector.predict(images=[picture])
for image in predicted:  # predicted - a list of lists, where the external list is the images, and the internal list is the signatures found on the pictures.
    for signature in image:
        picture = cv2.rectangle(picture,
                                (int(signature.x_min), int(signature.y_min)),
                                (int(signature.x_max), int(signature.y_max)),
                                (255, 0, 0),
                                2)

cv2.imshow("Window", picture)
cv2.waitKey()