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| import os | |
| import gradio as gr | |
| from PIL import Image | |
| import pytesseract | |
| import yolov5 | |
| # load model | |
| model = yolov5.load('keremberke/yolov5m-license-plate') | |
| # set model parameters | |
| model.conf = 0.5 # NMS confidence threshold | |
| model.iou = 0.25 # NMS IoU threshold | |
| model.agnostic = False # NMS class-agnostic | |
| model.multi_label = False # NMS multiple labels per box | |
| model.max_det = 1000 # maximum number of detections per image | |
| def license_plate_detect(img): | |
| results = model(img, size=640) | |
| # parse results | |
| if len(results.pred): | |
| predictions = results.pred[0] | |
| boxes = predictions[:, :4] # x1, y1, x2, y2 | |
| return boxes | |
| def read_license_number(img): | |
| boxes = license_plate_detect(img) | |
| if len(boxes[0]): | |
| image = Image.fromarray(img) | |
| return [pytesseract.image_to_string( | |
| image.crop(bbox.tolist())) | |
| for bbox in boxes] | |
| def greet(img): | |
| boxes = license_plate_detect(img) | |
| image = Image.fromarray(img) | |
| r = 'greet' | |
| if len(boxes[0]): | |
| r = [pytesseract.image_to_string( | |
| image.crop(bbox.tolist())) | |
| for bbox in boxes] | |
| return "Hello " + str(r) + "!!" | |
| iface = gr.Interface(fn=greet, inputs="image", outputs="text") | |
| iface.launch() |