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| import gradio as gr | |
| import requests | |
| from PIL import Image | |
| import io | |
| import cv2 | |
| import numpy as np | |
| alpr_count = 0 | |
| def plot_one_box(x, img, color=None, label=None, score=None, line_thickness=3): | |
| # Plots one bounding box on image img | |
| tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness | |
| color = color | |
| c1, c2 = (int(x[0]), int(x[1])), (int(x[2])+int(x[0]), int(x[3])+int(x[1])) | |
| cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) | |
| if label: | |
| tf = max(tl - 1, 1) # font thickness | |
| t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] | |
| c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 | |
| cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled | |
| cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [0, 0, 0], thickness=tf, lineType=cv2.LINE_AA) | |
| pro = f"{score:.3f}" | |
| t_size = cv2.getTextSize(pro, 0, fontScale=tl / 3, thickness=tf)[0] | |
| c1 = c2 | |
| c2 = c1[0] + t_size[0], c1[1] + t_size[1] + 3 | |
| cv2.rectangle(img, c1, c2, [0, 255, 255], -1, cv2.LINE_AA) # filled | |
| cv2.putText(img, pro, (c1[0], c2[1] - 2), 0, tl / 3, [0, 0, 0], thickness=tf, lineType=cv2.LINE_AA) | |
| return img | |
| def alpr(frame): | |
| global alpr_count | |
| alpr_count = alpr_count + 1 | |
| print("alpr_count", alpr_count) | |
| url = "http://127.0.0.1:8080/alpr" | |
| file = {'file': open(frame, 'rb')} | |
| r = requests.post(url=url, files=file) | |
| alpr_output = None | |
| result = r.json().get('result') | |
| plate_number = r.json().get('plate number') | |
| box = r.json().get('coordinate') | |
| pro = r.json().get('score') | |
| # print("\n number: ", plate_number) | |
| # print("\n coordinate: ", box) | |
| # print("\n score: ", pro) | |
| try: | |
| image = cv2.imread(frame, cv2.IMREAD_COLOR) | |
| if image is None: | |
| print('image is null') | |
| sys.exit() | |
| image = cv2.resize(image, (1024, 640)) | |
| for alpr in plate_number: | |
| # print(plate_number) | |
| image = plot_one_box(box[alpr], image, label=plate_number[alpr], score=pro[alpr], color=[0, 255, 0], line_thickness=1) | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| alpr_output = image.copy() | |
| except: | |
| pass | |
| return alpr_output | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """ | |
| # KBY-AI License Plate Recognition | |
| We offer SDKs for face recognition, liveness detection(anti-spoofing), ID card recognition and ID document liveness detection. | |
| We also specialize in providing outsourcing services with a variety of technical stacks like AI(Computer Vision/Machine Learning), mobile apps, and web apps. | |
| ##### KYC Verification Demo - https://github.com/kby-ai/KYC-Verification-Demo-Android | |
| ##### ID Capture Web Demo - https://cap.kby-ai.com | |
| """ | |
| ) | |
| with gr.TabItem("License Plate Recognition"): | |
| gr.Markdown( | |
| """ | |
| ##### Docker Hub - https://hub.docker.com/r/kbyai/license-plate-recognition | |
| ```bash | |
| sudo docker pull kbyai/license-plate-recognition:latest | |
| sudo docker run -v ./license.txt:/home/openvino/kby-ai-alpr/license.txt -p 8081:8080 -p 9001:9000 kbyai/license-plate-recognition:latest | |
| ``` | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| alpr_image_input = gr.Image(type='filepath', height=300) | |
| gr.Examples(['alpr_examples/test4.jpeg', 'alpr_examples/test5.jpeg', 'alpr_examples/test3.jpg', 'alpr_examples/test2.jpg'], | |
| inputs=alpr_image_input) | |
| alpr_confirmation_button = gr.Button("Confirm") | |
| with gr.Column(): | |
| alpr_output = gr.Image(type="numpy") | |
| alpr_confirmation_button.click(alpr, inputs=alpr_image_input, outputs=alpr_output) | |
| gr.HTML('<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fweb.kby-ai.com%2F"><img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fweb.kby-ai.com%2F&label=VISITORS&countColor=%23263759" /></a>') | |
| demo.launch(server_name="0.0.0.0", server_port=7860) |