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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| from ultralyticsplus import YOLO, render_result | |
| import cv2 | |
| import numpy as np | |
| from transformers import pipeline | |
| model = YOLO('best (1).pt') | |
| model2 = pipeline('image-classification','Kaludi/csgo-weapon-classification') | |
| name = ['grenade','knife','pistol','rifle'] | |
| # for i, r in enumerate(results): | |
| # # Plot results image | |
| # im_bgr = r.plot() | |
| # im_rgb = im_bgr[..., ::-1] # Convert BGR to RGB | |
| def response(image): | |
| print(image) | |
| results = model(image) | |
| text = "" | |
| name_weap = "" | |
| for r in results: | |
| conf = np.array(r.boxes.conf) | |
| cls = np.array(r.boxes.cls) | |
| cls = cls.astype(int) | |
| xywh = np.array(r.boxes.xywh) | |
| xywh = xywh.astype(int) | |
| for con, cl, xy in zip(conf, cls, xywh): | |
| cone = con.astype(float) | |
| conef = round(cone,3) | |
| conef = conef * 100 | |
| text += (f"Detected {name[cl]} with confidence {round(conef,1)}% at ({xy[0]},{xy[1]})\n") | |
| if cl == 0: | |
| name_weap += name[cl] + '\n' | |
| elif cl == 1: | |
| name_weap += name[cl] + '\n' | |
| elif cl == 2: | |
| out = model2(image) | |
| name_weap += out[0]["label"] + '\n' | |
| elif cl == 3: | |
| out = model2(image) | |
| name_weap += out[0]["label"] + '\n' | |
| # im_rgb = Image.fromarray(im_rgb) | |
| return name_weap, text | |
| def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold: gr.Slider = 0.3, iou_threshold: gr.Slider = 0.6): | |
| results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size) | |
| box = results[0].boxes | |
| render = render_result(model=model, image=image, result=results[0], rect_th = 1, text_th = 1) | |
| weapon_name, text_detection = response(image) | |
| # xywh = int(results.boxes.xywh) | |
| # x = xywh[0] | |
| # y = xywh[1] | |
| return render, text_detection, weapon_name | |
| inputs = [ | |
| gr.Image(type="filepath", label="Input Image"), | |
| gr.Slider(minimum=320, maximum=1280, value=640, | |
| step=32, label="Image Size"), | |
| gr.Slider(minimum=0.0, maximum=1.0, value=0.3, | |
| step=0.05, label="Confidence Threshold"), | |
| gr.Slider(minimum=0.0, maximum=1.0, value=0.6, | |
| step=0.05, label="IOU Threshold"), | |
| ] | |
| outputs = [gr.Image( type="filepath", label="Output Image"), | |
| gr.Textbox(label="Result"), | |
| gr.Textbox(label="Weapon Name") | |
| ] | |
| # examples = [['th (11).jpg', 640, 0.3, 0.6], | |
| # ['th (8).jpg', 640, 0.3, 0.6], | |
| # ['th (3).jpg', 640, 0.3, 0.6], | |
| # ['th.jpg', 640, 0.15, 0.6] | |
| # ] | |
| iface = gr.Interface(fn=response2, inputs=inputs, outputs=outputs) | |
| iface.launch(debug=True) |