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| import gradio as gr | |
| import cv2 | |
| import requests | |
| import os | |
| import torch | |
| import ultralytics | |
| model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5_0.65map_exp7_best.pt", | |
| force_reload=False) | |
| model.conf = 0.20 # NMS confidence threshold | |
| path = [['img/test-image.jpg'], ['img/test-image-2.jpg']] | |
| def show_preds_image(image_path): | |
| image = cv2.imread(image_path) | |
| # outputs = model(source=image_path) | |
| # results = outputs[0].cpu().numpy() | |
| results = model(image_path) | |
| results.xyxy[0] # img1 predictions (tensor) | |
| results.pandas().xyxy[0] # img1 predictions (pandas) | |
| predictions = results.pred[0] | |
| boxes = predictions[:, :4] # x1, y1, x2, y2 | |
| scores = predictions[:, 4] | |
| categories = predictions[:, 5] | |
| # for i, det in enumerate(results.boxes.xyxy): | |
| # cv2.rectangle( | |
| # image, | |
| # (int(det[0]), int(det[1])), | |
| # (int(det[2]), int(det[3])), | |
| # color=(0, 0, 255), | |
| # thickness=2, | |
| # lineType=cv2.LINE_AA | |
| # ) | |
| return results.show() | |
| inputs_image = [ | |
| gr.components.Image(type="filepath", label="Input Image"), | |
| ] | |
| outputs_image = [ | |
| gr.components.Image(type="numpy", label="Output Image"), | |
| ] | |
| interface_image = gr.Interface( | |
| fn=show_preds_image, | |
| inputs=inputs_image, | |
| outputs=outputs_image, | |
| title="Cashew Disease Detection", | |
| examples=path, | |
| cache_examples=False, | |
| ) | |
| interface_image.launch() |