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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(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() |