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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.predict(source=image_path)
# results = outputs[0].cpu().numpy()
# 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 cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
def show_preds_image(image_path):
# perform inference
image_path = path
results = model(image_path, size=640)
# Results
results.print()
results.xyxy[0] # img1 predictions (tensor)
results.pandas().xyxy[0] # img1 predictions (pandas)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
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="Pothole detector",
examples=path,
cache_examples=False,
)
interface_image.launch() |