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import gradio as gr
import cv2
import requests
import os
from PIL import Image
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()
def show_preds_image(im, size=640):
g = (size / max(im.size)) # gain
im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize
results = model(im) # inference
results.render() # updates results.imgs with boxes and labels
results.save()
os.system("ls")
return "out.png"
inputs_image = [
gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(type="file", 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() |