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import gradio as gr
from PIL import Image, ImageDraw, ImageFont
import os
from io import BytesIO
from roboflow import Roboflow
roboflow_key = os.getenv("roboflow")
rf = Roboflow(api_key=roboflow_key)
project = rf.workspace('yudi-pratama-putra-rwuep').project("corn-pest-detection-2")
model = project.version(3).model
def predict_image(image, confidence, overlap):
colors = [
"Red",
"Green",
"Blue",
"Yellow",
"Cyan",
"Magenta",
"Orange",
"Purple",
"Brown",
"Pink",
"DarkRed",
"Black",
"White"
]
pest_class = ['aphids', 'army worm', 'black cutworm', 'corn borer', 'grub', 'large cutworm', 'mole cricket', 'peach borer', 'potosiabre vitarsis', 'red spider', 'white margined moth', 'wireworm', 'yellow cutworm']
prediction = model.predict(image, confidence=confidence, overlap=overlap).json()
img = Image.open(image).convert("RGB")
resize_img = 1
img = img.resize((img.width * resize_img, img.height * resize_img))
draw = ImageDraw.Draw(img)
font = ImageFont.load_default(size=20)
for result in prediction['predictions']:
x0 = result['x'] - result['width'] / 2
y0 = result['y'] - result['height'] / 2
x1 = result['x'] + result['width'] / 2
y1 = result['y'] + result['height'] / 2
x0 *= resize_img
y0 *= resize_img
x1 *= resize_img
y1 *= resize_img
for i in range(len(pest_class)):
if result['class'] == pest_class[i]:
draw.rectangle([x0, y0, x1, y1], outline=colors[i], width=3)
label = f"{result['class']} ({result['confidence']*100:.2f}%)"
draw.text((x0, y0 - 25), label, fill=colors[i], font=font)
if prediction['predictions'] == []:
img = Image.open(image)
return img
inputs_image = [
gr.Image(type='filepath', label='input image'),
gr.Slider(minimum=0, maximum=100, value=40, label='Confidence (%)'),
gr.Slider(minimum=0, maximum=100, value=30, label='Overlap (%)')
]
outputs_image = [
gr.Image(type='numpy', label='output image')
]
interface_image = gr.Interface(
fn=predict_image,
inputs=inputs_image,
outputs=outputs_image,
title="Corn Pest Detection",
description=(
"Upload an image and the model will detect pests.\n\n"
"Model detected: aphids, army worm, black cutworm, corn borer, grub, large cutworm, mole cricket, peach borer, "
"potosiabre vitarsis, red spider, white margined moth, wireworm, yellow cutworm.\n\n"
"Confidence: The higher the confidence value, the more certain the model is about the detected object being correct. "
"For example, a higher confidence threshold will filter out less certain predictions.\n\n"
"Overlap: The higher the accepted overlap value, the more predictions are allowed, even if they overlap with each other. "
"A higher overlap value can help detect multiple objects that are close together."
)
)
interface_image.launch(share=True) |