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import gradio as gr
import cv2
import tempfile
from ultralytics import YOLOv10
import pandas as pd
from gradio import processing_utils
df = pd.read_csv('image_class.csv')
df = df[['name', 'class']]
df.drop_duplicates(inplace=True)
print(len(df))
df1 = pd.read_csv('image_class.csv')
df1 = df1[['name', 'class', 'im_file']]
df1['file_name'] = df1['im_file'].apply(lambda v: v.split('_')[-1].split('.')[0])
df1.drop(columns='im_file', inplace=True)
df1.drop_duplicates(inplace=True)
print(df1)
print(len(df1))
def yolov10_inference(image, video, image_size, conf_threshold, iou_threshold):
model = YOLOv10('./drug_yolov10.pt')
# model = YOLOv10('./pills_yolov10.pt')
if image:
results = model.predict(source=image, imgsz=image_size, conf=conf_threshold, iou=iou_threshold)
annotated_image = results[0].plot()
# Print the detected objects' information (class, coordinates, and probability)
box = results[0].boxes
cls = [int(c) for c in box.cls.tolist()]
cnf = [round(f,2) for f in box.conf.tolist()]
clcf = '\n'.join([f'Class:{cls[i]} , Confidence:{cnf[i]*100}%' for i in range(len(cls))]) #list(zip(cls,cnf))
name = '\n'.join([df[df['class']==n]['name'].item() for n in cls])
file_name = image.split('_')[-1].split('.')[0]
print(f'file name: {file_name}')
try:
drug_name = df1[df1['file_name']==file_name]['name'].item()
drug_class = df1[df1['file_name']==file_name]['class'].item()
drug_name = f'{drug_class}, {drug_name}'
print(drug_name)
except:
drug_name = 'No have data'
# print(cls)
# print(name)
# print(type(clcf))
# print("Object type:", box.cls)
# print("Coordinates:", box.xyxy)
# print("Probability:", box.conf)
# print('box.class data tyupe', type(box.cls.tolist()))
return annotated_image[:, :, ::-1], None, clcf, name, file_name, drug_name
else:
video_path = tempfile.mktemp(suffix=".webm")
with open(video_path, "wb") as f:
with open(video, "rb") as g:
f.write(g.read())
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
output_video_path = tempfile.mktemp(suffix=".webm")
out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'vp80'), fps, (frame_width, frame_height))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model.predict(source=frame, imgsz=image_size, conf=conf_threshold, iou=iou_threshold)
annotated_frame = results[0].plot()
out.write(annotated_frame)
cap.release()
out.release()
return None, output_video_path
def yolov10_inference_for_examples(image, image_size, conf_threshold, iou_threshold):
annotated_image, _, output_class, output_name = yolov10_inference(image, None, image_size, conf_threshold, iou_threshold)
return annotated_image#, None, output_class, output_name
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
# image = gr.Image(type="pil", label="Image", visible=True)
image = gr.Image(type="filepath", label="Image", visible=True)
video = gr.Video(label="Video", visible=False)
input_type = gr.Radio(
choices=["Image", "Video"],
value="Image",
label="Input Type",
)
file_name = gr.Textbox(label='File Name')
file_name.change(outputs=file_name)
drug_name = gr.Textbox(label='Drug Name (Ground Truth)')
drug_name.change(outputs=drug_name)
image_size = gr.Slider(
label="Image Size",
minimum=0,
maximum=1280,
step=10,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.25,
)
iou_threshold = gr.Slider(
label="IOU Threshold",
minimum=0,
maximum=1,
step=0.1,
value=0.6,
)
yolov10_infer = gr.Button(value="Detect Objects")
with gr.Column():
output_image = gr.Image(type="numpy", label="Annotated Image", visible=True)
output_video = gr.Video(label="Annotated Video", visible=False)
output_name = gr.Textbox(label='Predicted Drug Name')
output_name.change(outputs=output_name)
output_class = gr.Textbox(label='Predicted Class')
output_class.change(outputs=output_class)
def update_visibility(input_type):
image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
output_image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
output_video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
print(f'updated image: {image}')
return image, video, output_image, output_video
input_type.change(
fn=update_visibility,
inputs=[input_type],
outputs=[image, video, output_image, output_video],
)
def run_inference(image, video, image_size, conf_threshold, iou_threshold, input_type):
if input_type == "Image":
return yolov10_inference(image, None, image_size, conf_threshold, iou_threshold)
else:
return yolov10_inference(None, video, image_size, conf_threshold, iou_threshold)
yolov10_infer.click(
fn=run_inference,
inputs=[image, video, image_size, conf_threshold, iou_threshold, input_type],
outputs=[output_image, output_video, output_class, output_name, file_name, drug_name],
)
gr.Examples(
examples = [
['./RXBASE-600_00071-1014-68_NLMIMAGE10_5715ABFD.jpg', 280, 0.2, 0.6],
['./RXNAV-600_13668-0095-90_RXNAVIMAGE10_D145E8EF.jpg', 640, 0.2, 0.7],
['./RXBASE-600_00074-7126-13_NLMIMAGE10_C003606B.jpg', 640, 0.2, 0.8],
],
fn=yolov10_inference_for_examples,
inputs=[
image,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_image],
cache_examples='lazy',
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10: Real-Time End-to-End Object Detection
</h1>
""")
gr.HTML(
"""
<h3 style='text-align: center'>
<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
</h3>
""")
with gr.Row():
with gr.Column():
app()
if __name__ == '__main__':
gradio_app.launch()
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