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from PIL import Image
import gradio as gr
import cv2
from ultralytics import ASSETS, YOLO
import tempfile
import numpy as np
import time
def load_model(model_name):
"""Loads the specified YOLO model for either segmentation or detection."""
if model_name == "yolov9c-seg":
model_path = "yolov9c-seg.pt"
elif model_name == "yolov9e-seg":
model_path = "yolov9e-seg.pt"
elif model_name == "yolov9c":
model_path = "yolov9c.pt"
elif model_name == "yolov9e":
model_path = "yolov9e.pt"
elif model_name == "yolov8n":
model_path = "yolov8n.pt"
elif model_name == "yolov8n-seg":
model_path = "yolov8n-seg.pt"
else:
raise ValueError(f"Invalid model name: {model_name}")
return YOLO(model_path)
def predict_image(img, conf_threshold, iou_threshold, task="detection", model_name=None):
"""Predicts and plots results in an image using YOLO model with adjustable confidence and IOU thresholds."""
if task == "segmentation":
if not model_name:
model_name = "yolov9c-seg"
elif model_name not in ["yolov9c-seg", "yolov9e-seg", "yolov8n-seg"]:
raise ValueError(f"Invalid model name for segmentation: {model_name}")
elif task == "detection":
if not model_name:
model_name = "yolov9c"
elif model_name not in ["yolov9c", "yolov9e", "yolov8n"]:
raise ValueError(f"Invalid model name for detection: {model_name}")
else:
raise ValueError(f"Invalid task: {task}. Choose either 'segmentation' or 'detection'.")
model = load_model(model_name)
results = model.predict(
source=img,
conf=conf_threshold,
iou=iou_threshold,
show_labels=True,
show_conf=True,
imgsz=640,
)
for r in results:
im_array = r.plot()
im = Image.fromarray(im_array[..., ::-1])
return im
def predict_image_with_task(img, conf_threshold, iou_threshold, task, model_name):
return predict_image(img, conf_threshold, iou_threshold, task, model_name)
image_iface = gr.Interface(
fn=predict_image_with_task,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
gr.Dropdown(choices=["detection", "segmentation"], value="detection", label="Task"),
gr.Dropdown(choices=["yolov9c", "yolov9e", "yolov8n", "yolov9c-seg", "yolov9e-seg", "yolov8n-seg"], value="yolov9c", label="Model"),
],
outputs=gr.Image(type="pil", label="Result"),
title="X509",
description="Upload images for inference. Choose task and corresponding model.",
examples=[
["cars.jpg", 0.25, 0.45, "detection", "yolov9c"],
["cars.jpg", 0.25, 0.45, "segmentation", "yolov9c-seg"],
],
)
def predict_video(video_path, conf_threshold, iou_threshold, task="detection", model_name=None):
"""Predicts and processes video frames using YOLO model with adjustable confidence and IOU thresholds."""
if task == "segmentation":
if not model_name:
model_name = "yolov9c-seg"
elif model_name not in ["yolov9c-seg", "yolov9e-seg", "yolov8n-seg"]:
raise ValueError(f"Invalid model name for segmentation: {model_name}")
elif task == "detection":
if not model_name:
model_name = "yolov9c"
elif model_name not in ["yolov9c", "yolov9e", "yolov8n"]:
raise ValueError(f"Invalid model name for detection: {model_name}")
else:
raise ValueError(f"Invalid task: {task}. Choose either 'segmentation' or 'detection'.")
model = load_model(model_name)
cap = cv2.VideoCapture(video_path)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
temp_video_path = tempfile.mktemp(suffix=".mp4")
out = cv2.VideoWriter(temp_video_path, fourcc, fps, (width, height))
frame_count = 0
start_time = time.time()
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_count += 1
elapsed_time = time.time() - start_time
current_fps = frame_count / elapsed_time
pil_img = Image.fromarray(frame[..., ::-1])
results = model.predict(
source=pil_img,
conf=conf_threshold,
iou=iou_threshold,
show_labels=True,
show_conf=True,
imgsz=640,
)
for r in results:
im_array = r.plot()
processed_frame = Image.fromarray(im_array[..., ::-1])
frame = cv2.cvtColor(np.array(processed_frame), cv2.COLOR_RGB2BGR)
cv2.putText(frame, f"FPS: {current_fps:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
out.write(frame)
cap.release()
out.release()
return temp_video_path
def predict_video_with_task(video_path, conf_threshold, iou_threshold, task, model_name):
return predict_video(video_path, conf_threshold, iou_threshold, task, model_name)
video_iface = gr.Interface(
fn=predict_video_with_task,
inputs=[
gr.Video(label="Upload Video", interactive=True),
gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
gr.Dropdown(choices=["detection", "segmentation"], value="detection", label="Task"),
gr.Dropdown(choices=["yolov9c", "yolov9e", "yolov8n", "yolov9c-seg", "yolov9e-seg", "yolov8n-seg"], value="yolov9c", label="Model"),
],
outputs=gr.File(label="Result"),
title="X509",
description="Upload video for inference. Choose task and corresponding model.",
examples=[
["VID_20240517112011.mp4", 0.25, 0.45, "detection", "yolov8n"],
["VID_20240517112011.mp4", 0.25, 0.45, "segmentation", "yolov8n-seg"],
]
)
production = gr.TabbedInterface([image_iface, video_iface], ["Image Inference", "Video Inference"])
if __name__ == '__main__':
production.launch() |