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import os
import gradio as gr
import cv2
from ultralytics import YOLO
"""
input_dir = '/home/student/PycharmProjects/pythonProjectYoloObjectPredict/test_images'
output_dir = '/home/student/PycharmProjects/pythonProjectYoloObjectPredict/'
image_files = [f for f in os.listdir(input_dir) if f.lower().endswith('.jpg')]
for image_file in image_files:
image_path = os.path.join(input_dir, image_file)
output_path = os.path.join(output_dir, f'{os.path.splitext(image_file)[0]}_result.jpg')
results = model.predict(image_path, conf=0.25, save=True, save_crop=True)
print(f'Predictions for {image_file}: {results}')
"""
# Provide the directory path where your images are located
image_directory = '/content/drive/MyDrive/Work/yolo/images/val'
jpg_files = [file for file in os.listdir(image_directory) if file.lower().endswith('.jpg')]
# Create a list of full paths to the JPG files
path = [os.path.join(image_directory, filename) for filename in jpg_files]
model = YOLO('/content/drive/MyDrive/Work/yolo/best.pt')
inputs_image = [
gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(type="numpy", label="Output Image"),
]
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)
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="Floor Plan Detector",
examples=path,
cache_examples=False,
)
gr.TabbedInterface(
[interface_image],
tab_names=['Image Inference'],
).queue().launch(debug=True) |