app.py

from ultralytics import YOLO
import gradio as gr
import cv2
import os
import random

model = YOLO('best.pt')


def show_preds_image(image_path):
    image = cv2.imread(image_path)
    outputs = model.predict(source=image_path, conf=0.45, save=True)
    print("output:", outputs)
    results = outputs[0]
    print("results:", results)

    # for i, det in enumerate(results.boxes.xyxy.cpu().numpy()):
    #     cv2.rectangle(
    #         image,
    #         (int(det[0]), int(det[1])),
    #         (int(det[2]), int(det[3])),
    #         color=(random.randint(0,255), random.randint(0,255), random.randint(0,255)),
    #         thickness=2,
    #         lineType=cv2.LINE_AA
    #     )
    return f"runs/detect/predict/{os.path.split(image_path)[-1]}"
 
inputs_image = [
    gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
    gr.components.Image(type="filepath", label="Output Image"),
]
interface_image = gr.Interface(
    fn=show_preds_image,
    inputs=inputs_image,
    outputs=outputs_image,
    title="Cats and Dogs detector",
    cache_examples=False,
)


def show_preds_video(video_path):
    cap = cv2.VideoCapture(video_path)
    while(cap.isOpened()):
        ret, frame = cap.read()
        if ret:
            frame_copy = frame.copy()
            outputs = model.predict(source=frame, conf=0.45)
            results = outputs[0]
            for i, det in enumerate(results.boxes.xyxy.cpu().numpy()):
                cv2.rectangle(
                    frame_copy,
                    (int(det[0]), int(det[1])),
                    (int(det[2]), int(det[3])),
                    color=(random.randint(0,255), random.randint(0,255), random.randint(0,255)),
                    thickness=2,
                    lineType=cv2.LINE_AA
                )
            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
 
inputs_video = [
    gr.components.Video(type="filepath", label="Input Video"),
 
]
outputs_video = [
    gr.components.Image(type="numpy", label="Output Image"),
]
interface_video = gr.Interface(
    fn=show_preds_video,
    inputs=inputs_video,
    outputs=outputs_video,
    title="Cats and Dogs detector",
    cache_examples=False,
)


gr.TabbedInterface(
    [interface_image, interface_video],
    tab_names=['Image inference', 'Video inference']
).queue().launch()