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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() |