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import gradio as gr
import cv2
import requests
import os
from ultralytics import YOLO
file_urls = ["https://www.dropbox.com/scl/fi/i582tgw95r0f8i8cssmx0/images.jpg?rlkey=fa3d74yaj0bh941jo67n0elns&dl=0"
# 'https://www.dropbox.com/scl/fi/z4tnnills03s1o4evbqpl/download.jpg?rlkey=gmh63kexnnjcva6ahzo2wfmsd&dl=0'
]
def download_file(url, save_name):
url = url
if not os.path.exists(save_name):
file = requests.get(url)
open(save_name, 'wb').write(file.content)
for i, url in enumerate(file_urls):
if 'mp4' in file_urls[i]:
download_file(
file_urls[i],
f"video.mp4"
)
else:
download_file(
file_urls[i],
f"image_{i}.jpg"
)
model = YOLO('best.pt')
path = [['images.jpg'],['images_1.png'],['image/i1.png'],['image/i2.png'],['image/i3.png']]
# ,['image/i4.png'],['image/i5.png'],['image/i6.png'],['image/i7.png'],['image/i8.png'],['image/i9.png'],['image/i10.png'],['image/i11.png'],
# ['image/i12.png'],['image/i13.png'],['image/i14.png'],['image/i15.png'],['image/i16.png'],['image/i17.png'],['image/i18.png'],['image/i19.png'],
# ['image/i20.png'],['image/i21.png'],['image/i22.png'],['image/i23.png'],['image/i24.png'],['image/i25.png'],['image/i26.png'],['image/i27.png'],['image/i28.png']]
# video_path = [['video.mp4']]
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)
inputs_image = [
gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="Airport Luggage Weapon Detector app",
examples=path,
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)
results = outputs[0].cpu().numpy()
for i, det in enumerate(results.boxes.xyxy):
cv2.rectangle(
frame_copy,
(int(det[0]), int(det[1])),
(int(det[2]), int(det[3])),
color=(0, 0, 255),
thickness=2,
lineType=cv2.LINE_AA
)
yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
inputs_video = [
gr.components.Video(),
]
outputs_video = [
gr.components.Image(),
]
interface_video = gr.Interface(
fn=show_preds_video,
inputs=inputs_video,
outputs=outputs_video,
title="Airport Luggage Weapon Detector",
cache_examples=False,
)
MORE = """ ## TRY Other Models
### Instant Image: 4k images in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Image
"""
gr.Markdown(MORE)
gr.TabbedInterface(
[interface_image, interface_video],
tab_names=['Image inference', 'Video inference']
).queue().launch() |