Commit
β’
934ba8d
1
Parent(s):
2c715b9
Create app.py
Browse files
app.py
ADDED
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import gradio as gr
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import cv2
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import requests
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import os
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from ultralyticsplus import YOLO, render_result
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file_urls = [
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'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1',
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'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1',
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'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
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]
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def download_file(url, save_name):
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url = url
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if not os.path.exists(save_name):
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file = requests.get(url)
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open(save_name, 'wb').write(file.content)
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for i, url in enumerate(file_urls):
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if 'mp4' in file_urls[i]:
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download_file(
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file_urls[i],
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f"video.mp4"
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)
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else:
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download_file(
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file_urls[i],
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f"image_{i}.jpg"
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)
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model = YOLO('foduucom/stockmarket-pattern-detection-yolov8')
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path = [['image_0.jpg'], ['image_1.jpg']]
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video_path = [['video.mp4']]
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def show_preds_image(image_path):
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image = cv2.imread(image_path)
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outputs = model.predict(source=image_path)
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results = outputs[0].cpu().numpy()
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for i, det in enumerate(results.boxes.xyxy):
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cv2.rectangle(
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image,
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(int(det[0]), int(det[1])),
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(int(det[2]), int(det[3])),
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color=(0, 0, 255),
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thickness=2,
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lineType=cv2.LINE_AA
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)
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return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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inputs_image = [
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gr.components.Image(type="filepath", label="Input Image"),
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]
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outputs_image = [
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gr.components.Image(type="numpy", label="Output Image"),
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]
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interface_image = gr.Interface(
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fn=show_preds_image,
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inputs=inputs_image,
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outputs=outputs_image,
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title="Pothole detector app",
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examples=path,
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cache_examples=False,
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)
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def show_preds_video(video_path):
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cap = cv2.VideoCapture(video_path)
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while(cap.isOpened()):
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ret, frame = cap.read()
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if ret:
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frame_copy = frame.copy()
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outputs = model.predict(source=frame)
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results = outputs[0].cpu().numpy()
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for i, det in enumerate(results.boxes.xyxy):
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cv2.rectangle(
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frame_copy,
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(int(det[0]), int(det[1])),
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(int(det[2]), int(det[3])),
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color=(0, 0, 255),
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thickness=2,
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lineType=cv2.LINE_AA
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)
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yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
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inputs_video = [
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gr.components.Video(type="filepath", label="Input Video"),
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]
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outputs_video = [
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gr.components.Image(type="numpy", label="Output Image"),
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]
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description=""" π―οΈ Introducing CandleScan by Foduu AI π―οΈ
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Unleash the power of precise pattern recognition with CandleScan, your ultimate companion for deciphering intricate candlestick formations in the world of trading. ππ
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Unlock the secrets of successful trading by effortlessly identifying crucial candlestick patterns such as 'Head and Shoulders Bottom', 'Head and Shoulders Top', 'M-Head', 'StockLine', 'Triangle', and 'W-Bottom'. ππ
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Powered by the cutting-edge technology of Foduu AI, CandleScan is your expert guide to navigating the complexities of the market. Whether you're an experienced trader or a novice investor, our app empowers you to make informed decisions with confidence. πΌπ°
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But that's not all! CandleScan is just the beginning. If you're hungry for more pattern recognition prowess, simply reach out to us at info@foddu.com. Our dedicated team is ready to assist you in expanding your trading horizons by integrating additional pattern recognition features. π¬π²
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Show your appreciation for this space-age tool by hitting the 'Like' button and start embarking on a journey towards trading mastery with CandleScan! ππ―οΈπ
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π§ Contact us: info@foddu.com
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π Like | """
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interface_video = gr.Interface(
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fn=show_preds_video,
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inputs=inputs_video,
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outputs=outputs_video,
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title="CandleStickScan: Pattern Recognition for Trading Success",
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descripiton=description,
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examples=video_path,
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cache_examples=False,
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)
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gr.TabbedInterface(
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[interface_image, interface_video],
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tab_names=['Image inference', 'Video inference']
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).queue().launch()
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