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  1. app.py +104 -0
  2. best.pt +3 -0
  3. requirements.txt +47 -0
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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+
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+ from ultralytics import YOLO
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+
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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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+
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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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+
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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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+
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+ model = YOLO('best.pt')
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+ path = [['image_0.jpg'], ['image_1.jpg']]
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+ video_path = [['video.mp4']]
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+
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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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+
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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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+
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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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+
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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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+ ]
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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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+ 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="Pothole detector",
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+ examples=video_path,
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+ cache_examples=False,
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+ )
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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()
best.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:ce9a23590d666fcca004d3a2330f3e30b229bbe9df0dbf4b0fd390c20fbe67fe
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+ size 6233272
requirements.txt ADDED
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+ # Ultralytics requirements
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+ # Usage: pip install -r requirements.txt
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+
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+ # Base ----------------------------------------
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+ hydra-core>=1.2.0
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+ matplotlib>=3.2.2
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+ numpy>=1.18.5
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+ opencv-python>=4.1.1
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+ Pillow>=7.1.2
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+ PyYAML>=5.3.1
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+ requests>=2.23.0
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+ scipy>=1.4.1
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+ torch>=1.7.0
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+ torchvision>=0.8.1
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+ tqdm>=4.64.0
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+ ultralytics
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+
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+ # Logging -------------------------------------
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+ tensorboard>=2.4.1
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+ # clearml
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+ # comet
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+
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+ # Plotting ------------------------------------
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+ pandas>=1.1.4
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+ seaborn>=0.11.0
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+
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+ # Export --------------------------------------
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+ # coremltools>=6.0 # CoreML export
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+ # onnx>=1.12.0 # ONNX export
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+ # onnx-simplifier>=0.4.1 # ONNX simplifier
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+ # nvidia-pyindex # TensorRT export
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+ # nvidia-tensorrt # TensorRT export
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+ # scikit-learn==0.19.2 # CoreML quantization
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+ # tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos)
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+ # tensorflowjs>=3.9.0 # TF.js export
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+ # openvino-dev # OpenVINO export
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+
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+ # Extras --------------------------------------
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+ ipython # interactive notebook
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+ psutil # system utilization
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+ thop>=0.1.1 # FLOPs computation
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+ # albumentations>=1.0.3
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+ # pycocotools>=2.0.6 # COCO mAP
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+ # roboflow
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+
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+ # HUB -----------------------------------------
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+ GitPython>=3.1.24