initial commit

.gitignore

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+flagged/
+*.pt
+*.png
+*.jpg
+*.mp4
+*.mkv
+gradio_cached_examples/

app.py

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+import gradio as gr
+import cv2
+import requests
+import os
+
+from ultralytics import YOLO
+
+file_urls = [
+ 'https://drive.google.com/file/d/1EZRZOvno2LeHVgBzDd1yGSKZcHpjvazT/view?usp=drive_link',
+ 'https://drive.google.com/file/d/1WHLbEZPCzYHTfvFeH2r58Au5fqz_n5rj/view?usp=drive_link',
+ 'https://drive.google.com/file/d/1RUpwU3Qe4Q2DSfu6bE9OEZiDfI--a_8y/view?usp=drive_link'
+
+]
+
+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 = [['image_0.jpg'], ['image_1.jpg']]
+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="Numberplate detector",
+ 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(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="Numberplate detector",
+ examples=video_path,
+ cache_examples=False,
+)
+
+gr.TabbedInterface(
+ [interface_image, interface_video],
+ tab_names=['Image inference', 'Video inference']
+).queue().launch()

requirements.txt

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+# Ultralytics requirements
+# Usage: pip install -r requirements.txt
+
+# Base ----------------------------------------
+hydra-core>=1.2.0
+matplotlib>=3.2.2
+numpy>=1.18.5
+opencv-python>=4.1.1
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0
+torchvision>=0.8.1
+tqdm>=4.64.0
+ultralytics
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# clearml
+# comet
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=6.0 # CoreML export
+# onnx>=1.12.0 # ONNX export
+# onnx-simplifier>=0.4.1 # ONNX simplifier
+# nvidia-pyindex # TensorRT export
+# nvidia-tensorrt # TensorRT export
+# scikit-learn==0.19.2 # CoreML quantization
+# tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos)
+# tensorflowjs>=3.9.0 # TF.js export
+# openvino-dev # OpenVINO export
+
+# Extras --------------------------------------
+ipython # interactive notebook
+psutil # system utilization
+thop>=0.1.1 # FLOPs computation
+# albumentations>=1.0.3
+# pycocotools>=2.0.6 # COCO mAP
+# roboflow
+
+# HUB -----------------------------------------
+GitPython>=3.1.24