forked from whybeyoung/Gradio_YOLOv5_Det
Duplicate from Gradio-Blocks/Gradio_YOLOv5_Det
Co-authored-by: ZengYifu <Zengyf-CVer@users.noreply.huggingface.co>
This commit is contained in:
system
commit
a882a63b6d
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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# 图片格式
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*.jpg
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*.jpeg
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*.png
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*.svg
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*.gif
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# 视频格式
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*.mp4
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*.avi
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.ipynb_checkpoints
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*/__pycache__
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# 日志格式
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*.log
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*.datas
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*.txt
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# 生成文件
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*.pdf
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*.xlsx
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*.csv
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# 参数文件
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*.yaml
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*.json
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# 压缩文件格式
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*.zip
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*.tar
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*.tar.gz
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*.rar
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# 字体格式
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*.ttc
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*.ttf
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*.otf
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# 模型文件
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*.pt
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*.db
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/flagged
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/run
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!requirements.txt
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!cls_name/*
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!model_config/*
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!img_example/*
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!packages.txt
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app copy.py
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---
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title: Gradio_YOLOv5_Det
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emoji: 🚀
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colorFrom: red
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colorTo: red
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sdk: gradio
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sdk_version: 3.0.9
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app_file: app.py
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pinned: true
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license: gpl-3.0
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duplicated_from: Gradio-Blocks/Gradio_YOLOv5_Det
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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🚀 Project homepage：https://gitee.com/CV_Lab/gradio_yolov5_det
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__author__ = "曾逸夫（Zeng Yifu）"
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__email__ = "zyfiy1314@163.com"
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# Gradio YOLOv5 Det v0.4
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# author: Zeng Yifu（曾逸夫）
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# creation time: 2022-05-28
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# email: zyfiy1314@163.com
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# project homepage: https://gitee.com/CV_Lab/gradio_yolov5_det
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import argparse
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import csv
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import gc
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import json
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import os
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import sys
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from collections import Counter
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from pathlib import Path
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import cv2
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import gradio as gr
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import numpy as np
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import pandas as pd
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import torch
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import yaml
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from PIL import Image, ImageDraw, ImageFont
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from util.fonts_opt import is_fonts
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from util.pdf_opt import pdf_generate
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ROOT_PATH = sys.path[0] # root directory
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# model path
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model_path = "ultralytics/yolov5"
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# Gradio YOLOv5 Det version
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GYD_VERSION = "Gradio YOLOv5 Det v0.4"
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# model name temporary variable
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model_name_tmp = ""
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# Device temporary variables
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device_tmp = ""
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# File extension
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suffix_list = [".csv", ".yaml"]
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# font size
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FONTSIZE = 25
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# object style
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obj_style = ["Small Object", "Medium Object", "Large Object"]
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def parse_args(known=False):
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parser = argparse.ArgumentParser(description="Gradio YOLOv5 Det v0.4")
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parser.add_argument("--source", "-src", default="upload", type=str, help="input source")
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parser.add_argument("--source_video", "-src_v", default="webcam", type=str, help="video input source")
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parser.add_argument("--img_tool", "-it", default="editor", type=str, help="input image tool")
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parser.add_argument("--model_name", "-mn", default="yolov5s", type=str, help="model name")
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parser.add_argument(
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"--model_cfg",
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"-mc",
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default="./model_config/model_name_p5_p6_all.yaml",
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type=str,
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help="model config",
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)
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parser.add_argument(
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"--cls_name",
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"-cls",
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default="./cls_name/cls_name_en.yaml",
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type=str,
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help="cls name",
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)
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parser.add_argument(
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"--nms_conf",
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"-conf",
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default=0.5,
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type=float,
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help="model NMS confidence threshold",
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)
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parser.add_argument("--nms_iou", "-iou", default=0.45, type=float, help="model NMS IoU threshold")
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parser.add_argument(
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"--device",
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"-dev",
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default="cpu",
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type=str,
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help="cuda or cpu",
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)
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parser.add_argument("--inference_size", "-isz", default=640, type=int, help="model inference size")
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parser.add_argument("--max_detnum", "-mdn", default=50, type=float, help="model max det num")
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parser.add_argument("--slider_step", "-ss", default=0.05, type=float, help="slider step")
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parser.add_argument(
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"--is_login",
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"-isl",
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action="store_true",
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default=False,
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help="is login",
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)
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parser.add_argument('--usr_pwd',
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"-up",
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nargs='+',
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type=str,
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default=["admin", "admin"],
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help="user & password for login")
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parser.add_argument(
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"--is_share",
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"-is",
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action="store_true",
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default=False,
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|
help="is login",
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)
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args = parser.parse_known_args()[0] if known else parser.parse_args()
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|
return args
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|
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|
# yaml file parsing
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def yaml_parse(file_path):
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return yaml.safe_load(open(file_path, encoding="utf-8").read())
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# yaml csv file parsing
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def yaml_csv(file_path, file_tag):
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file_suffix = Path(file_path).suffix
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|
if file_suffix == suffix_list[0]:
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# model name
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file_names = [i[0] for i in list(csv.reader(open(file_path)))] # csv version
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elif file_suffix == suffix_list[1]:
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# model name
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file_names = yaml_parse(file_path).get(file_tag) # yaml version
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else:
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print(f"{file_path} is not in the correct format! Program exits!")
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sys.exit()
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return file_names
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# model loading
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def model_loading(model_name, device, opt=[]):
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# 加载本地模型
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|
try:
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# load model
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model = torch.hub.load(model_path,
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model_name,
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force_reload=[True if "refresh_yolov5" in opt else False][0],
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device=device,
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_verbose=False)
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except Exception as e:
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print(e)
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else:
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print(f"🚀 welcome to {GYD_VERSION}，{model_name} loaded successfully!")
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return model
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# check information
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def export_json(results, img_size):
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return [[{
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"ID": i,
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"CLASS": int(result[i][5]),
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"CLASS_NAME": model_cls_name_cp[int(result[i][5])],
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"BOUNDING_BOX": {
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"XMIN": round(result[i][:4].tolist()[0], 6),
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"YMIN": round(result[i][:4].tolist()[1], 6),
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"XMAX": round(result[i][:4].tolist()[2], 6),
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"YMAX": round(result[i][:4].tolist()[3], 6),},
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"CONF": round(float(result[i][4]), 2),
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"FPS": round(1000 / float(results.t[1]), 2),
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"IMG_WIDTH": img_size[0],
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"IMG_HEIGHT": img_size[1],} for i in range(len(result))] for result in results.xyxyn]
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# frame conversion
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def pil_draw(img, countdown_msg, textFont, xyxy, font_size, opt, obj_cls_index, color_list):
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img_pil = ImageDraw.Draw(img)
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img_pil.rectangle(xyxy, fill=None, outline=color_list[obj_cls_index]) # bounding box
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if "label" in opt:
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text_w, text_h = textFont.getsize(countdown_msg) # Label size
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img_pil.rectangle(
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(xyxy[0], xyxy[1], xyxy[0] + text_w, xyxy[1] + text_h),
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fill=color_list[obj_cls_index],
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outline=color_list[obj_cls_index],
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) # label background
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img_pil.multiline_text(
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(xyxy[0], xyxy[1]),
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countdown_msg,
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fill=(255, 255, 255),
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font=textFont,
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align="center",
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)
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return img
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# Label and bounding box color settings
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def color_set(cls_num):
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color_list = []
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for i in range(cls_num):
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color = tuple(np.random.choice(range(256), size=3))
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# color = ["#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)])]
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color_list.append(color)
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return color_list
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# YOLOv5 image detection function
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def yolo_det_img(img, device, model_name, infer_size, conf, iou, max_num, model_cls, opt):
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global model, model_name_tmp, device_tmp
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# object size num
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s_obj, m_obj, l_obj = 0, 0, 0
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# object area list
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area_obj_all = []
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# cls num stat
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cls_det_stat = []
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|
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||||||
|
if model_name_tmp != model_name:
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||||||
|
# Model judgment to avoid repeated loading
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||||||
|
model_name_tmp = model_name
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||||||
|
print(f"Loading model {model_name_tmp}......")
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||||||
|
model = model_loading(model_name_tmp, device, opt)
|
||||||
|
elif device_tmp != device:
|
||||||
|
# Device judgment to avoid repeated loading
|
||||||
|
device_tmp = device
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||||||
|
print(f"Loading model {model_name_tmp}......")
|
||||||
|
model = model_loading(model_name_tmp, device, opt)
|
||||||
|
else:
|
||||||
|
print(f"Loading model {model_name_tmp}......")
|
||||||
|
model = model_loading(model_name_tmp, device, opt)
|
||||||
|
|
||||||
|
# -------------Model tuning -------------
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||||||
|
model.conf = conf # NMS confidence threshold
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||||||
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model.iou = iou # NMS IoU threshold
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||||||
|
model.max_det = int(max_num) # Maximum number of detection frames
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||||||
|
model.classes = model_cls # model classes
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||||||
|
|
||||||
|
color_list = color_set(len(model_cls_name_cp)) # 设置颜色
|
||||||
|
|
||||||
|
img_size = img.size # frame size
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||||||
|
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||||||
|
results = model(img, size=infer_size) # detection
|
||||||
|
|
||||||
|
# ----------------目标裁剪----------------
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||||||
|
crops = results.crop(save=False)
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||||||
|
img_crops = []
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||||||
|
for i in range(len(crops)):
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||||||
|
img_crops.append(crops[i]["im"][..., ::-1])
|
||||||
|
|
||||||
|
# Data Frame
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||||||
|
dataframe = results.pandas().xyxy[0].round(2)
|
||||||
|
|
||||||
|
det_csv = "./Det_Report.csv"
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||||||
|
det_excel = "./Det_Report.xlsx"
|
||||||
|
|
||||||
|
if "csv" in opt:
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||||||
|
dataframe.to_csv(det_csv, index=False)
|
||||||
|
else:
|
||||||
|
det_csv = None
|
||||||
|
|
||||||
|
if "excel" in opt:
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||||||
|
dataframe.to_excel(det_excel, sheet_name='sheet1', index=False)
|
||||||
|
else:
|
||||||
|
det_excel = None
|
||||||
|
|
||||||
|
# ----------------Load fonts----------------
|
||||||
|
yaml_index = cls_name.index(".yaml")
|
||||||
|
cls_name_lang = cls_name[yaml_index - 2:yaml_index]
|
||||||
|
|
||||||
|
if cls_name_lang == "zh":
|
||||||
|
# Chinese
|
||||||
|
textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/SimSun.ttf"), size=FONTSIZE)
|
||||||
|
elif cls_name_lang in ["en", "ru", "es", "ar"]:
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||||||
|
# English, Russian, Spanish, Arabic
|
||||||
|
textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/TimesNewRoman.ttf"), size=FONTSIZE)
|
||||||
|
elif cls_name_lang == "ko":
|
||||||
|
# Korean
|
||||||
|
textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/malgun.ttf"), size=FONTSIZE)
|
||||||
|
|
||||||
|
for result in results.xyxyn:
|
||||||
|
for i in range(len(result)):
|
||||||
|
id = int(i) # instance ID
|
||||||
|
obj_cls_index = int(result[i][5]) # category index
|
||||||
|
obj_cls = model_cls_name_cp[obj_cls_index] # category
|
||||||
|
cls_det_stat.append(obj_cls)
|
||||||
|
|
||||||
|
# ------------ border coordinates ------------
|
||||||
|
x0 = float(result[i][:4].tolist()[0])
|
||||||
|
y0 = float(result[i][:4].tolist()[1])
|
||||||
|
x1 = float(result[i][:4].tolist()[2])
|
||||||
|
y1 = float(result[i][:4].tolist()[3])
|
||||||
|
|
||||||
|
# ------------ Actual coordinates of the border ------------
|
||||||
|
x0 = int(img_size[0] * x0)
|
||||||
|
y0 = int(img_size[1] * y0)
|
||||||
|
x1 = int(img_size[0] * x1)
|
||||||
|
y1 = int(img_size[1] * y1)
|
||||||
|
|
||||||
|
conf = float(result[i][4]) # confidence
|
||||||
|
# fps = f"{(1000 / float(results.t[1])):.2f}" # FPS
|
||||||
|
|
||||||
|
det_img = pil_draw(
|
||||||
|
img,
|
||||||
|
f"{id}-{obj_cls}:{conf:.2f}",
|
||||||
|
textFont,
|
||||||
|
[x0, y0, x1, y1],
|
||||||
|
FONTSIZE,
|
||||||
|
opt,
|
||||||
|
obj_cls_index,
|
||||||
|
color_list,
|
||||||
|
)
|
||||||
|
|
||||||
|
# ----------add object size----------
|
||||||
|
w_obj = x1 - x0
|
||||||
|
h_obj = y1 - y0
|
||||||
|
area_obj = w_obj * h_obj
|
||||||
|
area_obj_all.append(area_obj)
|
||||||
|
|
||||||
|
# ------------JSON generate------------
|
||||||
|
det_json = export_json(results, img.size)[0] # Detection information
|
||||||
|
det_json_format = json.dumps(det_json, sort_keys=False, indent=4, separators=(",", ":"),
|
||||||
|
ensure_ascii=False) # JSON formatting
|
||||||
|
|
||||||
|
if "json" not in opt:
|
||||||
|
det_json = None
|
||||||
|
|
||||||
|
# -------PDF generate-------
|
||||||
|
report = "./Det_Report.pdf"
|
||||||
|
if "pdf" in opt:
|
||||||
|
pdf_generate(f"{det_json_format}", report, GYD_VERSION)
|
||||||
|
else:
|
||||||
|
report = None
|
||||||
|
|
||||||
|
# --------------object size compute--------------
|
||||||
|
for i in range(len(area_obj_all)):
|
||||||
|
if (0 < area_obj_all[i] <= 32 ** 2):
|
||||||
|
s_obj = s_obj + 1
|
||||||
|
elif (32 ** 2 < area_obj_all[i] <= 96 ** 2):
|
||||||
|
m_obj = m_obj + 1
|
||||||
|
elif (area_obj_all[i] > 96 ** 2):
|
||||||
|
l_obj = l_obj + 1
|
||||||
|
|
||||||
|
sml_obj_total = s_obj + m_obj + l_obj
|
||||||
|
|
||||||
|
objSize_dict = {obj_style[i]: [s_obj, m_obj, l_obj][i] / sml_obj_total for i in range(3)}
|
||||||
|
|
||||||
|
# ------------cls stat------------
|
||||||
|
clsRatio_dict = {}
|
||||||
|
clsDet_dict = Counter(cls_det_stat)
|
||||||
|
clsDet_dict_sum = sum(clsDet_dict.values())
|
||||||
|
|
||||||
|
for k, v in clsDet_dict.items():
|
||||||
|
clsRatio_dict[k] = v / clsDet_dict_sum
|
||||||
|
|
||||||
|
return det_img, img_crops, objSize_dict, clsRatio_dict, dataframe, det_json, report, det_csv, det_excel
|
||||||
|
|
||||||
|
|
||||||
|
# YOLOv5 video detection function
|
||||||
|
def yolo_det_video(video, device, model_name, infer_size, conf, iou, max_num, model_cls, opt):
|
||||||
|
|
||||||
|
global model, model_name_tmp, device_tmp
|
||||||
|
|
||||||
|
os.system("""
|
||||||
|
if [ -e './output.mp4' ]; then
|
||||||
|
rm ./output.mp4
|
||||||
|
fi
|
||||||
|
""")
|
||||||
|
|
||||||
|
if model_name_tmp != model_name:
|
||||||
|
# Model judgment to avoid repeated loading
|
||||||
|
model_name_tmp = model_name
|
||||||
|
print(f"Loading model {model_name_tmp}......")
|
||||||
|
model = model_loading(model_name_tmp, device, opt)
|
||||||
|
elif device_tmp != device:
|
||||||
|
# Device judgment to avoid repeated loading
|
||||||
|
device_tmp = device
|
||||||
|
print(f"Loading model {model_name_tmp}......")
|
||||||
|
model = model_loading(model_name_tmp, device, opt)
|
||||||
|
else:
|
||||||
|
print(f"Loading model {model_name_tmp}......")
|
||||||
|
model = model_loading(model_name_tmp, device, opt)
|
||||||
|
|
||||||
|
# -------------Model tuning -------------
|
||||||
|
model.conf = conf # NMS confidence threshold
|
||||||
|
model.iou = iou # NMS IOU threshold
|
||||||
|
model.max_det = int(max_num) # Maximum number of detection frames
|
||||||
|
model.classes = model_cls # model classes
|
||||||
|
|
||||||
|
color_list = color_set(len(model_cls_name_cp)) # 设置颜色
|
||||||
|
|
||||||
|
# ----------------Load fonts----------------
|
||||||
|
yaml_index = cls_name.index(".yaml")
|
||||||
|
cls_name_lang = cls_name[yaml_index - 2:yaml_index]
|
||||||
|
|
||||||
|
if cls_name_lang == "zh":
|
||||||
|
# Chinese
|
||||||
|
textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/SimSun.ttf"), size=FONTSIZE)
|
||||||
|
elif cls_name_lang in ["en", "ru", "es", "ar"]:
|
||||||
|
# English, Russian, Spanish, Arabic
|
||||||
|
textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/TimesNewRoman.ttf"), size=FONTSIZE)
|
||||||
|
elif cls_name_lang == "ko":
|
||||||
|
# Korean
|
||||||
|
textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/malgun.ttf"), size=FONTSIZE)
|
||||||
|
|
||||||
|
# video->frame
|
||||||
|
gc.collect()
|
||||||
|
output_video_path = "./output.avi"
|
||||||
|
cap = cv2.VideoCapture(video)
|
||||||
|
fourcc = cv2.VideoWriter_fourcc(*"I420") # encoder
|
||||||
|
|
||||||
|
out = cv2.VideoWriter(output_video_path, fourcc, 30.0, (int(cap.get(3)), int(cap.get(4))))
|
||||||
|
while cap.isOpened():
|
||||||
|
ret, frame = cap.read()
|
||||||
|
# Determine empty frame
|
||||||
|
if not ret:
|
||||||
|
break
|
||||||
|
|
||||||
|
results = model(frame, size=infer_size) # detection
|
||||||
|
h, w, _ = frame.shape # frame size
|
||||||
|
img_size = (w, h) # frame size
|
||||||
|
|
||||||
|
for result in results.xyxyn:
|
||||||
|
for i in range(len(result)):
|
||||||
|
id = int(i) # instance ID
|
||||||
|
obj_cls_index = int(result[i][5]) # category index
|
||||||
|
obj_cls = model_cls_name_cp[obj_cls_index] # category
|
||||||
|
|
||||||
|
# ------------ border coordinates ------------
|
||||||
|
x0 = float(result[i][:4].tolist()[0])
|
||||||
|
y0 = float(result[i][:4].tolist()[1])
|
||||||
|
x1 = float(result[i][:4].tolist()[2])
|
||||||
|
y1 = float(result[i][:4].tolist()[3])
|
||||||
|
|
||||||
|
# ------------ Actual coordinates of the border ------------
|
||||||
|
x0 = int(img_size[0] * x0)
|
||||||
|
y0 = int(img_size[1] * y0)
|
||||||
|
x1 = int(img_size[0] * x1)
|
||||||
|
y1 = int(img_size[1] * y1)
|
||||||
|
|
||||||
|
conf = float(result[i][4]) # confidence
|
||||||
|
# fps = f"{(1000 / float(results.t[1])):.2f}" # FPS
|
||||||
|
|
||||||
|
frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
|
||||||
|
frame = pil_draw(
|
||||||
|
frame,
|
||||||
|
f"{id}-{obj_cls}:{conf:.2f}",
|
||||||
|
textFont,
|
||||||
|
[x0, y0, x1, y1],
|
||||||
|
FONTSIZE,
|
||||||
|
opt,
|
||||||
|
obj_cls_index,
|
||||||
|
color_list,
|
||||||
|
)
|
||||||
|
|
||||||
|
frame = cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR)
|
||||||
|
|
||||||
|
# frame->video
|
||||||
|
out.write(frame)
|
||||||
|
out.release()
|
||||||
|
cap.release()
|
||||||
|
# cv2.destroyAllWindows()
|
||||||
|
|
||||||
|
return output_video_path
|
||||||
|
|
||||||
|
|
||||||
|
def main(args):
|
||||||
|
gr.close_all()
|
||||||
|
|
||||||
|
global model, model_cls_name_cp, cls_name
|
||||||
|
|
||||||
|
source = args.source
|
||||||
|
source_video = args.source_video
|
||||||
|
img_tool = args.img_tool
|
||||||
|
nms_conf = args.nms_conf
|
||||||
|
nms_iou = args.nms_iou
|
||||||
|
model_name = args.model_name
|
||||||
|
model_cfg = args.model_cfg
|
||||||
|
cls_name = args.cls_name
|
||||||
|
device = args.device
|
||||||
|
inference_size = args.inference_size
|
||||||
|
max_detnum = args.max_detnum
|
||||||
|
slider_step = args.slider_step
|
||||||
|
is_login = args.is_login
|
||||||
|
usr_pwd = args.usr_pwd
|
||||||
|
is_share = args.is_share
|
||||||
|
|
||||||
|
is_fonts(f"{ROOT_PATH}/fonts") # Check font files
|
||||||
|
|
||||||
|
# model loading
|
||||||
|
model = model_loading(model_name, device)
|
||||||
|
|
||||||
|
model_names = yaml_csv(model_cfg, "model_names") # model names
|
||||||
|
model_cls_name = yaml_csv(cls_name, "model_cls_name") # class name
|
||||||
|
|
||||||
|
model_cls_name_cp = model_cls_name.copy() # class name
|
||||||
|
|
||||||
|
# ------------------- Input Components -------------------
|
||||||
|
inputs_img = gr.Image(image_mode="RGB", source=source, tool=img_tool, type="pil", label="original image")
|
||||||
|
inputs_device01 = gr.Radio(choices=["cuda:0", "cpu"], value=device, label="device")
|
||||||
|
inputs_model01 = gr.Dropdown(choices=model_names, value=model_name, type="value", label="model")
|
||||||
|
inputs_size01 = gr.Radio(choices=[320, 640, 1280], value=inference_size, label="inference size")
|
||||||
|
input_conf01 = gr.Slider(0, 1, step=slider_step, value=nms_conf, label="confidence threshold")
|
||||||
|
inputs_iou01 = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU threshold")
|
||||||
|
inputs_maxnum01 = gr.Number(value=max_detnum, label="Maximum number of detections")
|
||||||
|
inputs_clsName01 = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="category")
|
||||||
|
inputs_opt01 = gr.CheckboxGroup(choices=["refresh_yolov5", "label", "pdf", "json", "csv", "excel"],
|
||||||
|
value=["label", "pdf"],
|
||||||
|
type="value",
|
||||||
|
label="operate")
|
||||||
|
|
||||||
|
# ------------------- Input Components -------------------
|
||||||
|
inputs_video = gr.Video(format="mp4", source=source_video, label="original video") # webcam
|
||||||
|
inputs_device02 = gr.Radio(choices=["cuda:0", "cpu"], value=device, label="device")
|
||||||
|
inputs_model02 = gr.Dropdown(choices=model_names, value=model_name, type="value", label="model")
|
||||||
|
inputs_size02 = gr.Radio(choices=[320, 640, 1280], value=inference_size, label="inference size")
|
||||||
|
input_conf02 = gr.Slider(0, 1, step=slider_step, value=nms_conf, label="confidence threshold")
|
||||||
|
inputs_iou02 = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU threshold")
|
||||||
|
inputs_maxnum02 = gr.Number(value=max_detnum, label="Maximum number of detections")
|
||||||
|
inputs_clsName02 = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="category")
|
||||||
|
inputs_opt02 = gr.CheckboxGroup(choices=["refresh_yolov5", "label"], value=["label"], type="value", label="operate")
|
||||||
|
|
||||||
|
# Input parameters
|
||||||
|
inputs_img_list = [
|
||||||
|
inputs_img, # input image
|
||||||
|
inputs_device01, # device
|
||||||
|
inputs_model01, # model
|
||||||
|
inputs_size01, # inference size
|
||||||
|
input_conf01, # confidence threshold
|
||||||
|
inputs_iou01, # IoU threshold
|
||||||
|
inputs_maxnum01, # maximum number of detections
|
||||||
|
inputs_clsName01, # category
|
||||||
|
inputs_opt01, # detect operations
|
||||||
|
]
|
||||||
|
|
||||||
|
inputs_video_list = [
|
||||||
|
inputs_video, # input image
|
||||||
|
inputs_device02, # device
|
||||||
|
inputs_model02, # model
|
||||||
|
inputs_size02, # inference size
|
||||||
|
input_conf02, # confidence threshold
|
||||||
|
inputs_iou02, # IoU threshold
|
||||||
|
inputs_maxnum02, # maximum number of detections
|
||||||
|
inputs_clsName02, # category
|
||||||
|
inputs_opt02, # detect operation
|
||||||
|
]
|
||||||
|
|
||||||
|
# -------------------output component-------------------
|
||||||
|
outputs_img = gr.Image(type="pil", label="Detection image")
|
||||||
|
outputs_crops = gr.Gallery(label="Object crop")
|
||||||
|
outputs_df = gr.Dataframe(max_rows=5,
|
||||||
|
overflow_row_behaviour="paginate",
|
||||||
|
type="pandas",
|
||||||
|
label="List of detection information")
|
||||||
|
outputs_objSize = gr.Label(label="Object size ratio statistics")
|
||||||
|
outputs_clsSize = gr.Label(label="Category detection proportion statistics")
|
||||||
|
outputs_json = gr.JSON(label="Detection information")
|
||||||
|
outputs_pdf = gr.File(label="pdf detection report")
|
||||||
|
outputs_csv = gr.File(label="csv detection report")
|
||||||
|
outputs_excel = gr.File(label="xlsx detection report")
|
||||||
|
|
||||||
|
# -------------------output component-------------------
|
||||||
|
outputs_video = gr.Video(format='mp4', label="Detection video")
|
||||||
|
|
||||||
|
# output parameters
|
||||||
|
outputs_img_list = [
|
||||||
|
outputs_img, outputs_crops, outputs_objSize, outputs_clsSize, outputs_df, outputs_json, outputs_pdf,
|
||||||
|
outputs_csv, outputs_excel]
|
||||||
|
outputs_video_list = [outputs_video]
|
||||||
|
|
||||||
|
# title
|
||||||
|
title = "Gradio YOLOv5 Det v0.4"
|
||||||
|
|
||||||
|
# describe
|
||||||
|
description = "Author: 曾逸夫（Zeng Yifu）, Project Address: https://gitee.com/CV_Lab/gradio_yolov5_det, Github: https://github.com/Zengyf-CVer, thanks to [Gradio](https://github.com/gradio-app/gradio) & [YOLOv5](https://github.com/ultralytics/yolov5)"
|
||||||
|
# article="https://gitee.com/CV_Lab/gradio_yolov5_det"
|
||||||
|
|
||||||
|
# example image
|
||||||
|
examples = [
|
||||||
|
[
|
||||||
|
"./img_example/bus.jpg",
|
||||||
|
"cpu",
|
||||||
|
"yolov5s",
|
||||||
|
640,
|
||||||
|
0.6,
|
||||||
|
0.5,
|
||||||
|
10,
|
||||||
|
["person", "bus"],
|
||||||
|
["label", "pdf"],],
|
||||||
|
[
|
||||||
|
"./img_example/giraffe.jpg",
|
||||||
|
"cpu",
|
||||||
|
"yolov5l",
|
||||||
|
320,
|
||||||
|
0.5,
|
||||||
|
0.45,
|
||||||
|
12,
|
||||||
|
["giraffe"],
|
||||||
|
["label", "pdf"],],
|
||||||
|
[
|
||||||
|
"./img_example/zidane.jpg",
|
||||||
|
"cpu",
|
||||||
|
"yolov5m",
|
||||||
|
640,
|
||||||
|
0.6,
|
||||||
|
0.5,
|
||||||
|
15,
|
||||||
|
["person", "tie"],
|
||||||
|
["pdf", "json"],],
|
||||||
|
[
|
||||||
|
"./img_example/Millenial-at-work.jpg",
|
||||||
|
"cpu",
|
||||||
|
"yolov5s6",
|
||||||
|
1280,
|
||||||
|
0.5,
|
||||||
|
0.5,
|
||||||
|
20,
|
||||||
|
["person", "chair", "cup", "laptop"],
|
||||||
|
["label", "pdf"],],]
|
||||||
|
|
||||||
|
# interface
|
||||||
|
gyd_img = gr.Interface(
|
||||||
|
fn=yolo_det_img,
|
||||||
|
inputs=inputs_img_list,
|
||||||
|
outputs=outputs_img_list,
|
||||||
|
title=title,
|
||||||
|
description=description,
|
||||||
|
# article=article,
|
||||||
|
examples=examples,
|
||||||
|
cache_examples=False,
|
||||||
|
# theme="seafoam",
|
||||||
|
# live=True, # Change output in real time
|
||||||
|
flagging_dir="run", # output directory
|
||||||
|
# allow_flagging="manual",
|
||||||
|
# flagging_options=["good", "generally", "bad"],
|
||||||
|
)
|
||||||
|
|
||||||
|
gyd_video = gr.Interface(
|
||||||
|
# fn=yolo_det_video_test,
|
||||||
|
fn=yolo_det_video,
|
||||||
|
inputs=inputs_video_list,
|
||||||
|
outputs=outputs_video_list,
|
||||||
|
title=title,
|
||||||
|
description=description,
|
||||||
|
# article=article,
|
||||||
|
# examples=examples,
|
||||||
|
# theme="seafoam",
|
||||||
|
# live=True, # Change output in real time
|
||||||
|
flagging_dir="run", # output directory
|
||||||
|
allow_flagging="never",
|
||||||
|
# flagging_options=["good", "generally", "bad"],
|
||||||
|
)
|
||||||
|
|
||||||
|
gyd = gr.TabbedInterface(interface_list=[gyd_img, gyd_video], tab_names=["Image Mode", "Video Mode"])
|
||||||
|
|
||||||
|
if not is_login:
|
||||||
|
gyd.launch(
|
||||||
|
inbrowser=True, # Automatically open default browser
|
||||||
|
show_tips=True, # Automatically display the latest features of gradio
|
||||||
|
share=is_share, # Project sharing, other devices can access
|
||||||
|
favicon_path="./icon/logo.ico", # web icon
|
||||||
|
show_error=True, # Display error message in browser console
|
||||||
|
quiet=True, # Suppress most print statements
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
gyd.launch(
|
||||||
|
inbrowser=True, # Automatically open default browser
|
||||||
|
show_tips=True, # Automatically display the latest features of gradio
|
||||||
|
auth=usr_pwd, # login interface
|
||||||
|
share=is_share, # Project sharing, other devices can access
|
||||||
|
favicon_path="./icon/logo.ico", # web icon
|
||||||
|
show_error=True, # Display error message in browser console
|
||||||
|
quiet=True, # Suppress most print statements
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
args = parse_args()
|
||||||
|
main(args)
|
||||||
|
|
@ -0,0 +1,80 @@
|
||||||
|
人
|
||||||
|
自行车
|
||||||
|
汽车
|
||||||
|
摩托车
|
||||||
|
飞机
|
||||||
|
公交车
|
||||||
|
火车
|
||||||
|
卡车
|
||||||
|
船
|
||||||
|
红绿灯
|
||||||
|
消防栓
|
||||||
|
停止标志
|
||||||
|
停车收费表
|
||||||
|
长凳
|
||||||
|
鸟
|
||||||
|
猫
|
||||||
|
狗
|
||||||
|
马
|
||||||
|
羊
|
||||||
|
牛
|
||||||
|
象
|
||||||
|
熊
|
||||||
|
斑马
|
||||||
|
长颈鹿
|
||||||
|
背包
|
||||||
|
雨伞
|
||||||
|
手提包
|
||||||
|
领带
|
||||||
|
手提箱
|
||||||
|
飞盘
|
||||||
|
滑雪板
|
||||||
|
单板滑雪
|
||||||
|
运动球
|
||||||
|
风筝
|
||||||
|
棒球棒
|
||||||
|
棒球手套
|
||||||
|
滑板
|
||||||
|
冲浪板
|
||||||
|
网球拍
|
||||||
|
瓶子
|
||||||
|
红酒杯
|
||||||
|
杯子
|
||||||
|
叉子
|
||||||
|
刀
|
||||||
|
勺
|
||||||
|
碗
|
||||||
|
香蕉
|
||||||
|
苹果
|
||||||
|
三明治
|
||||||
|
橙子
|
||||||
|
西兰花
|
||||||
|
胡萝卜
|
||||||
|
热狗
|
||||||
|
比萨
|
||||||
|
甜甜圈
|
||||||
|
蛋糕
|
||||||
|
椅子
|
||||||
|
长椅
|
||||||
|
盆栽
|
||||||
|
床
|
||||||
|
餐桌
|
||||||
|
马桶
|
||||||
|
电视
|
||||||
|
笔记本电脑
|
||||||
|
鼠标
|
||||||
|
遥控器
|
||||||
|
键盘
|
||||||
|
手机
|
||||||
|
微波炉
|
||||||
|
烤箱
|
||||||
|
烤面包机
|
||||||
|
洗碗槽
|
||||||
|
冰箱
|
||||||
|
书
|
||||||
|
时钟
|
||||||
|
花瓶
|
||||||
|
剪刀
|
||||||
|
泰迪熊
|
||||||
|
吹风机
|
||||||
|
牙刷
|
||||||
|
|
|
@ -0,0 +1,7 @@
|
||||||
|
model_cls_name: ['人', '自行车', '汽车', '摩托车', '飞机', '公交车', '火车', '卡车', '船', '红绿灯', '消防栓', '停止标志',
|
||||||
|
'停车收费表', '长凳', '鸟', '猫', '狗', '马', '羊', '牛', '象', '熊', '斑马', '长颈鹿', '背包', '雨伞', '手提包', '领带',
|
||||||
|
'手提箱', '飞盘', '滑雪板', '单板滑雪', '运动球', '风筝', '棒球棒', '棒球手套', '滑板', '冲浪板', '网球拍', '瓶子', '红酒杯',
|
||||||
|
'杯子', '叉子', '刀', '勺', '碗', '香蕉', '苹果', '三明治', '橙子', '西兰花', '胡萝卜', '热狗', '比萨', '甜甜圈', '蛋糕',
|
||||||
|
'椅子', '长椅', '盆栽', '床', '餐桌', '马桶', '电视', '笔记本电脑', '鼠标', '遥控器', '键盘', '手机', '微波炉', '烤箱',
|
||||||
|
'烤面包机', '洗碗槽', '冰箱', '书', '时钟', '花瓶', '剪刀', '泰迪熊', '吹风机', '牙刷'
|
||||||
|
]
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
model_cls_name: [" الناس " , " الدراجات " , " السيارات " , " الدراجات النارية " , " الطائرات " , " الحافلات " , " القطارات " , " الشاحنات " , " السفن " , " إشارات المرور " ,
|
||||||
|
" صنبور " , " علامة " , " موقف سيارات " , " الجدول " , " مقعد " , " الطيور " , " القط " , " الكلب " , " الحصان " , " الأغنام " , " الثور " , " الفيل " ,
|
||||||
|
" الدب " , " حمار وحشي " , " الزرافة " , " حقيبة " , " مظلة " , " حقيبة يد " , " ربطة عنق " , " حقيبة " , " الفريسبي " , " الزلاجات " , " الزلاجات " ,
|
||||||
|
" الكرة الرياضية " , " طائرة ورقية " , " مضرب بيسبول " , " قفازات البيسبول " , " لوح التزلج " , " ركوب الأمواج " , " مضرب تنس " , " زجاجة " ,
|
||||||
|
" كأس " , " كأس " , " شوكة " , " سكين " , " ملعقة " , " وعاء " , " الموز " , " التفاح " , " ساندويتش " , " البرتقال " , " القرنبيط " ,
|
||||||
|
" الجزر " , " الكلاب الساخنة " , " البيتزا " , " دونات " , " كعكة " , " كرسي " , " أريكة " , " بوعاء " , " السرير " , " طاولة الطعام " , " المرحاض " ,
|
||||||
|
التلفزيون , الكمبيوتر المحمول , الفأرة , وحدة تحكم عن بعد , لوحة المفاتيح , الهاتف المحمول , فرن الميكروويف , محمصة خبز كهربائية , بالوعة , ثلاجة ,
|
||||||
|
" كتاب " , " ساعة " , " زهرية " , " مقص " , " دمية دب " , " مجفف الشعر " , " فرشاة الأسنان "
|
||||||
|
]
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
model_cls_name: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
|
||||||
|
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant',
|
||||||
|
'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
|
||||||
|
'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle',
|
||||||
|
'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli',
|
||||||
|
'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet',
|
||||||
|
'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator',
|
||||||
|
'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
|
||||||
|
]
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
model_cls_name: ['persona', 'bicicleta', 'coche', 'motocicleta', 'avión', 'autobús', 'tren', 'camión', 'barco', 'semáforo',
|
||||||
|
'boca de incendios', 'señal de alto', 'parquímetro', 'banco', 'pájaro', 'gato', 'perro', 'caballo', 'oveja', 'vaca', 'elefante',
|
||||||
|
'oso', 'cebra', 'jirafa', 'mochila', 'paraguas', 'bolso', 'corbata', 'maleta', 'frisbee', 'esquís', 'snowboard',
|
||||||
|
'pelota deportiva', 'cometa', 'bate de béisbol', 'guante de béisbol', 'monopatín', 'tabla de surf', 'raqueta de tenis', 'botella',
|
||||||
|
'copa de vino', 'taza', 'tenedor', 'cuchillo', 'cuchara', 'tazón', 'plátano', 'manzana', 'sándwich', 'naranja', 'brócoli',
|
||||||
|
'zanahoria', 'perrito caliente', 'pizza', 'rosquilla', 'pastel', 'silla', 'sofá', 'planta en maceta', 'cama', 'mesa de comedor', 'inodoro',
|
||||||
|
'tv', 'laptop', 'ratón', 'control remoto', 'teclado', 'celular', 'microondas', 'horno', 'tostadora', 'fregadero', 'nevera',
|
||||||
|
'libro', 'reloj', 'jarrón', 'tijeras', 'oso de peluche', 'secador de pelo', 'cepillo de dientes'
|
||||||
|
]
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
model_cls_name: ['사람', '자전거', '자동차', '오토바이', '비행기', '버스', '기차', '트럭', '보트', '신호등',
|
||||||
|
'소화전', '정지 신호', '주차 미터기', '벤치', '새', '고양이', '개', '말', '양', '소', '코끼리',
|
||||||
|
'곰', '얼룩말', '기린', '배낭', '우산', '핸드백', '타이', '여행가방', '프리스비', '스키', '스노우보드',
|
||||||
|
'스포츠 공', '연', '야구 방망이', '야구 글러브', '스케이트보드', '서프보드', '테니스 라켓', '병',
|
||||||
|
'와인잔', '컵', '포크', '나이프', '숟가락', '그릇', '바나나', '사과', '샌드위치', '오렌지', '브로콜리',
|
||||||
|
'당근', '핫도그', '피자', '도넛', '케이크', '의자', '소파', '화분', '침대', '식탁', '화장실',
|
||||||
|
'tv', '노트북', '마우스', '리모컨', '키보드', '휴대전화', '전자레인지', '오븐', '토스터', '싱크대', '냉장고',
|
||||||
|
'책', '시계', '꽃병', '가위', '테디베어', '드라이기', '칫솔'
|
||||||
|
]
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
model_cls_name: ['человек', 'велосипед', 'автомобиль', 'мотоцикл', 'самолет', 'автобус', 'поезд', 'грузовик', 'лодка', 'светофор',
|
||||||
|
'пожарный гидрант', 'стоп', 'паркомат', 'скамейка', 'птица', 'кошка', 'собака', 'лошадь', 'овца', 'корова', 'слон',
|
||||||
|
'медведь', 'зебра', 'жираф', 'рюкзак', 'зонт', 'сумочка', 'галстук', 'чемодан', 'фрисби', 'лыжи', 'сноуборд',
|
||||||
|
'спортивный мяч', 'воздушный змей', 'бейсбольная бита', 'бейсбольная перчатка', 'скейтборд', 'доска для серфинга', 'теннисная ракетка', 'бутылка',
|
||||||
|
'бокал', 'чашка', 'вилка', 'нож', 'ложка', 'миска', 'банан', 'яблоко', 'бутерброд', 'апельсин', 'брокколи',
|
||||||
|
'морковь', 'хот-дог', 'пицца', 'пончик', 'торт', 'стул', 'диван', 'растение в горшке', 'кровать', 'обеденный стол', 'туалет',
|
||||||
|
'телевизор', 'ноутбук', 'мышь', 'пульт', 'клавиатура', 'мобильный телефон', 'микроволновая печь', 'духовка', 'тостер', 'раковина', 'холодильник',
|
||||||
|
'книга', 'часы', 'ваза', 'ножницы', 'плюшевый мишка', 'фен', 'зубная щетка'
|
||||||
|
]
|
||||||
|
|
@ -0,0 +1,7 @@
|
||||||
|
model_cls_name: ['人', '自行车', '汽车', '摩托车', '飞机', '公交车', '火车', '卡车', '船', '红绿灯', '消防栓', '停止标志',
|
||||||
|
'停车收费表', '长凳', '鸟', '猫', '狗', '马', '羊', '牛', '象', '熊', '斑马', '长颈鹿', '背包', '雨伞', '手提包', '领带',
|
||||||
|
'手提箱', '飞盘', '滑雪板', '单板滑雪', '运动球', '风筝', '棒球棒', '棒球手套', '滑板', '冲浪板', '网球拍', '瓶子', '红酒杯',
|
||||||
|
'杯子', '叉子', '刀', '勺', '碗', '香蕉', '苹果', '三明治', '橙子', '西兰花', '胡萝卜', '热狗', '比萨', '甜甜圈', '蛋糕',
|
||||||
|
'椅子', '长椅', '盆栽', '床', '餐桌', '马桶', '电视', '笔记本电脑', '鼠标', '遥控器', '键盘', '手机', '微波炉', '烤箱',
|
||||||
|
'烤面包机', '洗碗槽', '冰箱', '书', '时钟', '花瓶', '剪刀', '泰迪熊', '吹风机', '牙刷'
|
||||||
|
]
|
||||||
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|
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|
After Width: | Height: | Size: 476 KiB |
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|
After Width: | Height: | Size: 192 KiB |
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|
After Width: | Height: | Size: 165 KiB |
|
|
@ -0,0 +1,5 @@
|
||||||
|
yolov5n
|
||||||
|
yolov5s
|
||||||
|
yolov5m
|
||||||
|
yolov5l
|
||||||
|
yolov5x
|
||||||
|
|
|
@ -0,0 +1 @@
|
||||||
|
model_names: ["yolov5n", "yolov5s", "yolov5m", "yolov5l", "yolov5x"]
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
yolov5n
|
||||||
|
|
|
@ -0,0 +1 @@
|
||||||
|
model_names: ["yolov5n"]
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
model_names: ["yolov5n", "yolov5s", "yolov5m", "yolov5l", "yolov5x", "yolov5n6", "yolov5s6", "yolov5m6", "yolov5l6", "yolov5x6"]
|
||||||
|
|
@ -0,0 +1,5 @@
|
||||||
|
yolov5n6
|
||||||
|
yolov5s6
|
||||||
|
yolov5m6
|
||||||
|
yolov5l6
|
||||||
|
yolov5x6
|
||||||
|
|
|
@ -0,0 +1 @@
|
||||||
|
model_names: ["yolov5n6", "yolov5s6", "yolov5m6", "yolov5l6", "yolov5x6"]
|
||||||
|
|
@ -0,0 +1,8 @@
|
||||||
|
cd ./yolov5
|
||||||
|
|
||||||
|
# 下载YOLOv5模型
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt
|
||||||
|
|
@ -0,0 +1,4 @@
|
||||||
|
cd ./yolov5
|
||||||
|
|
||||||
|
# 下载YOLOv5模型
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt
|
||||||
|
|
@ -0,0 +1,8 @@
|
||||||
|
cd ./yolov5
|
||||||
|
|
||||||
|
# 下载YOLOv5模型
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt
|
||||||
|
wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt
|
||||||
|
|
@ -0,0 +1,3 @@
|
||||||
|
ffmpeg
|
||||||
|
x264
|
||||||
|
libx264-dev
|
||||||
|
|
@ -0,0 +1,45 @@
|
||||||
|
# Base ----------------------------------------
|
||||||
|
matplotlib>=3.2.2
|
||||||
|
numpy>=1.22.3
|
||||||
|
opencv-python-headless>=4.5.5.64
|
||||||
|
Pillow>=7.1.2
|
||||||
|
PyYAML>=5.3.1
|
||||||
|
requests>=2.23.0
|
||||||
|
scipy>=1.4.1 # Google Colab version
|
||||||
|
torch>=1.7.0
|
||||||
|
torchvision>=0.8.1
|
||||||
|
tqdm>=4.41.0
|
||||||
|
|
||||||
|
# Gradio YOLOv5 Det ----------------------------------------
|
||||||
|
gradio>=3.0.3
|
||||||
|
wget>=3.2
|
||||||
|
rich>=12.2.0
|
||||||
|
fpdf>=1.7.2
|
||||||
|
plotly>=5.7.0
|
||||||
|
bokeh>=2.4.2
|
||||||
|
openpyxl>=3.0.10
|
||||||
|
|
||||||
|
# Logging -------------------------------------
|
||||||
|
tensorboard>=2.4.1
|
||||||
|
# wandb
|
||||||
|
|
||||||
|
# Plotting ------------------------------------
|
||||||
|
pandas>=1.1.4
|
||||||
|
seaborn>=0.11.0
|
||||||
|
|
||||||
|
# Export --------------------------------------
|
||||||
|
# coremltools>=4.1 # CoreML export
|
||||||
|
# onnx>=1.9.0 # ONNX export
|
||||||
|
# onnx-simplifier>=0.3.6 # ONNX simplifier
|
||||||
|
# scikit-learn==0.19.2 # CoreML quantization
|
||||||
|
# tensorflow>=2.4.1 # TFLite export
|
||||||
|
# tensorflowjs>=3.9.0 # TF.js export
|
||||||
|
# openvino-dev # OpenVINO export
|
||||||
|
|
||||||
|
# Extras --------------------------------------
|
||||||
|
ipython # interactive notebook
|
||||||
|
psutil # system utilization
|
||||||
|
thop # FLOPs computation
|
||||||
|
# albumentations>=1.0.3
|
||||||
|
# pycocotools>=2.0 # COCO mAP
|
||||||
|
# roboflow
|
||||||
|
|
@ -0,0 +1,69 @@
|
||||||
|
# font management
|
||||||
|
# author: Zeng Yifu（曾逸夫）
|
||||||
|
# creation time: 2022-05-01
|
||||||
|
# email: zyfiy1314@163.com
|
||||||
|
# project homepage: https://gitee.com/CV_Lab/gradio_yolov5_det
|
||||||
|
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
import wget
|
||||||
|
from rich.console import Console
|
||||||
|
|
||||||
|
ROOT_PATH = sys.path[0] # Project root directory
|
||||||
|
|
||||||
|
# Chinese, English, Russian, Spanish, Arabic, Korean
|
||||||
|
fonts_list = ["SimSun.ttf", "TimesNewRoman.ttf", "malgun.ttf"] # font list
|
||||||
|
fonts_suffix = ["ttc", "ttf", "otf"] # font suffix
|
||||||
|
|
||||||
|
data_url_dict = {
|
||||||
|
"SimSun.ttf": "https://gitee.com/CV_Lab/gradio_yolov5_det/attach_files/1053539/download/SimSun.ttf",
|
||||||
|
"TimesNewRoman.ttf": "https://gitee.com/CV_Lab/gradio_yolov5_det/attach_files/1053537/download/TimesNewRoman.ttf",
|
||||||
|
"malgun.ttf": "https://gitee.com/CV_Lab/gradio_yolov5_det/attach_files/1053538/download/malgun.ttf",}
|
||||||
|
|
||||||
|
console = Console()
|
||||||
|
|
||||||
|
|
||||||
|
# create font library
|
||||||
|
def add_fronts(font_diff):
|
||||||
|
|
||||||
|
global font_name
|
||||||
|
|
||||||
|
for k, v in data_url_dict.items():
|
||||||
|
if k in font_diff:
|
||||||
|
font_name = v.split("/")[-1] # font name
|
||||||
|
Path(f"{ROOT_PATH}/fonts").mkdir(parents=True, exist_ok=True) # Create a directory
|
||||||
|
|
||||||
|
file_path = f"{ROOT_PATH}/fonts/{font_name}" # font path
|
||||||
|
|
||||||
|
try:
|
||||||
|
# Download font file
|
||||||
|
wget.download(v, file_path)
|
||||||
|
except Exception as e:
|
||||||
|
print("Path error! Program ended!")
|
||||||
|
print(e)
|
||||||
|
sys.exit()
|
||||||
|
else:
|
||||||
|
print()
|
||||||
|
console.print(f"{font_name} [bold green]font file download complete![/bold green] has been saved to: {file_path}")
|
||||||
|
|
||||||
|
|
||||||
|
# Determine the font file
|
||||||
|
def is_fonts(fonts_dir):
|
||||||
|
if os.path.isdir(fonts_dir):
|
||||||
|
# if the font library exists
|
||||||
|
f_list = os.listdir(fonts_dir) # local font library
|
||||||
|
|
||||||
|
font_diff = list(set(fonts_list).difference(set(f_list)))
|
||||||
|
|
||||||
|
if font_diff != []:
|
||||||
|
# font does not exist
|
||||||
|
console.print("[bold red] font does not exist, loading...[/bold red]")
|
||||||
|
add_fronts(font_diff) # Create a font library
|
||||||
|
else:
|
||||||
|
console.print(f"{fonts_list}[bold green]font already exists![/bold green]")
|
||||||
|
else:
|
||||||
|
# The font library does not exist, create a font library
|
||||||
|
console.print("[bold red]font library does not exist, creating...[/bold red]")
|
||||||
|
add_fronts(fonts_list) # Create a font library
|
||||||
|
|
@ -0,0 +1,78 @@
|
||||||
|
# PDF management
|
||||||
|
# author: Zeng Yifu
|
||||||
|
# creation time: 2022-05-05
|
||||||
|
|
||||||
|
from fpdf import FPDF
|
||||||
|
|
||||||
|
|
||||||
|
# PDF generation class
|
||||||
|
class PDF(FPDF):
|
||||||
|
# Reference: https://pyfpdf.readthedocs.io/en/latest/Tutorial/index.html
|
||||||
|
def header(self):
|
||||||
|
# Set Chinese font
|
||||||
|
self.add_font("SimSun", "", "./fonts/SimSun.ttf", uni=True)
|
||||||
|
self.set_font("SimSun", "", 16)
|
||||||
|
# Calculate width of title and position
|
||||||
|
w = self.get_string_width(title) + 6
|
||||||
|
self.set_x((210 - w) / 2)
|
||||||
|
# Colors of frame, background and text
|
||||||
|
self.set_draw_color(255, 255, 255)
|
||||||
|
self.set_fill_color(255, 255, 255)
|
||||||
|
self.set_text_color(0, 0, 0)
|
||||||
|
# Thickness of frame (1 mm)
|
||||||
|
# self.set_line_width(1)
|
||||||
|
# Title
|
||||||
|
self.cell(w, 9, title, 1, 1, "C", 1)
|
||||||
|
# Line break
|
||||||
|
self.ln(10)
|
||||||
|
|
||||||
|
def footer(self):
|
||||||
|
# Position at 1.5 cm from bottom
|
||||||
|
self.set_y(-15)
|
||||||
|
# Set Chinese font
|
||||||
|
self.add_font("SimSun", "", "./fonts/SimSun.ttf", uni=True)
|
||||||
|
self.set_font("SimSun", "", 12)
|
||||||
|
# Text color in gray
|
||||||
|
self.set_text_color(128)
|
||||||
|
# Page number
|
||||||
|
self.cell(0, 10, "Page " + str(self.page_no()), 0, 0, "C")
|
||||||
|
|
||||||
|
def chapter_title(self, num, label):
|
||||||
|
# Set Chinese font
|
||||||
|
self.add_font("SimSun", "", "./fonts/SimSun.ttf", uni=True)
|
||||||
|
self.set_font("SimSun", "", 12)
|
||||||
|
# Background color
|
||||||
|
self.set_fill_color(200, 220, 255)
|
||||||
|
# Title
|
||||||
|
# self.cell(0, 6, 'Chapter %d : %s' % (num, label), 0, 1, 'L', 1)
|
||||||
|
self.cell(0, 6, "Detection Result:", 0, 1, "L", 1)
|
||||||
|
# Line break
|
||||||
|
self.ln(4)
|
||||||
|
|
||||||
|
def chapter_body(self, name):
|
||||||
|
|
||||||
|
# Set Chinese font
|
||||||
|
self.add_font("SimSun", "", "./fonts/SimSun.ttf", uni=True)
|
||||||
|
self.set_font("SimSun", "", 12)
|
||||||
|
# Output justified text
|
||||||
|
self.multi_cell(0, 5, name)
|
||||||
|
# Line break
|
||||||
|
self.ln()
|
||||||
|
self.cell(0, 5, "--------------------------------------")
|
||||||
|
|
||||||
|
def print_chapter(self, num, title, name):
|
||||||
|
self.add_page()
|
||||||
|
self.chapter_title(num, title)
|
||||||
|
self.chapter_body(name)
|
||||||
|
|
||||||
|
|
||||||
|
# pdf generation function
|
||||||
|
def pdf_generate(input_file, output_file, title_):
|
||||||
|
global title
|
||||||
|
|
||||||
|
title = title_
|
||||||
|
pdf = PDF()
|
||||||
|
pdf.set_title(title)
|
||||||
|
pdf.set_author("Zeng Yifu")
|
||||||
|
pdf.print_chapter(1, "A RUNAWAY REEF", input_file)
|
||||||
|
pdf.output(output_file)
|
||||||
Loading…
Reference in New Issue