Zengyf-CVer's picture
v03 update
65fff86
raw
history blame
13.4 kB
# Gradio YOLOv5 Det v0.3
# author: Zeng Yifu(曾逸夫)
# creation time: 2022-05-09
# email: zyfiy1314@163.com
# project homepage: https://gitee.com/CV_Lab/gradio_yolov5_det
# import os
# os.system("pip install gradio==3.0.1")
import argparse
import csv
import json
import sys
from collections import Counter
from pathlib import Path
import pandas as pd
import gradio as gr
import torch
import yaml
from PIL import Image, ImageDraw, ImageFont
from util.fonts_opt import is_fonts
from util.pdf_opt import pdf_generate
ROOT_PATH = sys.path[0] # root directory
# model path
model_path = "ultralytics/yolov5"
# Gradio YOLOv5 Det version
GYD_VERSION = "Gradio YOLOv5 Det v0.3"
# model name temporary variable
model_name_tmp = ""
# Device temporary variables
device_tmp = ""
# File extension
suffix_list = [".csv", ".yaml"]
# font size
FONTSIZE = 25
# object style
obj_style = ["Small Object", "Medium Object", "Large Object"]
def parse_args(known=False):
parser = argparse.ArgumentParser(description="Gradio YOLOv5 Det v0.3")
parser.add_argument("--source", "-src", default="upload", type=str, help="input source")
parser.add_argument("--img_tool", "-it", default="editor", type=str, help="input image tool")
parser.add_argument("--model_name", "-mn", default="yolov5s", type=str, help="model name")
parser.add_argument(
"--model_cfg",
"-mc",
default="./model_config/model_name_p5_p6_all.yaml",
type=str,
help="model config",
)
parser.add_argument(
"--cls_name",
"-cls",
default="./cls_name/cls_name_en.yaml",
type=str,
help="cls name",
)
parser.add_argument(
"--nms_conf",
"-conf",
default=0.5,
type=float,
help="model NMS confidence threshold",
)
parser.add_argument("--nms_iou", "-iou", default=0.45, type=float, help="model NMS IoU threshold")
parser.add_argument(
"--device",
"-dev",
default="cpu",
type=str,
help="cuda or cpu",
)
parser.add_argument("--inference_size", "-isz", default=640, type=int, help="model inference size")
parser.add_argument("--max_detnum", "-mdn", default="50", type=str, help="model max det num")
args = parser.parse_known_args()[0] if known else parser.parse_args()
return args
# yaml file parsing
def yaml_parse(file_path):
return yaml.safe_load(open(file_path, encoding="utf-8").read())
# yaml csv file parsing
def yaml_csv(file_path, file_tag):
file_suffix = Path(file_path).suffix
if file_suffix == suffix_list[0]:
# model name
file_names = [i[0] for i in list(csv.reader(open(file_path)))] # csv version
elif file_suffix == suffix_list[1]:
# model name
file_names = yaml_parse(file_path).get(file_tag) # yaml version
else:
print(f"{file_path} is not in the correct format! Program exits!")
sys.exit()
return file_names
# model loading
def model_loading(model_name, device):
# load model
model = torch.hub.load(
model_path, model_name, force_reload=True, device=device, _verbose=False
)
return model
# check information
def export_json(results, model, img_size):
return [
[
{
"id": i,
"class": int(result[i][5]),
# "class_name": model.model.names[int(result[i][5])],
"class_name": model_cls_name_cp[int(result[i][5])],
"normalized_box": {
"x0": round(result[i][:4].tolist()[0], 6),
"y0": round(result[i][:4].tolist()[1], 6),
"x1": round(result[i][:4].tolist()[2], 6),
"y1": round(result[i][:4].tolist()[3], 6),},
"confidence": round(float(result[i][4]), 2),
"fps": round(1000 / float(results.t[1]), 2),
"width": img_size[0],
"height": img_size[1],} for i in range(len(result))] for result in results.xyxyn]
# frame conversion
def pil_draw(img, countdown_msg, textFont, xyxy, font_size, opt):
img_pil = ImageDraw.Draw(img)
img_pil.rectangle(xyxy, fill=None, outline="green") # bounding box
if "label" in opt:
text_w, text_h = textFont.getsize(countdown_msg) # Label size
img_pil.rectangle(
(xyxy[0], xyxy[1], xyxy[0] + text_w, xyxy[1] + text_h),
fill="green",
outline="green",
) # label background
img_pil.multiline_text(
(xyxy[0], xyxy[1]),
countdown_msg,
fill=(205, 250, 255),
font=textFont,
align="center",
)
return img
# YOLOv5 image detection function
def yolo_det(img, device, model_name, inference_size, conf, iou, max_num, model_cls, opt):
global model, model_name_tmp, device_tmp
# object size num
s_obj, m_obj, l_obj = 0, 0, 0
# object area list
area_obj_all = []
# cls num stat
cls_det_stat = []
if model_name_tmp != model_name:
# Model judgment to avoid repeated loading
model_name_tmp = model_name
model = model_loading(model_name_tmp, device)
elif device_tmp != device:
device_tmp = device
model = model_loading(model_name_tmp, device)
# -------------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
img_size = img.size # frame size
results = model(img, size=inference_size) # detection
# Data Frame
dataframe = results.pandas().xyxy[0].round(2)
# ----------------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)
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,
)
# ----------add object size----------
w_obj = x1 - x0
h_obj = y1 - y0
area_obj = w_obj * h_obj
area_obj_all.append(area_obj)
det_json = export_json(results, model, 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-------
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, objSize_dict, clsRatio_dict, det_json, report, dataframe
def main(args):
gr.close_all()
global model, model_cls_name_cp, cls_name
slider_step = 0.05 # sliding step
source = args.source
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
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_device = gr.Radio(choices=["cuda:0", "cpu"], value=device, label="device")
inputs_model = gr.Dropdown(choices=model_names, value=model_name, type="value", label="model")
inputs_size = gr.Radio(choices=[320, 640, 1280], value=inference_size, label="inference size")
input_conf = gr.Slider(0, 1, step=slider_step, value=nms_conf, label="confidence threshold")
inputs_iou = gr.Slider(0, 1, step=slider_step, value=nms_iou, label="IoU threshold")
inputs_maxnum = gr.Textbox(lines=1, placeholder="Maximum number of detections", value=max_detnum, label="Maximum number of detections")
inputs_clsName = gr.CheckboxGroup(choices=model_cls_name, value=model_cls_name, type="index", label="category")
inputs_opt = gr.CheckboxGroup(choices=["label", "pdf", "json"],
value=["label", "pdf"],
type="value",
label="operate")
# Input parameters
inputs = [
inputs_img, # input image
inputs_device, # device
inputs_model, # model
inputs_size, # inference size
input_conf, # confidence threshold
inputs_iou, # IoU threshold
inputs_maxnum, # maximum number of detections
inputs_clsName, # category
inputs_opt, # detect operations
]
# Output parameters
outputs_img = gr.Image(type="pil", label="Detection image")
outputs_json = gr.JSON(label="Detection information")
outputs_pdf = gr.File(label="Download test report")
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 = [outputs_img, outputs_objSize, outputs_clsSize, outputs_json, outputs_pdf, outputs_df]
# title
title = "Gradio YOLOv5 Det v0.3"
# describe
description = "<div align='center'>Customizable target detection model, easy to install, easy to use</div>"
# 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,
0.25,
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
gr.Interface(
fn=yolo_det,
inputs=inputs,
outputs=outputs,
title=title,
description=description,
article="",
# examples=examples,
# theme="seafoam",
# flagging_dir="run", # output directory
).launch(
inbrowser=True, # Automatically open default browser
show_tips=True, # Automatically display the latest features of gradio
)
if __name__ == "__main__":
args = parse_args()
main(args)