|
from turtle import title |
|
import torch |
|
import argparse |
|
import gradio as gr |
|
from PIL import Image |
|
from numpy import random |
|
from pathlib import Path |
|
import os |
|
import time |
|
import torch.backends.cudnn as cudnn |
|
from models.experimental import attempt_load |
|
import cv2 |
|
from utils.datasets import LoadStreams, LoadImages |
|
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier,scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path |
|
from utils.plots import plot_one_box |
|
from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel |
|
|
|
|
|
os.system('git clone https://github.com/WongKinYiu/yolov7') |
|
|
|
""" |
|
Ejemplo de uso: |
|
para la empresa GCC , la imagen debe contener cualquiera equipo contra acidentes personales |
|
* Cascos de Seguridad. ... |
|
* Tapones para oídos y Orejeras. ... |
|
* Lentes de Seguridad. ... |
|
* Respiradores. ... |
|
* Chaleco de Seguridad. ... |
|
* Guantes de seguridad. ... |
|
* Botas de Seguridad. ... |
|
* Fuentes. |
|
""" |
|
|
|
def Custom_detect(img): |
|
model='best' |
|
parser = argparse.ArgumentParser() |
|
parser.add_argument('--weights', nargs='+', type=str, default=model+".pt", help='model.pt path(s)') |
|
parser.add_argument('--source', type=str, default='Temp_file/', help='source') |
|
parser.add_argument('--img-size', type=int, default=100, help='inference size (pixels)') |
|
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') |
|
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') |
|
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') |
|
parser.add_argument('--view-img', action='store_true', help='display results') |
|
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') |
|
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') |
|
parser.add_argument('--nosave', action='store_true', help='do not save images/videos') |
|
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') |
|
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') |
|
parser.add_argument('--augment', action='store_true', help='augmented inference') |
|
parser.add_argument('--update', action='store_true', help='update all models') |
|
parser.add_argument('--project', default='runs/detect', help='save results to project/name') |
|
parser.add_argument('--name', default='exp', help='save results to project/name') |
|
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') |
|
parser.add_argument('--trace', action='store_true', help='trace model') |
|
opt = parser.parse_args() |
|
img.save("Temp_file/test.jpg") |
|
source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.trace |
|
save_img = True |
|
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) |
|
save_dir = Path(increment_path(Path(opt.project)/opt.name,exist_ok=opt.exist_ok)) |
|
|
|
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) |
|
set_logging() |
|
device = select_device(opt.device) |
|
half = device.type != 'cpu' |
|
model = attempt_load(weights, map_location=device) |
|
stride = int(model.stride.max()) |
|
imgsz = check_img_size(imgsz, s=stride) |
|
if trace: |
|
model = TracedModel(model, device, opt.img_size) |
|
if half: |
|
model.half() |
|
|
|
classify = False |
|
if classify: |
|
modelc = load_classifier(name='resnet101', n=2) |
|
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() |
|
vid_path, vid_writer = None, None |
|
if webcam: |
|
view_img = check_imshow() |
|
cudnn.benchmark = True |
|
dataset = LoadStreams(source, img_size=imgsz, stride=stride) |
|
else: |
|
dataset = LoadImages(source, img_size=imgsz, stride=stride) |
|
names = model.module.names if hasattr(model, 'module') else model.names |
|
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] |
|
if device.type != 'cpu': |
|
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) |
|
t0 = time.time() |
|
for path, img, im0s, vid_cap in dataset: |
|
img = torch.from_numpy(img).to(device) |
|
img = img.half() if half else img.float() |
|
img /= 255.0 |
|
if img.ndimension() == 3: |
|
img = img.unsqueeze(0) |
|
|
|
|
|
t1 = time_synchronized() |
|
pred = model(img, augment=opt.augment)[0] |
|
|
|
pred = non_max_suppression(pred,opt.conf_thres,opt.iou_thres,classes=opt.classes, agnostic=opt.agnostic_nms) |
|
t2 = time_synchronized() |
|
|
|
|
|
|
|
if classify: |
|
pred = apply_classifier(pred, modelc, img, im0s) |
|
|
|
for i, det in enumerate(pred): |
|
if webcam: |
|
p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count |
|
else: |
|
p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) |
|
|
|
p = Path(p) |
|
save_path = str(save_dir / p.name) |
|
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') |
|
s += '%gx%g ' % img.shape[2:] |
|
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] |
|
if len(det): |
|
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() |
|
|
|
|
|
for c in det[:, -1].unique(): |
|
n = (det[:, -1] == c).sum() |
|
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " |
|
|
|
|
|
for *xyxy, conf, cls in reversed(det): |
|
if save_txt: |
|
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() |
|
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) |
|
with open(txt_path + '.txt', 'a') as f: |
|
f.write(('%g ' * len(line)).rstrip() % line + '\n') |
|
|
|
if save_img or view_img: |
|
label = f'{names[int(cls)]} {conf:.2f}' |
|
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) |
|
if view_img: |
|
cv2.imshow(str(p), im0) |
|
cv2.waitKey(1) |
|
|
|
if save_img: |
|
if dataset.mode == 'image': |
|
cv2.imwrite(save_path, im0) |
|
else: |
|
if vid_path != save_path: |
|
vid_path = save_path |
|
if isinstance(vid_writer, cv2.VideoWriter): |
|
vid_writer.release() |
|
if vid_cap: |
|
fps = vid_cap.get(cv2.CAP_PROP_FPS) |
|
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) |
|
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) |
|
else: |
|
fps, w, h = 30, im0.shape[1], im0.shape[0] |
|
save_path += '.mp4' |
|
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) |
|
vid_writer.write(im0) |
|
|
|
if save_txt or save_img: |
|
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' |
|
|
|
print(f'Done. ({time.time() - t0:.3f}s)') |
|
|
|
return Image.fromarray(im0[:,:,::-1]) |
|
|
|
description = """ |
|
<table> |
|
<tr> |
|
<th>Description</th> |
|
<th>VIDEO</th> |
|
</tr> |
|
<tr> |
|
<td><h3>Ejemplo para la empresa GCC, La imagen debe contener cualquiera equipo contra acidentes personales</h3> |
|
<ul> |
|
<li> Cascos de Seguridad. ...</li> |
|
<li> Botas de Seguridad. ...</li> |
|
<li> Guantes de Seguridad. ...</li> |
|
<li> Arnés de Seguridad. ...</li> |
|
<li> Lentes de Seguridad. ...</li> |
|
<li> Ropa de Seguridad. ...</li> |
|
<li> CubreBocas ...</li> |
|
</ul> |
|
<a href="https://imgbb.com/"><img src="https://i.ibb.co/bN3k8rt/307136886-514139580194714-961127180922884176-n.jpg" alt="307136886-514139580194714-961127180922884176-n" border="0"></a></td> |
|
<td><iframe width="560" height="315" src="https://www.youtube.com/embed/9SPbZrCaZZE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> </td> |
|
""" |
|
|
|
inp = gr.Image(type="pil") |
|
output = gr.Image(type="pil") |
|
banner = gr.Image("banner.jpg", width=400) |
|
examples=[["Examples/Image1.jpg","Image1"],["Examples/Image2.jpg","Image2"]] |
|
|
|
io=gr.Interface(fn=Custom_detect, inputs=inp, outputs=output, title='Prueba de GuardIA',examples=examples,cache_examples=False,description=description) |
|
|
|
io.launch() |
