Is it possible to process multiple videos using PyQT5 library in YOLOv5 model?

[kmsdoit]

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Question

this is my code
`import ast
import sys
import cv2
from PySide6.QtWidgets import QApplication, QWidget, QGridLayout, QLabel
from PySide6.QtCore import QThread, Signal, Slot, Qt
from PySide6.QtGui import QImage, QPixmap
import numpy as np
import argparse
import os
import platform
import numpy as np
from pathlib import Path

import torch

FILE = Path(file).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative

from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode

class CameraThread(QThread):
changePixmap = Signal(QPixmap)

def __init__(self, rtsp_link):
    super(CameraThread, self).__init__()
    self.rtsp_link = rtsp_link
    self.is_running = True
    self.device = None

def run(self):
    source = str(self.rtsp_link)
    IMGZ = (640,640)

    device = select_device("0,1")
    data = ROOT / 'data/coco128.yaml'
    model = DetectMultiBackend(weights=ROOT / 'custom_models/ALL_M2.pt', device=device, dnn=False, data = data, fp16=False)
    print("using Device:",self.device)

    stride, names, pt = model.stride, model.names, model.pt
    IMGSZ = check_img_size(IMGZ, s = stride)

    dataset = LoadStreams(source, img_size=IMGZ, stride=stride, auto=pt, vid_stride=1)
    bs = len(dataset)
    model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *IMGSZ))
    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())

    for path, im, im0s, vid_cap, s in dataset:
        with dt[0]:
            im = torch.from_numpy(im).to(model.device)
            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
            im /= 255  # 0 - 255 to 0.0 - 1.0
            if len(im.shape) == 3:
                im = im[None]  # expand for batch dim

        with dt[1]:
            pred = model(im, augment=False)

        with dt[2]:
            with torch.no_grad():
                pred = non_max_suppression(pred,0.25, 0.45,[5,6], False, max_det=1000)

        for i, det in enumerate(pred):
            seen += 1
            im0, frame = im0s[i].copy(), dataset.count
            annotator = Annotator(im0, line_width=3, example=str(names))
            if len(det):
                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()

                for *xyxy, conf, cls in reversed(det):
                    c = int(cls)
                    center = (int((xyxy[0] + xyxy[2]) / 2), int((xyxy[1] + xyxy[3]) / 2))
                    label = f'{names[c]} {conf:.2f} {center}'
                    annotator.box_label(xyxy, label, color=colors(c, True))

            im0 = annotator.result()
            ratio_roi = ast.literal_eval(str("[[[ 0.19,0.39],[0.38,0.3],[0.4,0.63],[0.19,0.87]]]"))
            roi = [[[item[0] * im0.shape[1], item[1] * im0.shape[0]] for item in sublist] for sublist in ratio_roi]
            pts1 = np.array(roi, dtype=np.int32)
            # print(pts1)
            cv2.polylines(im0, [pts1], True, (255, 255, 255), 4)
            rgb_image = cv2.cvtColor(im0, cv2.COLOR_BGR2RGB)
            h, w, ch = rgb_image.shape
            bytes_per_line = ch * w
            rgb_image = cv2.resize(rgb_image,(w,h), cv2.INTER_AREA)
            qt_image = QImage(rgb_image.data, w, h, bytes_per_line, QImage.Format_RGB888)
            pixmap = QPixmap.fromImage(qt_image)
            self.changePixmap.emit(pixmap)



def stop(self):
    self.is_running = False
    self.wait()

class CameraWidget(QLabel):
def init(self, rtsp_link, parent=None):
super(CameraWidget, self).init(parent)
self._pixmap = QPixmap()
self.cameraThread = CameraThread(rtsp_link)
self.cameraThread.changePixmap.connect(self.setVideo)
self.cameraThread.start()

@Slot(QPixmap)
def setVideo(self, image):
    self._pixmap = image
    self.updatePixmap()

def resizeEvent(self, event):
    self.updatePixmap()

def updatePixmap(self):
    if not self._pixmap.isNull():
        pixmap = self._pixmap.scaled(self.width(), self.height(), Qt.KeepAspectRatio)
        self.setPixmap(pixmap)

class MyApp(QWidget):
def init(self, rtsp_links):
super().init()
self.rtsp_links = rtsp_links
self.initUI()

def initUI(self):
    grid = QGridLayout()
    self.setLayout(grid)
    for i, rtsp_link in enumerate(self.rtsp_links):
        camera_widget = CameraWidget(rtsp_link)
        grid.addWidget(camera_widget, i//4, i%4)
    self.setWindowTitle('Camera Viewer')
    self.resize(1920, 1080)
    self.show()

if name == 'main':
rtsp_addresses = ['rtsp1','rtsp2','rtsp3','rtsp4','rtsp5','rtsp6','rtsp7','rtsp8']
app = QApplication(sys.argv)
ex = MyApp(rtsp_addresses)
sys.exit(app.exec_())
`

If you run this code and you run the device with cpu, you run it well, but if you run with gpu, you can see a screen or two and it stops I'd appreciate it if you could tell me what the problem is

Additional

No response

[github-actions]

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[glenn-jocher]

@kmsdoit hi,

Based on your code, it appears that you are using the PyQT5 library to process multiple videos with the YOLOv5 model. It is possible to use the PyQT5 library in conjunction with the YOLOv5 model for video processing.

However, regarding the issue you mentioned where the code stops when running with a GPU, it is likely that there is a problem related to GPU utilization or compatibility. To further investigate and diagnose the problem, it would be helpful to provide additional information such as error messages or log files.

Please ensure that you have the necessary GPU drivers, CUDA, and cuDNN installed and compatible with the version of PyTorch and YOLOv5 you are using. Additionally, make sure that your GPU is properly recognized and utilized by the code.

If you are still experiencing difficulties, it may be helpful to consult the YOLOv5 documentation, or seek assistance from the YOLOv5 GitHub community, where fellow developers and contributors can provide more specific guidance.

Hope this helps! Let me know if you have any further questions.

[github-actions]

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