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inference.py
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inference.py
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import cv2
import torch
import torch.backends.cudnn as cudnn
from models.experimental import attempt_load
from utils.streamUtils import *
from utils.utils import *
from utils.annotateUtils import *
device = 'cpu'
@torch.no_grad()
def inference(weights='weights/yolov5s.pt', source=0, imgsz=640, hide_labels=False, hide_conf=True):
stride, names = 64, ["class{}".format(i for i in range(1000))]
model = attempt_load(weights, map_location=device)
stride = int(model.stride.max())
names = model.module.names if hasattr(model, 'module') else model.names
cudnn.benchmark = True
stream = vidLoader(source, img_size=imgsz, stride=stride)
seen = 0
for path, img, im0s in stream:
img = torch.from_numpy(img).to(device)
img = img.float()
img = img / 255.0
if len(img.shape) == 3:
img = img[None]
pred = model(img)[0]
pred = nms(pred, conf_thres=0.25, iou_thres=0.45, max_det=500)
for i, det in enumerate(pred):
seen += 1
p, im0 = path[i], im0s[i].copy()
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
pen = MagicPen(im0)
if len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
for *xyxy, conf, cls in reversed(det):
c = int(cls)
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
d=dist_calc(xywh)
label = label+". Distance = "+str(d)
color=Colors()
pen.box_label(xyxy, label, color=color(c, True))
######### Showing Result ###########
im0 = pen.result()
cv2.imshow(str(p), im0)
cv2.waitKey(1)
if __name__ == "__main__":
inference()