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load model by torch.hub.load() and export polys pixel #529

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load model by torch.hub.load() and export polys pixel #529

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26 changes: 16 additions & 10 deletions models/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,10 +22,10 @@

from utils.datasets import exif_transpose, letterbox
from utils.general import (LOGGER, check_requirements, check_suffix, check_version, colorstr, increment_path,
make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh)
make_divisible, non_max_suppression, non_max_suppression_obb, scale_coords, scale_polys, xywh2xyxy, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import copy_attr, time_sync

from utils.rboxs_utils import rbox2poly, poly2hbb

def autopad(k, p=None): # kernel, padding
# Pad to 'same'
Expand Down Expand Up @@ -515,28 +515,34 @@ def forward(self, imgs, size=640, augment=False, profile=False):
t.append(time_sync())

# Post-process
y = non_max_suppression(y if self.dmb else y[0], self.conf, iou_thres=self.iou, classes=self.classes,
agnostic=self.agnostic, multi_label=self.multi_label, max_det=self.max_det) # NMS
for i in range(n):
scale_coords(shape1, y[i][:, :4], shape0[i])
y = non_max_suppression_obb(y, self.conf, self.iou, self.classes, self.agnostic, self.multi_label, max_det=self.max_det)
pred = []
for i, det in enumerate(y):
pred_poly = rbox2poly(det[:, :5])
if len(det):
pred_poly = scale_polys(shape1, pred_poly, shape0[i])
det = torch.cat((pred_poly, det[:, -2:]), dim=1)
pred.append(det)

t.append(time_sync())
return Detections(imgs, y, files, t, self.names, x.shape)
return Detections(imgs, pred, files, t, self.names, x.shape)


class Detections:
# YOLOv5 detections class for inference results
def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None):
super().__init__()
d = pred[0].device # device
gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations
gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0])], device=d) for im in imgs] # normalizations
self.imgs = imgs # list of images as numpy arrays
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
self.names = names # class names
self.files = files # image filenames
self.times = times # profiling times
self.xyxy = pred # xyxy pixels
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
self.polys = [p[:, :8] for p in pred]
self.xyxy = [poly2hbb(p[:, :8]) for p in pred]
self.xywh = [poly2hbb(p[:, :8]) for p in pred] # xywh pixels
self.xyxy = [xywh2xyxy(x) for x in self.xywh] # xyxy pixels
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
self.n = len(self.pred) # number of images (batch size)
Expand Down