from models.experimental import attempt_load | from models.experimental import attempt_load | ||||
from utils.datasets import LoadStreams, LoadImages | from utils.datasets import LoadStreams, LoadImages | ||||
from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \ | |||||
from utils.general import check_img_size, check_requirements, check_imshow, colorstr, is_ascii, non_max_suppression, \ | |||||
apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box | apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box | ||||
from utils.plots import colors, Annotator | |||||
from utils.plots import Annotator, colors | |||||
from utils.torch_utils import select_device, load_classifier, time_sync | from utils.torch_utils import select_device, load_classifier, time_sync | ||||
output_details = interpreter.get_output_details() # outputs | output_details = interpreter.get_output_details() # outputs | ||||
int8 = input_details[0]['dtype'] == np.uint8 # is TFLite quantized uint8 model | int8 = input_details[0]['dtype'] == np.uint8 # is TFLite quantized uint8 model | ||||
imgsz = check_img_size(imgsz, s=stride) # check image size | imgsz = check_img_size(imgsz, s=stride) # check image size | ||||
ascii = is_ascii(names) # names are ascii (use PIL for UTF-8) | |||||
# Dataloader | # Dataloader | ||||
if webcam: | if webcam: | ||||
s += '%gx%g ' % img.shape[2:] # print string | s += '%gx%g ' % img.shape[2:] # print string | ||||
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh | ||||
imc = im0.copy() if save_crop else im0 # for save_crop | imc = im0.copy() if save_crop else im0 # for save_crop | ||||
annotator = Annotator(im0, line_width=line_thickness, pil=False) | |||||
annotator = Annotator(im0, line_width=line_thickness, pil=not ascii) | |||||
if len(det): | if len(det): | ||||
# Rescale boxes from img_size to im0 size | # Rescale boxes from img_size to im0 size | ||||
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() |
from torch.cuda import amp | from torch.cuda import amp | ||||
from utils.datasets import exif_transpose, letterbox | from utils.datasets import exif_transpose, letterbox | ||||
from utils.general import colorstr, non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, \ | |||||
save_one_box | |||||
from utils.plots import colors, Annotator | |||||
from utils.general import colorstr, increment_path, is_ascii, make_divisible, non_max_suppression, save_one_box, \ | |||||
scale_coords, xyxy2xywh | |||||
from utils.plots import Annotator, colors | |||||
from utils.torch_utils import time_sync | from utils.torch_utils import time_sync | ||||
LOGGER = logging.getLogger(__name__) | LOGGER = logging.getLogger(__name__) | ||||
self.imgs = imgs # list of images as numpy arrays | self.imgs = imgs # list of images as numpy arrays | ||||
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) | self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) | ||||
self.names = names # class names | self.names = names # class names | ||||
self.ascii = is_ascii(names) # names are ascii (use PIL for UTF-8) | |||||
self.files = files # image filenames | self.files = files # image filenames | ||||
self.xyxy = pred # xyxy pixels | self.xyxy = pred # xyxy pixels | ||||
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels | self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels | ||||
n = (pred[:, -1] == c).sum() # detections per class | n = (pred[:, -1] == c).sum() # detections per class | ||||
str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string | str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string | ||||
if show or save or render or crop: | if show or save or render or crop: | ||||
annotator = Annotator(im, pil=False) | |||||
annotator = Annotator(im, pil=not self.ascii) | |||||
for *box, conf, cls in reversed(pred): # xyxy, confidence, class | for *box, conf, cls in reversed(pred): # xyxy, confidence, class | ||||
label = f'{self.names[int(cls)]} {conf:.2f}' | label = f'{self.names[int(cls)]} {conf:.2f}' | ||||
if crop: | if crop: |
def is_ascii(s=''): | def is_ascii(s=''): | ||||
# Is string composed of all ASCII (no UTF) characters? | # Is string composed of all ASCII (no UTF) characters? | ||||
s = str(s) # convert to str() in case of None, etc. | |||||
s = str(s) # convert list, tuple, None, etc. to str | |||||
return len(s.encode().decode('ascii', 'ignore')) == len(s) | return len(s.encode().decode('ascii', 'ignore')) == len(s) | ||||