zjc
/
yolov5
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Automatic Chinese fonts plotting (#4951) 

* Automatic Chinese fonts plotting

* Default PIL=False
This commit is contained in:
Glenn Jocher 2021-09-27 13:48:15 -07:00 committed by GitHub
parent c5ba2abb4a
commit a820b43aca
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 18 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
detect.py
View File

@ -23,7 +23,7 @@ if str(ROOT) not in sys.path:
from models.experimental import attempt_load
from utils.datasets import LoadImages, LoadStreams
from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \
increment_path, is_ascii, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \
increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \
strip_optimizer, xyxy2xywh
from utils.plots import Annotator, colors
from utils.torch_utils import load_classifier, select_device, time_sync
@ -108,7 +108,6 @@ def run(weights='yolov5s.pt', # model.pt path(s)
output_details = interpreter.get_output_details() # outputs
int8 = input_details[0]['dtype'] == np.uint8 # is TFLite quantized uint8 model
imgsz = check_img_size(imgsz, s=stride) # check image size
ascii = is_ascii(names) # names are ascii (use PIL for UTF-8)
# Dataloader
if webcam:
@ -190,7 +189,7 @@ def run(weights='yolov5s.pt', # model.pt path(s)
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, pil=not ascii)
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

13
models/common.py
View File

@ -18,7 +18,7 @@ from PIL import Image
from torch.cuda import amp
from utils.datasets import exif_transpose, letterbox
from utils.general import colorstr, increment_path, is_ascii, make_divisible, non_max_suppression, save_one_box, \
from utils.general import colorstr, increment_path, make_divisible, non_max_suppression, save_one_box, \
scale_coords, xyxy2xywh
from utils.plots import Annotator, colors
from utils.torch_utils import time_sync
@ -356,7 +356,6 @@ class Detections:
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.ascii = is_ascii(names) # names are ascii (use PIL for UTF-8)
self.files = files # image filenames
self.xyxy = pred # xyxy pixels
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
@ -369,13 +368,13 @@ class Detections:
def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
crops = []
for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
if pred.shape[0]:
for c in pred[:, -1].unique():
n = (pred[:, -1] == c).sum() # detections per class
str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
if show or save or render or crop:
annotator = Annotator(im, pil=not self.ascii)
annotator = Annotator(im, example=str(self.names))
for *box, conf, cls in reversed(pred): # xyxy, confidence, class
label = f'{self.names[int(cls)]} {conf:.2f}'
if crop:
@ -386,11 +385,11 @@ class Detections:
annotator.box_label(box, label, color=colors(cls))
im = annotator.im
else:
str += '(no detections)'
s += '(no detections)'
im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
if pprint:
LOGGER.info(str.rstrip(', '))
LOGGER.info(s.rstrip(', '))
if show:
im.show(self.files[i]) # show
if save:

7
utils/general.py
View File

@ -161,10 +161,9 @@ def is_pip():
return 'site-packages' in Path(__file__).resolve().parts
def is_ascii(s=''):
# Is string composed of all ASCII (no UTF) characters?
s = str(s) # convert list, tuple, None, etc. to str
return len(s.encode().decode('ascii', 'ignore')) == len(s)
def is_chinese(s='人工智能'):
# Is string composed of any Chinese characters?
return re.search('[\u4e00-\u9fff]', s)
def emojis(str=''):

13
utils/plots.py
View File

@ -17,7 +17,7 @@ import seaborn as sn
import torch
from PIL import Image, ImageDraw, ImageFont
from utils.general import user_config_dir, is_ascii, xywh2xyxy, xyxy2xywh
from utils.general import user_config_dir, is_chinese, xywh2xyxy, xyxy2xywh
from utils.metrics import fitness
# Settings
@ -66,20 +66,21 @@ class Annotator:
check_font() # download TTF if necessary
# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=True):
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
self.pil = pil
self.pil = pil or not example.isascii() or is_chinese(example)
if self.pil: # use PIL
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
self.draw = ImageDraw.Draw(self.im)
self.font = check_font(font, size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
self.font = check_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
else: # use cv2
self.im = im
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
# Add one xyxy box to image with label
if self.pil or not is_ascii(label):
if self.pil or not label.isascii():
self.draw.rectangle(box, width=self.lw, outline=color) # box
if label:
w, h = self.font.getsize(label) # text width, height
@ -177,7 +178,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
# Annotate
fs = int((h + w) * ns * 0.01) # font size
annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs)
annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True)
for i in range(i + 1):
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders