New Colors() class (#2963)

detect.py

@@ -11,7 +11,7 @@ from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
 from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
     scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
-from utils.plots import plot_one_box
+from utils.plots import colors, plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
@@ -34,6 +34,7 @@ def detect(opt):
     model = attempt_load(weights, map_location=device)  # load FP32 model
     stride = int(model.stride.max())  # model stride
     imgsz = check_img_size(imgsz, s=stride)  # check img_size
+    names = model.module.names if hasattr(model, 'module') else model.names  # get class names
     if half:
         model.half()  # to FP16
 
@@ -52,10 +53,6 @@ def detect(opt):
     else:
         dataset = LoadImages(source, img_size=imgsz, stride=stride)
 
-    # Get names and colors
-    names = model.module.names if hasattr(model, 'module') else model.names
-    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
-
     # Run inference
     if device.type != 'cpu':
         model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
@@ -112,7 +109,7 @@ def detect(opt):
                         c = int(cls)  # integer class
                         label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
 
-                        plot_one_box(xyxy, im0, label=label, color=colors[c], line_thickness=opt.line_thickness)
+                        plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness)
                         if opt.save_crop:
                             save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
 

models/common.py

@@ -14,7 +14,7 @@ from torch.cuda import amp
 
 from utils.datasets import letterbox
 from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
-from utils.plots import color_list, plot_one_box
+from utils.plots import colors, plot_one_box
 from utils.torch_utils import time_synchronized
 
 
@@ -312,7 +312,6 @@ class Detections:
         self.s = shape  # inference BCHW shape
 
     def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
-        colors = color_list()
         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]} '
             if pred is not None:
@@ -325,7 +324,7 @@ class Detections:
                         if crop:
                             save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
                         else:  # all others
-                            plot_one_box(box, im, label=label, color=colors[int(cls) % 10])
+                            plot_one_box(box, im, label=label, color=colors(cls))
 
             im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
             if pprint:

utils/plots.py

@@ -26,12 +26,22 @@ matplotlib.rc('font', **{'size': 11})
 matplotlib.use('Agg')  # for writing to files only
 
 
-def color_list():
+class Colors:
-    # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
+    # Ultralytics color palette https://ultralytics.com/
-    def hex2rgb(h):
+    def __init__(self):
+        self.palette = [self.hex2rgb(c) for c in matplotlib.colors.TABLEAU_COLORS.values()]
+        self.n = len(self.palette)
+
+    def __call__(self, i, bgr=False):
+        c = self.palette[int(i) % self.n]
+        return (c[2], c[1], c[0]) if bgr else c
+
+    @staticmethod
+    def hex2rgb(h):  # rgb order (PIL)
         return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
 
-    return [hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values()]  # or BASE_ (8), CSS4_ (148), XKCD_ (949)
+
+colors = Colors()  # create instance for 'from utils.plots import colors'
 
 
 def hist2d(x, y, n=100):
@@ -137,7 +147,6 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
         h = math.ceil(scale_factor * h)
         w = math.ceil(scale_factor * w)
 
-    colors = color_list()  # list of colors
     mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init
     for i, img in enumerate(images):
         if i == max_subplots:  # if last batch has fewer images than we expect
@@ -168,7 +177,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
             boxes[[1, 3]] += block_y
             for j, box in enumerate(boxes.T):
                 cls = int(classes[j])
-                color = colors[cls % len(colors)]
+                color = colors(cls)
                 cls = names[cls] if names else cls
                 if labels or conf[j] > 0.25:  # 0.25 conf thresh
                     label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j])
@@ -276,7 +285,6 @@ def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
     print('Plotting labels... ')
     c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
     nc = int(c.max() + 1)  # number of classes
-    colors = color_list()
     x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
 
     # seaborn correlogram
@@ -302,7 +310,7 @@ def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
     labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
     img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
     for cls, *box in labels[:1000]:
-        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10])  # plot
+        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls))  # plot
     ax[1].imshow(img)
     ax[1].axis('off')