Create `Annotator()` class (#4591)

* Add Annotator() class

* Download Arial

* 2x for loop

* Cleanup

* tuple 2 list

* max_size=1920

* bold logging results to

* tolist()

* im = annotator.im

* PIL save in detect.py

* Smart asarray in detect.py

* revert to cv2.imwrite

* Cleanup

* Return result asarray

* Add `Profile()` profiler

* CamelCase Timeout

* Resize after mosaic

* pillow>=8.0.0

* daemon imwrite

* Add cv2 support

* Remove plot_wh_methods and plot_one_box

* pil=False for hubconf.py annotations

* im.shape bug fix

* colorstr common.py

* join daemons

* Update t.daemon

* Removed daemon saving

detect.py

@@ -23,7 +23,7 @@ from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
 from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
     apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
-from utils.plots import colors, plot_one_box
+from utils.plots import colors, Annotator
 from utils.torch_utils import select_device, load_classifier, time_sync
 
 
@@ -181,6 +181,7 @@ 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=False)
             if len(det):
                 # Rescale boxes from img_size to im0 size
                 det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
@@ -201,7 +202,7 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
                     if save_img or save_crop or view_img:  # Add bbox to image
                         c = int(cls)  # integer class
                         label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
-                        im0 = plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_width=line_thickness)
+                        annotator.box_label(xyxy, label, color=colors(c, True))
                         if save_crop:
                             save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
 
@@ -209,6 +210,7 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
             print(f'{s}Done. ({t2 - t1:.3f}s)')
 
             # Stream results
+            im0 = annotator.result()
             if view_img:
                 cv2.imshow(str(p), im0)
                 cv2.waitKey(1)  # 1 millisecond

models/common.py

@@ -18,8 +18,9 @@ from PIL import Image
 from torch.cuda import amp
 
 from utils.datasets import exif_transpose, letterbox
-from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
-from utils.plots import colors, plot_one_box
+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.torch_utils import time_sync
 
 LOGGER = logging.getLogger(__name__)
@@ -370,12 +371,14 @@ class Detections:
                     n = (pred[:, -1] == c).sum()  # detections per class
                     str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
                 if show or save or render or crop:
+                    annotator = Annotator(im, pil=False)
                     for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
                         label = f'{self.names[int(cls)]} {conf:.2f}'
                         if crop:
                             save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
                         else:  # all others
-                            im = plot_one_box(box, im, label=label, color=colors(cls))
+                            annotator.box_label(box, label, color=colors(cls))
+                    im = annotator.im
             else:
                 str += '(no detections)'
 
@@ -388,7 +391,7 @@ class Detections:
                 f = self.files[i]
                 im.save(save_dir / f)  # save
                 if i == self.n - 1:
-                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to '{save_dir}'")
+                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
             if render:
                 self.imgs[i] = np.asarray(im)
 

requirements.txt

@@ -4,7 +4,7 @@
 matplotlib>=3.2.2
 numpy>=1.18.5
 opencv-python>=4.1.2
-Pillow
+Pillow>=8.0.0
 PyYAML>=5.3.1
 scipy>=1.4.1
 torch>=1.7.0

train.py

@@ -260,7 +260,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     compute_loss = ComputeLoss(model)  # init loss class
     LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
                 f'Using {train_loader.num_workers} dataloader workers\n'
-                f'Logging results to {save_dir}\n'
+                f"Logging results to {colorstr('bold', save_dir)}\n"
                 f'Starting training for {epochs} epochs...')
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         model.train()

utils/general.py

@@ -122,9 +122,10 @@ def is_pip():
     return 'site-packages' in Path(__file__).absolute().parts
 
 
-def is_ascii(str=''):
+def is_ascii(s=''):
     # Is string composed of all ASCII (no UTF) characters?
-    return len(str.encode().decode('ascii', 'ignore')) == len(str)
+    s = str(s)  # convert to str() in case of None, etc.
+    return len(s.encode().decode('ascii', 'ignore')) == len(s)
 
 
 def emojis(str=''):

utils/plots.py

@@ -67,51 +67,59 @@ def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
     return filtfilt(b, a, data)  # forward-backward filter
 
 
-def plot_one_box(box, im, color=(128, 128, 128), txt_color=(255, 255, 255), label=None, line_width=3, use_pil=False):
-    # Plots one xyxy box on image im with label
-    assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'
-    lw = line_width or max(int(min(im.size) / 200), 2)  # line width
-
-    if use_pil or (label is not None and not is_ascii(label)):  # use PIL
-        im = Image.fromarray(im)
-        draw = ImageDraw.Draw(im)
-        draw.rectangle(box, width=lw + 1, outline=color)  # plot
-        if label:
-            font = ImageFont.truetype("Arial.ttf", size=max(round(max(im.size) / 40), 12))
-            txt_width, txt_height = font.getsize(label)
-            draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=color)
-            draw.text((box[0], box[1] - txt_height + 1), label, fill=txt_color, font=font)
-        return np.asarray(im)
-    else:  # use OpenCV
-        c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-        cv2.rectangle(im, c1, c2, color, thickness=lw, lineType=cv2.LINE_AA)
-        if label:
-            tf = max(lw - 1, 1)  # font thickness
-            txt_width, txt_height = cv2.getTextSize(label, 0, fontScale=lw / 3, thickness=tf)[0]
-            c2 = c1[0] + txt_width, c1[1] - txt_height - 3
-            cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA)  # filled
-            cv2.putText(im, label, (c1[0], c1[1] - 2), 0, lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
-        return im
-
-
-def plot_wh_methods():  # from utils.plots import *; plot_wh_methods()
-    # Compares the two methods for width-height anchor multiplication
-    # https://github.com/ultralytics/yolov3/issues/168
-    x = np.arange(-4.0, 4.0, .1)
-    ya = np.exp(x)
-    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
-
-    fig = plt.figure(figsize=(6, 3), tight_layout=True)
-    plt.plot(x, ya, '.-', label='YOLOv3')
-    plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2')
-    plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6')
-    plt.xlim(left=-4, right=4)
-    plt.ylim(bottom=0, top=6)
-    plt.xlabel('input')
-    plt.ylabel('output')
-    plt.grid()
-    plt.legend()
-    fig.savefig('comparison.png', dpi=200)
+class Annotator:
+    # YOLOv5 PIL Annotator class
+    def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=True):
+        assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'
+        self.pil = pil
+        if self.pil:  # use PIL
+            self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
+            self.draw = ImageDraw.Draw(self.im)
+            s = sum(self.im.size) / 2  # mean shape
+            f = font_size or max(round(s * 0.035), 12)
+            try:
+                self.font = ImageFont.truetype(font, size=f)
+            except:  # download TTF
+                url = "https://github.com/ultralytics/yolov5/releases/download/v1.0/" + font
+                torch.hub.download_url_to_file(url, font)
+                self.font = ImageFont.truetype(font, size=f)
+            self.fh = self.font.getsize('a')[1] - 3  # font height
+        else:  # use cv2
+            self.im = im
+        s = sum(im.shape) / 2  # mean shape
+        self.lw = line_width or max(round(s * 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):
+            self.draw.rectangle(box, width=self.lw, outline=color)  # box
+            if label:
+                w = self.font.getsize(label)[0]  # text width
+                self.draw.rectangle([box[0], box[1] - self.fh, box[0] + w + 1, box[1] + 1], fill=color)
+                self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')
+        else:  # cv2
+            c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+            cv2.rectangle(self.im, c1, c2, color, thickness=self.lw, lineType=cv2.LINE_AA)
+            if label:
+                tf = max(self.lw - 1, 1)  # font thickness
+                w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0]
+                c2 = c1[0] + w, c1[1] - h - 3
+                cv2.rectangle(self.im, c1, c2, color, -1, cv2.LINE_AA)  # filled
+                cv2.putText(self.im, label, (c1[0], c1[1] - 2), 0, self.lw / 3, txt_color, thickness=tf,
+                            lineType=cv2.LINE_AA)
+
+    def rectangle(self, xy, fill=None, outline=None, width=1):
+        # Add rectangle to image (PIL-only)
+        self.draw.rectangle(xy, fill, outline, width)
+
+    def text(self, xy, text, txt_color=(255, 255, 255)):
+        # Add text to image (PIL-only)
+        w, h = self.font.getsize(text)  # text width, height
+        self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)
+
+    def result(self):
+        # Return annotated image as array
+        return np.asarray(self.im)
 
 
 def output_to_target(output):
@@ -123,82 +131,65 @@ def output_to_target(output):
     return np.array(targets)
 
 
-def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
+def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=1920, max_subplots=16):
     # Plot image grid with labels
-
     if isinstance(images, torch.Tensor):
         images = images.cpu().float().numpy()
     if isinstance(targets, torch.Tensor):
         targets = targets.cpu().numpy()
-
-    # un-normalise
     if np.max(images[0]) <= 1:
-        images *= 255
-
-    tl = 3  # line thickness
-    tf = max(tl - 1, 1)  # font thickness
+        images *= 255.0  # de-normalise (optional)
     bs, _, h, w = images.shape  # batch size, _, height, width
     bs = min(bs, max_subplots)  # limit plot images
     ns = np.ceil(bs ** 0.5)  # number of subplots (square)
 
-    # Check if we should resize
-    scale_factor = max_size / max(h, w)
-    if scale_factor < 1:
-        h = math.ceil(scale_factor * h)
-        w = math.ceil(scale_factor * w)
-
+    # Build Image
     mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init
-    for i, img in enumerate(images):
+    for i, im in enumerate(images):
         if i == max_subplots:  # if last batch has fewer images than we expect
             break
-
-        block_x = int(w * (i // ns))
-        block_y = int(h * (i % ns))
-
-        img = img.transpose(1, 2, 0)
-        if scale_factor < 1:
-            img = cv2.resize(img, (w, h))
-
-        mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
+        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
+        im = im.transpose(1, 2, 0)
+        mosaic[y:y + h, x:x + w, :] = im
+
+    # Resize (optional)
+    scale = max_size / ns / max(h, w)
+    if scale < 1:
+        h = math.ceil(scale * h)
+        w = math.ceil(scale * w)
+        mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
+
+    # Annotate
+    fs = int(h * ns * 0.02)  # font size
+    annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs)
+    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
+        if paths:
+            annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
         if len(targets) > 0:
-            image_targets = targets[targets[:, 0] == i]
-            boxes = xywh2xyxy(image_targets[:, 2:6]).T
-            classes = image_targets[:, 1].astype('int')
-            labels = image_targets.shape[1] == 6  # labels if no conf column
-            conf = None if labels else image_targets[:, 6]  # check for confidence presence (label vs pred)
+            ti = targets[targets[:, 0] == i]  # image targets
+            boxes = xywh2xyxy(ti[:, 2:6]).T
+            classes = ti[:, 1].astype('int')
+            labels = ti.shape[1] == 6  # labels if no conf column
+            conf = None if labels else ti[:, 6]  # check for confidence presence (label vs pred)
 
             if boxes.shape[1]:
                 if boxes.max() <= 1.01:  # if normalized with tolerance 0.01
                     boxes[[0, 2]] *= w  # scale to pixels
                     boxes[[1, 3]] *= h
-                elif scale_factor < 1:  # absolute coords need scale if image scales
-                    boxes *= scale_factor
-            boxes[[0, 2]] += block_x
-            boxes[[1, 3]] += block_y
-            for j, box in enumerate(boxes.T):
-                cls = int(classes[j])
+                elif scale < 1:  # absolute coords need scale if image scales
+                    boxes *= scale
+            boxes[[0, 2]] += x
+            boxes[[1, 3]] += y
+            for j, box in enumerate(boxes.T.tolist()):
+                cls = classes[j]
                 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])
-                    mosaic = plot_one_box(box, mosaic, label=label, color=color, line_width=tl)
-
-        # Draw image filename labels
-        if paths:
-            label = Path(paths[i]).name[:40]  # trim to 40 char
-            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
-            cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
-                        lineType=cv2.LINE_AA)
-
-        # Image border
-        cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3)
-
-    if fname:
-        r = min(1280. / max(h, w) / ns, 1.0)  # ratio to limit image size
-        mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)
-        # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))  # cv2 save
-        Image.fromarray(mosaic).save(fname)  # PIL save
-    return mosaic
+                    label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
+                    annotator.box_label(box, label, color=color)
+    annotator.im.save(fname)  # save
 
 
 def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):