Allow PyTorch Hub results to display in notebooks (#9825)

* Allow PyTorch Hub results to display in notebooks

* fix CI

* fix CI

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix CI

* fix CI

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix CI

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix CI

* fix CI

* fix CI

* fix CI

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix CI

* fix CI

* fix CI

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

classify/predict.py

@@ -91,7 +91,7 @@ def run(
     # Dataloader
     bs = 1  # batch_size
     if webcam:
-        view_img = check_imshow()
+        view_img = check_imshow(warn=True)
         dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
         bs = len(dataset)
     elif screenshot:

detect.py

@@ -99,7 +99,7 @@ def run(
     # Dataloader
     bs = 1  # batch_size
     if webcam:
-        view_img = check_imshow()
+        view_img = check_imshow(warn=True)
         dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
         bs = len(dataset)
     elif screenshot:

models/common.py

@@ -18,16 +18,20 @@ import pandas as pd
 import requests
 import torch
 import torch.nn as nn
+from IPython.display import display
 from PIL import Image
 from torch.cuda import amp
 
+from utils import TryExcept
 from utils.dataloaders import exif_transpose, letterbox
-from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
+from utils.general import (LOGGER, ROOT, Profile, check_imshow, check_requirements, check_suffix, check_version,
-                           increment_path, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy, xyxy2xywh,
+                           colorstr, increment_path, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
-                           yaml_load)
+                           xyxy2xywh, yaml_load)
 from utils.plots import Annotator, colors, save_one_box
 from utils.torch_utils import copy_attr, smart_inference_mode
 
+CHECK_IMSHOW = check_imshow()
+
 
 def autopad(k, p=None, d=1):  # kernel, padding, dilation
     # Pad to 'same' shape outputs
@@ -756,7 +760,7 @@ class Detections:
 
             im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
             if show:
-                im.show(self.files[i])  # show
+                im.show(self.files[i]) if CHECK_IMSHOW else display(im)
             if save:
                 f = self.files[i]
                 im.save(save_dir / f)  # save
@@ -772,6 +776,7 @@ class Detections:
                 LOGGER.info(f'Saved results to {save_dir}\n')
             return crops
 
+    @TryExcept('Showing images is not supported in this environment')
     def show(self, labels=True):
         self._run(show=True, labels=labels)  # show results
 

segment/predict.py

@@ -102,7 +102,7 @@ def run(
     # Dataloader
     bs = 1  # batch_size
     if webcam:
-        view_img = check_imshow()
+        view_img = check_imshow(warn=True)
         dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
         bs = len(dataset)
     elif screenshot:

utils/__init__.py

@@ -23,7 +23,7 @@ class TryExcept(contextlib.ContextDecorator):
 
     def __exit__(self, exc_type, value, traceback):
         if value:
-            print(emojis(f'{self.msg}{value}'))
+            print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
         return True
 
 

utils/autoanchor.py

@@ -26,7 +26,7 @@ def check_anchor_order(m):
         m.anchors[:] = m.anchors.flip(0)
 
 
-@TryExcept(f'{PREFIX}ERROR: ')
+@TryExcept(f'{PREFIX}ERROR')
 def check_anchors(dataset, model, thr=4.0, imgsz=640):
     # Check anchor fit to data, recompute if necessary
     m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()

utils/general.py

@@ -27,6 +27,7 @@ from typing import Optional
 from zipfile import ZipFile
 
 import cv2
+import IPython
 import numpy as np
 import pandas as pd
 import pkg_resources as pkg
@@ -73,6 +74,12 @@ def is_colab():
     return 'COLAB_GPU' in os.environ
 
 
+def is_notebook():
+    # Is environment a Jupyter notebook? Verified on Colab, Jupyterlab, Kaggle, Paperspace
+    ipython_type = str(type(IPython.get_ipython()))
+    return 'colab' in ipython_type or 'zmqshell' in ipython_type
+
+
 def is_kaggle():
     # Is environment a Kaggle Notebook?
     return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com'
@@ -383,18 +390,20 @@ def check_img_size(imgsz, s=32, floor=0):
     return new_size
 
 
-def check_imshow():
+def check_imshow(warn=False):
     # Check if environment supports image displays
     try:
-        assert not is_docker(), 'cv2.imshow() is disabled in Docker environments'
+        assert not is_notebook()
-        assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments'
+        assert not is_docker()
+        assert 'NoneType' not in str(type(IPython.get_ipython()))  # SSH terminals, GitHub CI
         cv2.imshow('test', np.zeros((1, 1, 3)))
         cv2.waitKey(1)
         cv2.destroyAllWindows()
         cv2.waitKey(1)
         return True
     except Exception as e:
-        LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
+        if warn:
+            LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}')
         return False
 
 

utils/metrics.py

@@ -186,7 +186,7 @@ class ConfusionMatrix:
         # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)
         return tp[:-1], fp[:-1]  # remove background class
 
-    @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure: ')
+    @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
     def plot(self, normalize=True, save_dir='', names=()):
         import seaborn as sn