Ban module level imports for cv2, matplotlib and numpy (#292)

pyproject.toml

@@ -75,7 +75,6 @@ exclude = [
     "tests/manual/debugme.py", # file is intentionally broken
 ]
 
-
 # Allow unused variables when underscore-prefixed.
 dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
 
@@ -92,6 +91,13 @@ convention = "google"
 # Preserve types, even if a file imports `from __future__ import annotations`.
 keep-runtime-typing = true
 
+[tool.ruff.lint.flake8-tidy-imports]
+banned-module-level-imports = [
+    "cv2",
+    "matplotlib",
+    "numpy",
+]
+
 [tool.mypy]
 python_version = "3.8"
 exclude = ["^build/"]

roboflow/core/version.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import copy
 import json
 import os
@@ -6,9 +8,8 @@
 import time
 import zipfile
 from importlib import import_module
-from typing import Optional, Union
+from typing import TYPE_CHECKING, Optional, Union
 
-import numpy as np
 import requests
 import yaml
 from dotenv import load_dotenv
@@ -28,7 +29,6 @@
 )
 from roboflow.core.dataset import Dataset
 from roboflow.models.classification import ClassificationModel
-from roboflow.models.inference import InferenceModel
 from roboflow.models.instance_segmentation import InstanceSegmentationModel
 from roboflow.models.keypoint_detection import KeypointDetectionModel
 from roboflow.models.object_detection import ObjectDetectionModel
@@ -37,6 +37,11 @@
 from roboflow.util.general import write_line
 from roboflow.util.versions import get_wrong_dependencies_versions, print_warn_for_wrong_dependencies_versions
 
+if TYPE_CHECKING:
+    import numpy as np
+
+    from roboflow.models.inference import InferenceModel
+
 load_dotenv()
 
 
@@ -401,6 +406,8 @@ def live_plot(epochs, mAP, loss, title=""):
             loss: Union[np.ndarray, list]
 
             if "roboflow-train" in models.keys():
+                import numpy as np
+
                 # training has started
                 epochs = np.array([int(epoch["epoch"]) for epoch in models["roboflow-train"]["epochs"]])
                 mAP = np.array([float(epoch["mAP"]) for epoch in models["roboflow-train"]["epochs"]])

roboflow/core/workspace.py

@@ -5,9 +5,7 @@
 import sys
 from typing import Any, List
 
-import numpy as np
 import requests
-from numpy import ndarray
 from PIL import Image
 
 from roboflow.adapters import rfapi
@@ -407,6 +405,8 @@ def active_learning(
             use_localhost: (bool) = determines if local http format used or remote endpoint
             local_server: (str) = local http address for inference server, use_localhost must be True for this to be used
         """  # noqa: E501 // docs
+        import numpy as np
+
         prediction_results = []
 
         # ensure that all fields of conditionals have a key:value pair
@@ -528,7 +528,9 @@ def active_learning(
 
         # return predictions with filenames if globbed images from dir,
         # otherwise return latest prediction result
-        return prediction_results if type(raw_data_location) is not ndarray else prediction_results[-1]["predictions"]
+        return (
+            prediction_results if type(raw_data_location) is not np.ndarray else prediction_results[-1]["predictions"]
+        )
 
     def __str__(self):
         projects = self.projects()

roboflow/models/object_detection.py

@@ -6,8 +6,6 @@
 import random
 import urllib
 
-import cv2
-import numpy as np
 import requests
 from PIL import Image
 
@@ -178,6 +176,9 @@ def predict(  # type: ignore[override]
         original_dimensions = None
         # If image is local image
         if not hosted:
+            import cv2
+            import numpy as np
+
             if isinstance(image_path, str):
                 image = Image.open(image_path).convert("RGB")
                 dimensions = image.size
@@ -294,6 +295,7 @@ def webcam(
             stroke (int): Stroke width for bounding box
             labels (bool): Whether to show labels on bounding box
         """  # noqa: E501 // docs
+        import cv2
 
         os.environ["OPENCV_VIDEOIO_PRIORITY_MSMF"] = "0"
 

roboflow/util/image_utils.py

@@ -3,8 +3,6 @@
 import os
 import urllib
 
-import cv2
-import numpy as np
 import requests
 import yaml
 from PIL import Image
@@ -40,6 +38,9 @@ def mask_image(image, encoded_mask, transparency=60):
     :param transparency: alpha transparency of masks for semantic overlays
     :returns: CV2 image / numpy.ndarray matrix
     """
+    import cv2
+    import numpy as np
+
     np_data = np.fromstring(base64.b64decode(encoded_mask), np.uint8)  # type: ignore[no-overload]
     mask = cv2.imdecode(np_data, cv2.IMREAD_UNCHANGED)
 
@@ -71,6 +72,8 @@ def validate_image_path(image_path):
 
 
 def file2jpeg(image_path):
+    import cv2
+
     img = cv2.imread(image_path)
     image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     pilImage = Image.fromarray(image)

roboflow/util/prediction.py

@@ -4,12 +4,7 @@
 import urllib.request
 import warnings
 
-import cv2
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-import numpy as np
 import requests
-from matplotlib import patches
 from PIL import Image
 
 from roboflow.config import (
@@ -29,6 +24,8 @@ def plot_image(image_path):
     :param image_path: path of image to be plotted (can be hosted or local)
     :return:
     """
+    import matplotlib.pyplot as plt
+
     validate_image_path(image_path)
     try:
         img = Image.open(image_path)
@@ -52,6 +49,8 @@ def plot_annotation(axes, prediction=None, stroke=1, transparency=60, colors=Non
     :param transparency: alpha transparency of masks for semantic overlays
     :return:
     """
+    from matplotlib import patches
+
     # Object Detection annotation
 
     colors = {} if colors is None else colors
@@ -88,6 +87,8 @@ def plot_annotation(axes, prediction=None, stroke=1, transparency=60, colors=Non
         polygon = patches.Polygon(points, linewidth=stroke, edgecolor=stroke_color, facecolor="none")
         axes.add_patch(polygon)
     elif prediction["prediction_type"] == SEMANTIC_SEGMENTATION_MODEL:
+        import matplotlib.image as mpimg
+
         encoded_mask = prediction["segmentation_mask"]
         mask_bytes = io.BytesIO(base64.b64decode(encoded_mask))
         mask = mpimg.imread(mask_bytes, format="JPG")
@@ -121,6 +122,9 @@ def json(self):
         return self.json_prediction
 
     def __load_image(self):
+        import cv2
+        import numpy as np
+
         if "http://" in self.image_path:
             req = urllib.request.urlopen(self.image_path)
             arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
@@ -131,6 +135,8 @@ def __load_image(self):
         return cv2.imread(self.image_path)
 
     def plot(self, stroke=1):
+        import matplotlib.pyplot as plt
+
         # Exception to check if image path exists
         validate_image_path(self["image_path"])
         _, axes = plot_image(self["image_path"])
@@ -146,6 +152,9 @@ def save(self, output_path="predictions.jpg", stroke=2, transparency=60):
         :param stroke: line width to use when drawing rectangles and polygons
         :param transparency: alpha transparency of masks for semantic overlays
         """
+        import cv2
+        import numpy as np
+
         image = self.__load_image()
         stroke_color = (255, 0, 0)
 
@@ -302,6 +311,8 @@ def add_prediction(self, prediction=None):
         self.predictions.append(prediction)
 
     def plot(self, stroke=1):
+        import matplotlib.pyplot as plt
+
         if len(self) > 0:
             validate_image_path(self.base_image_path)
             _, axes = plot_image(self.base_image_path)
@@ -311,6 +322,9 @@ def plot(self, stroke=1):
         plt.show()
 
     def __load_image(self):
+        import cv2
+        import numpy as np
+
         # Check if it is a hosted image and open image as needed
         if "http://" in self.base_image_path or "https://" in self.base_image_path:
             req = urllib.request.urlopen(self.base_image_path)
@@ -322,6 +336,9 @@ def __load_image(self):
         return cv2.imread(self.base_image_path)
 
     def save(self, output_path="predictions.jpg", stroke=2):
+        import cv2
+        import numpy as np
+
         # Load image based on image path as an array
         image = self.__load_image()
         stroke_color = (255, 0, 0)

tests/models/test_object_detection.py

@@ -1,6 +1,5 @@
 import unittest
 
-import numpy as np
 import responses
 from PIL import UnidentifiedImageError
 from requests.exceptions import HTTPError
@@ -83,6 +82,8 @@ def test_predict_with_local_image_request(self):
 
     @responses.activate
     def test_predict_with_a_numpy_array_request(self):
+        import numpy as np
+
         np_array = np.ones((100, 100, 1), dtype=np.uint8)
         instance = ObjectDetectionModel(self.api_key, self.version_id, version=self.version)