From 220fd6a9ee9982a4265b6786127a086b75fc3101 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 3 Apr 2024 16:49:22 +0000 Subject: [PATCH 1/4] chore(deps): bump pillow from 10.2.0 to 10.3.0 Bumps [pillow](https://github.com/python-pillow/Pillow) from 10.2.0 to 10.3.0. - [Release notes](https://github.com/python-pillow/Pillow/releases) - [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst) - [Commits](https://github.com/python-pillow/Pillow/compare/10.2.0...10.3.0) --- updated-dependencies: - dependency-name: pillow dependency-type: direct:production ... Signed-off-by: dependabot[bot] --- poetry.lock | 148 ++++++++++++++++++++++++++-------------------------- 1 file changed, 75 insertions(+), 73 deletions(-) diff --git a/poetry.lock b/poetry.lock index 7b261078c..5afb985e3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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`PIL` --- dataquality/utils/cv.py | 5 +++-- dataquality/utils/cv_smart_features.py | 14 ++++++++------ dataquality/utils/semantic_segmentation/metrics.py | 9 +++++---- docs/cv/coco_hf_datasets.py | 2 +- docs/cv/cv-demo-hf.py | 2 +- docs/cv/cv-testing-benchmark.py | 2 +- tests/integrations/semantic/test_torch_semantic.py | 2 +- 7 files changed, 20 insertions(+), 16 deletions(-) diff --git a/dataquality/utils/cv.py b/dataquality/utils/cv.py index 034920520..ee8772036 100644 --- a/dataquality/utils/cv.py +++ b/dataquality/utils/cv.py @@ -3,7 +3,8 @@ from io import BytesIO from typing import Any, Optional -from PIL import Image +from PIL.Image import Image +from PIL.Image import open as Image_open from pydantic import UUID4 from dataquality import config @@ -18,7 +19,7 @@ def _bytes_to_img(b: bytes) -> Image: - return Image.open(BytesIO(b)) + return Image_open(BytesIO(b)) def _write_img_bytes_to_file( diff --git a/dataquality/utils/cv_smart_features.py b/dataquality/utils/cv_smart_features.py index dd8bee59b..547374038 100644 --- a/dataquality/utils/cv_smart_features.py +++ b/dataquality/utils/cv_smart_features.py @@ -5,7 +5,9 @@ import vaex from imagededup.methods import PHash from multiprocess import Pool, cpu_count -from PIL import Image, ImageFilter, ImageStat +from PIL import ImageFilter, ImageStat +from PIL.Image import Image +from PIL.Image import open as Image_open from vaex.dataframe import DataFrame from dataquality.exceptions import GalileoWarning @@ -19,7 +21,7 @@ METHODS: the methods for detecting smart feature all follow the same architecture with a first method to quantify the anomaly (as a real number) followed by a method to threshold it and return a boolean (qualitive). -For example to detect if a method is blurry we call +For example to detect if a method is blurry we call blurriness = _blurry_laplace(image_gray) is_blurry = _is_blurry_laplace(blurriness) These methods are kept separate to allow easy generalization to the use-case where we @@ -256,7 +258,7 @@ def _is_under_exposed( return q_max_over <= under_exposed_max_thresh -def _blurry_laplace(image_gray: Image.Image) -> float: +def _blurry_laplace(image_gray: Image) -> float: """ Bluriness detector method where we compute the Variance of the Laplacian. We use PIL to estimate the Laplacian via the 3x3 convolution filter @@ -284,7 +286,7 @@ def _is_blurry_laplace( return blurriness < blurry_thresh -def _image_content_entropy(image: Image.Image) -> float: +def _image_content_entropy(image: Image) -> float: """ Returns the entropy of the pixels on the image. A high entropy means a more complex image with lots of variation in the pixel values (histogram closer to being uniform) @@ -396,7 +398,7 @@ def _is_near_duplicate(in_frame: DataFrame) -> np.ndarray: return np_is_near_duplicate -def _open_and_resize(image_path: str) -> Tuple[Image.Image, Image.Image, np.ndarray]: +def _open_and_resize(image_path: str) -> Tuple[Image, Image, np.ndarray]: """ Open the image at the given path and return a triple with - the original image opened with PIL @@ -406,7 +408,7 @@ def _open_and_resize(image_path: str) -> Tuple[Image.Image, Image.Image, np.ndar If any of the sides of the image is larger than 2**11, resize the image so that the largest side is now 2**11. """ - image = Image.open(image_path) + image = Image_open(image_path) image_gray = image.convert( "L" ) # TODO: check if image already grey, faster to skip that ? diff --git a/dataquality/utils/semantic_segmentation/metrics.py b/dataquality/utils/semantic_segmentation/metrics.py index ef37ab5f2..556ccc7af 100644 --- a/dataquality/utils/semantic_segmentation/metrics.py +++ b/dataquality/utils/semantic_segmentation/metrics.py @@ -4,7 +4,8 @@ import numpy as np import torch -from PIL import Image, ImageColor +from PIL import ImageColor +from PIL.Image import Image, fromarray from dataquality import config from dataquality.clients.objectstore import ObjectStore @@ -36,7 +37,7 @@ def calculate_and_upload_dep( return dep_heatmaps -def colorize_dep_heatmap(image: Image.Image, dep_mean: int) -> Image.Image: +def colorize_dep_heatmap(image: Image, dep_mean: int) -> Image: """Recolors a grayscale image to a color image based on our dep mapping""" color_1 = ImageColor.getrgb("#9bc33f") # Red color_2 = ImageColor.getrgb("#ece113") # Yellow @@ -62,7 +63,7 @@ def colorize_dep_heatmap(image: Image.Image, dep_mean: int) -> Image.Image: colorized_image[~threshold_mask, 1] = (1 - ratio) * color_2[1] + ratio * color_3[1] colorized_image[~threshold_mask, 2] = (1 - ratio) * color_2[2] + ratio * color_3[2] - return Image.fromarray(colorized_image.astype(np.uint8)) + return fromarray(colorized_image.astype(np.uint8)) def calculate_dep_heatmaps( @@ -166,7 +167,7 @@ def dep_heatmap_to_img(dep_heatmap: np.ndarray) -> Image: # Scale the array values to the range [0, 255] dep_heatmap = (dep_heatmap * 255).astype(np.uint8) # Create a PIL Image object from the numpy array as grey-scale - img = Image.fromarray(dep_heatmap, mode="L") + img = fromarray(dep_heatmap, mode="L") if img.size[0] > MAX_DEP_HEATMAP_SIZE or img.size[1] > MAX_DEP_HEATMAP_SIZE: img = img.resize((MAX_DEP_HEATMAP_SIZE, MAX_DEP_HEATMAP_SIZE)) return img diff --git a/docs/cv/coco_hf_datasets.py b/docs/cv/coco_hf_datasets.py index 1db67df70..bbc1bb612 100644 --- a/docs/cv/coco_hf_datasets.py +++ b/docs/cv/coco_hf_datasets.py @@ -4,7 +4,7 @@ import numpy as np import torch from google.cloud import storage -from PIL import Image +from PIL.Image import Image from torchvision import transforms from tqdm import tqdm diff --git a/docs/cv/cv-demo-hf.py b/docs/cv/cv-demo-hf.py index 1f23cc48c..8538eb31a 100644 --- a/docs/cv/cv-demo-hf.py +++ b/docs/cv/cv-demo-hf.py @@ -2,7 +2,7 @@ from io import BytesIO from datasets import load_dataset -from PIL import Image +from PIL.Image import Image from torchvision.transforms import Compose, Normalize, RandomResizedCrop, ToTensor from transformers import ( AutoFeatureExtractor, diff --git a/docs/cv/cv-testing-benchmark.py b/docs/cv/cv-testing-benchmark.py index ec3ec3a92..371ab2ad0 100644 --- a/docs/cv/cv-testing-benchmark.py +++ b/docs/cv/cv-testing-benchmark.py @@ -2,7 +2,7 @@ from io import BytesIO from datasets import load_dataset -from PIL import Image +from PIL.Image import Image food = load_dataset("sasha/dog-food") diff --git a/tests/integrations/semantic/test_torch_semantic.py b/tests/integrations/semantic/test_torch_semantic.py index 32365f2d4..1a78caed0 100644 --- a/tests/integrations/semantic/test_torch_semantic.py +++ b/tests/integrations/semantic/test_torch_semantic.py @@ -5,7 +5,7 @@ import numpy as np import torch import torch.nn as nn -from PIL import Image +from PIL.Image import Image from torch.utils.data import DataLoader from torchvision import transforms From b07d8160d48dc75c3ef3faa7cdef9d3e0a1c334e Mon Sep 17 00:00:00 2001 From: Setu Shah Date: Wed, 3 Apr 2024 12:34:16 -0700 Subject: [PATCH 3/4] fix: Import in test --- .../semantic/test_torch_semantic.py | 30 ++++++------------- 1 file changed, 9 insertions(+), 21 deletions(-) diff --git a/tests/integrations/semantic/test_torch_semantic.py b/tests/integrations/semantic/test_torch_semantic.py index 1a78caed0..6e4a1f421 100644 --- a/tests/integrations/semantic/test_torch_semantic.py +++ b/tests/integrations/semantic/test_torch_semantic.py @@ -5,7 +5,7 @@ import numpy as np import torch import torch.nn as nn -from PIL.Image import Image +from PIL.Image import Image, open as Image_open from torch.utils.data import DataLoader from torchvision import transforms @@ -116,19 +116,13 @@ def __len__(self) -> int: def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, int, np.ndarray]]: # gets a single item from our dataset - image_path = os.path.join( - self.dataset_path, self.relative_img_path, self.images[idx] - ) - mask_path = os.path.join( - self.dataset_path, self.relative_mask_path, self.masks[idx] - ) - image = Image.open(image_path) - mask = Image.open(mask_path) + image_path = os.path.join(self.dataset_path, self.relative_img_path, self.images[idx]) + mask_path = os.path.join(self.dataset_path, self.relative_mask_path, self.masks[idx]) + image = Image_open(image_path) + mask = Image_open(mask_path) # resize image and mask to given size - unnormalized_image = image.copy().resize( - (self.size, self.size), resample=Image.NEAREST - ) + unnormalized_image = image.copy().resize((self.size, self.size), resample=Image.NEAREST) unnormalized_image = transforms.ToTensor()(unnormalized_image) unnormalized_image = expand_gray_channel()(unnormalized_image) unnormalized_image = np.array(unnormalized_image) @@ -165,9 +159,7 @@ def __call__(self, tensor: torch.Tensor) -> torch.Tensor: img_transforms = transforms.Compose( [ transforms.ToTensor(), - transforms.Resize( - (IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.BICUBIC - ), + transforms.Resize((IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.BICUBIC), expand_gray_channel(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ] @@ -175,9 +167,7 @@ def __call__(self, tensor: torch.Tensor) -> torch.Tensor: mask_transforms = transforms.Compose( [ transforms.PILToTensor(), - transforms.Resize( - (IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.NEAREST - ), + transforms.Resize((IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.NEAREST), ] ) @@ -206,9 +196,7 @@ def __call__(self, tensor: torch.Tensor) -> torch.Tensor: ) labels = ["background", "person"] train_dataloader = DataLoader(train_dataset, batch_size=6, shuffle=True, num_workers=1) -validation_dataloader = DataLoader( - validation_dataset, batch_size=6, shuffle=True, num_workers=1 -) +validation_dataloader = DataLoader(validation_dataset, batch_size=6, shuffle=True, num_workers=1) class DummyDeepLabV3(nn.Module): From a52831ed512435f8946df73f93dc7a086807f977 Mon Sep 17 00:00:00 2001 From: Setu Shah Date: Wed, 3 Apr 2024 16:32:30 -0700 Subject: [PATCH 4/4] fix: `Image.NEAREST` => `Resampling.NEAREST` --- docs/cv/coco_hf_datasets.py | 6 ++-- .../semantic/test_torch_semantic.py | 29 ++++++++++++++----- 2 files changed, 24 insertions(+), 11 deletions(-) diff --git a/docs/cv/coco_hf_datasets.py b/docs/cv/coco_hf_datasets.py index bbc1bb612..67b80eb35 100644 --- a/docs/cv/coco_hf_datasets.py +++ b/docs/cv/coco_hf_datasets.py @@ -4,7 +4,7 @@ import numpy as np import torch from google.cloud import storage -from PIL.Image import Image +from PIL.Image import Image, Resampling from torchvision import transforms from tqdm import tqdm @@ -57,7 +57,7 @@ def __init__( if mask_transform is None: mask_transform = transforms.Compose( [ - transforms.Resize((size, size), resample=Image.NEAREST), + transforms.Resize((size, size), resample=Resampling.NEAREST), transforms.ToTensor(), ] ) @@ -117,7 +117,7 @@ def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, int, np.ndarray # resize image and mask to given size unnormalized_image = image.copy().resize( - (self.size, self.size), resample=Image.NEAREST + (self.size, self.size), resample=Resampling.NEAREST ) unnormalized_image = transforms.ToTensor()(unnormalized_image) unnormalized_image = expand_gray_channel()(unnormalized_image) diff --git a/tests/integrations/semantic/test_torch_semantic.py b/tests/integrations/semantic/test_torch_semantic.py index 6e4a1f421..00df902db 100644 --- a/tests/integrations/semantic/test_torch_semantic.py +++ b/tests/integrations/semantic/test_torch_semantic.py @@ -5,7 +5,8 @@ import numpy as np import torch import torch.nn as nn -from PIL.Image import Image, open as Image_open +from PIL.Image import Resampling +from PIL.Image import open as Image_open from torch.utils.data import DataLoader from torchvision import transforms @@ -67,7 +68,7 @@ def __init__( if mask_transform is None: mask_transform = transforms.Compose( [ - transforms.Resize((size, size), resample=Image.NEAREST), + transforms.Resize((size, size), resample=Resampling.NEAREST), transforms.ToTensor(), ] ) @@ -116,13 +117,19 @@ def __len__(self) -> int: def __getitem__(self, idx: int) -> Dict[str, Union[torch.Tensor, int, np.ndarray]]: # gets a single item from our dataset - image_path = os.path.join(self.dataset_path, self.relative_img_path, self.images[idx]) - mask_path = os.path.join(self.dataset_path, self.relative_mask_path, self.masks[idx]) + image_path = os.path.join( + self.dataset_path, self.relative_img_path, self.images[idx] + ) + mask_path = os.path.join( + self.dataset_path, self.relative_mask_path, self.masks[idx] + ) image = Image_open(image_path) mask = Image_open(mask_path) # resize image and mask to given size - unnormalized_image = image.copy().resize((self.size, self.size), resample=Image.NEAREST) + unnormalized_image = image.copy().resize( + (self.size, self.size), resample=Resampling.NEAREST + ) unnormalized_image = transforms.ToTensor()(unnormalized_image) unnormalized_image = expand_gray_channel()(unnormalized_image) unnormalized_image = np.array(unnormalized_image) @@ -159,7 +166,9 @@ def __call__(self, tensor: torch.Tensor) -> torch.Tensor: img_transforms = transforms.Compose( [ transforms.ToTensor(), - transforms.Resize((IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.BICUBIC), + transforms.Resize( + (IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.BICUBIC + ), expand_gray_channel(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ] @@ -167,7 +176,9 @@ def __call__(self, tensor: torch.Tensor) -> torch.Tensor: mask_transforms = transforms.Compose( [ transforms.PILToTensor(), - transforms.Resize((IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.NEAREST), + transforms.Resize( + (IMG_SIZE, IMG_SIZE), interpolation=transforms.InterpolationMode.NEAREST + ), ] ) @@ -196,7 +207,9 @@ def __call__(self, tensor: torch.Tensor) -> torch.Tensor: ) labels = ["background", "person"] train_dataloader = DataLoader(train_dataset, batch_size=6, shuffle=True, num_workers=1) -validation_dataloader = DataLoader(validation_dataset, batch_size=6, shuffle=True, num_workers=1) +validation_dataloader = DataLoader( + validation_dataset, batch_size=6, shuffle=True, num_workers=1 +) class DummyDeepLabV3(nn.Module):