OpenVINO™ による光学式文字認識 (OCR)¶
この Jupyter ノートブックはオンラインで起動でき、ブラウザーのウィンドウで対話型環境を開きます。ローカルにインストールすることもできます。次のオプションのいずれかを選択します。
このチュートリアルでは、OpenVINO モデルを使用して光学式文字認識 (OCR) を実行する方法を説明します。これは、テキスト検出のみを示す 004-hello-detection チュートリアルの続きです。
horizontal-text-detection-0001 と text-recognition-resnet モデルは、テキスト検出とテキスト認識に使用されます。
このチュートリアルでは、モデル・ダウンローダー、モデル・コンバーター、情報ダンパーなどの Open Model Zoo ツールを使用して、Open Model Zoo からモデルをダウンロードして変換します。詳細については、104-model-tools チュートリアルを参照してください。
目次¶
# Install openvino-dev package
%pip install -q "openvino-dev>=2023.1.0" onnx
Note: you may need to restart the kernel to use updated packages.
インポート¶
from pathlib import Path
import cv2
import matplotlib.pyplot as plt
import numpy as np
import openvino as ov
from IPython.display import Markdown, display
from PIL import Image
# Fetch `notebook_utils` module
import urllib.request
urllib.request.urlretrieve(
url='https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/main/notebooks/utils/notebook_utils.py',
filename='notebook_utils.py'
)
from notebook_utils import load_image
設定¶
core = ov.Core()
model_dir = Path("model")
precision = "FP16"
detection_model = "horizontal-text-detection-0001"
recognition_model = "text-recognition-resnet-fc"
model_dir.mkdir(exist_ok=True)
モデルのダウンロード¶
次のセルではモデル・ダウンローダーを実行して、検出モデルと認識モデルをダウンロードします。モデルが以前にダウンロードされている場合、再度ダウンロードすることはできません。
download_command = f"omz_downloader --name {detection_model},{recognition_model} --output_dir {model_dir} --cache_dir {model_dir} --precision {precision} --num_attempts 5"
display(Markdown(f"Download command: `{download_command}`"))
display(Markdown(f"Downloading {detection_model}, {recognition_model}…"))
!$download_command
display(Markdown(f"Finished downloading {detection_model}, {recognition_model}."))
detection_model_path = (model_dir / "intel/horizontal-text-detection-0001" / precision / detection_model).with_suffix(".xml")
recognition_model_path = (model_dir / "public/text-recognition-resnet-fc" / precision / recognition_model).with_suffix(".xml")
ダウンロードコマンド:
omz_downloader --name horizontal-text-detection-0001,text-recognition-resnet-fc --output_dir model --cache_dir model --precision FP16 --num_attempts 5
Downloading horizontal-text-detection-0001, text-recognition-resnet-fc…
################|| Downloading horizontal-text-detection-0001 ||################
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################|| Downloading text-recognition-resnet-fc ||################
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========== Unpacking model/public/text-recognition-resnet-fc/vedastr/addict-2.4.0-py3-none-any.whl
Finished downloading horizontal-text-detection-0001, text-recognition-resnet-fc.
### The text-recognition-resnet-fc model consists of many files. All filenames are printed in
### the output of Model Downloader. Uncomment the next two lines to show this output.
# for line in download_result:
# print(line)
モデルの変換¶
ダウンロードされた検出モデルはインテルのモデルであり、すでに OpenVINO 中間表現 (OpenVINO IR) 形式になっています。テキスト認識モデルはパブリックモデルであり、OpenVINO IR に変換する必要があります。このモデルは Open Model Zoo からダウンロードされたものであるため、モデル・コンバーターを使用してモデルを OpenVINO IR 形式に変換します。
モデル・コンバーターの出力が表示されます。変換が成功すると、出力の最後の行には次の内容が含まれます。
[ SUCCESS ] Generated IR version 11 model.
convert_command = f"omz_converter --name {recognition_model} --precisions {precision} --download_dir {model_dir} --output_dir {model_dir}"
display(Markdown(f"Convert command: `{convert_command}`"))
display(Markdown(f"Converting {recognition_model}…"))
! $convert_command
変換コマンド:
omz_converter --name text-recognition-resnet-fc --precisions FP16 --download_dir model --output_dir model
Converting text-recognition-resnet-fc…
========== Converting text-recognition-resnet-fc to ONNX
Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/model_zoo/internal_scripts/pytorch_to_onnx.py --model-path=/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/model_zoo/models/public/text-recognition-resnet-fc --model-path=model/public/text-recognition-resnet-fc --model-name=get_model --import-module=model '--model-param=file_config=r"model/public/text-recognition-resnet-fc/vedastr/configs/resnet_fc.py"' '--model-param=weights=r"model/public/text-recognition-resnet-fc/vedastr/ckpt/resnet_fc.pth"' --input-shape=1,1,32,100 --input-names=input --output-names=output --output-file=model/public/text-recognition-resnet-fc/resnet_fc.onnx
ONNX check passed successfully.
========== Converting text-recognition-resnet-fc to IR (FP16)
Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/text-recognition-resnet-fc/FP16 --model_name=text-recognition-resnet-fc --input=input '--mean_values=input[127.5]' '--scale_values=input[127.5]' --output=output --input_model=model/public/text-recognition-resnet-fc/resnet_fc.onnx '--layout=input(NCHW)' '--input_shape=[1, 1, 32, 100]' --compress_to_fp16=True
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/208-optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/208-optical-character-recognition/model/public/text-recognition-resnet-fc/FP16/text-recognition-resnet-fc.bin
推論デバイスの選択¶
OpenVINO を使用して推論を実行するためにドロップダウン・リストからデバイスを選択します。
import ipywidgets as widgets
device = widgets.Dropdown(
options=core.available_devices + ["AUTO"],
value='AUTO',
description='Device:',
disabled=False,
)
device
Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
物体検出¶
検出モデルをロードし、画像をロードし、推論を実行して、検出推論結果を取得します。
検出モデルをロード¶
detection_model = core.read_model(
model=detection_model_path, weights=detection_model_path.with_suffix(".bin")
)
detection_compiled_model = core.compile_model(model=detection_model, device_name=device.value)
detection_input_layer = detection_compiled_model.input(0)
画像のロード¶
# The `image_file` variable can point to a URL or a local image.
image_file = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg"
image = load_image(image_file)
# N,C,H,W = batch size, number of channels, height, width.
N, C, H, W = detection_input_layer.shape
# Resize the image to meet network expected input sizes.
resized_image = cv2.resize(image, (W, H))
# Reshape to the network input shape.
input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0)
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB));
推論の実行¶
画像内でテキストボックスが検出され、[100, 5] 形状のデータのブロブとして返されます。検出の各説明は [x_min, y_min, x_max, y_max, conf] の形式になります。
output_key = detection_compiled_model.output("boxes")
boxes = detection_compiled_model([input_image])[output_key]
# Remove zero only boxes.
boxes = boxes[~np.all(boxes == 0, axis=1)]
検出結果を取得¶
def multiply_by_ratio(ratio_x, ratio_y, box):
return [
max(shape * ratio_y, 10) if idx % 2 else shape * ratio_x
for idx, shape in enumerate(box[:-1])
]
def run_preprocesing_on_crop(crop, net_shape):
temp_img = cv2.resize(crop, net_shape)
temp_img = temp_img.reshape((1,) * 2 + temp_img.shape)
return temp_img
def convert_result_to_image(bgr_image, resized_image, boxes, threshold=0.3, conf_labels=True):
# Define colors for boxes and descriptions.
colors = {"red": (255, 0, 0), "green": (0, 255, 0), "white": (255, 255, 255)}
# Fetch image shapes to calculate a ratio.
(real_y, real_x), (resized_y, resized_x) = image.shape[:2], resized_image.shape[:2]
ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
# Convert the base image from BGR to RGB format.
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
# Iterate through non-zero boxes.
for box, annotation in boxes:
# Pick a confidence factor from the last place in an array.
conf = box[-1]
if conf > threshold:
# Convert float to int and multiply position of each box by x and y ratio.
(x_min, y_min, x_max, y_max) = map(int, multiply_by_ratio(ratio_x, ratio_y, box))
# Draw a box based on the position. Parameters in the `rectangle` function are: image, start_point, end_point, color, thickness.
cv2.rectangle(rgb_image, (x_min, y_min), (x_max, y_max), colors["green"], 3)
# Add a text to an image based on the position and confidence. Parameters in the `putText` function are: image, text, bottomleft_corner_textfield, font, font_scale, color, thickness, line_type
if conf_labels:
# Create a background box based on annotation length.
(text_w, text_h), _ = cv2.getTextSize(
f"{annotation}", cv2.FONT_HERSHEY_TRIPLEX, 0.8, 1
)
image_copy = rgb_image.copy()
cv2.rectangle(
image_copy,
(x_min, y_min - text_h - 10),
(x_min + text_w, y_min - 10),
colors["white"],
-1,
)
# Add weighted image copy with white boxes under a text.
cv2.addWeighted(image_copy, 0.4, rgb_image, 0.6, 0, rgb_image)
cv2.putText(
rgb_image,
f"{annotation}",
(x_min, y_min - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.8,
colors["red"],
1,
cv2.LINE_AA,
)
return rgb_image
テキスト認識¶
テキスト認識モデルをロードし、検出モデルから検出されたボックスに対して推論を実行します。
テキスト認識モデルをロード¶
recognition_model = core.read_model(
model=recognition_model_path, weights=recognition_model_path.with_suffix(".bin")
)
recognition_compiled_model = core.compile_model(model=recognition_model, device_name=device.value)
recognition_output_layer = recognition_compiled_model.output(0)
recognition_input_layer = recognition_compiled_model.input(0)
# Get the height and width of the input layer.
_, _, H, W = recognition_input_layer.shape
推論の実行¶
# Calculate scale for image resizing.
(real_y, real_x), (resized_y, resized_x) = image.shape[:2], resized_image.shape[:2]
ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
# Convert the image to grayscale for the text recognition model.
grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Get a dictionary to encode output, based on the model documentation.
letters = "~0123456789abcdefghijklmnopqrstuvwxyz"
# Prepare an empty list for annotations.
annotations = list()
cropped_images = list()
# fig, ax = plt.subplots(len(boxes), 1, figsize=(5,15), sharex=True, sharey=True)
# Get annotations for each crop, based on boxes given by the detection model.
for i, crop in enumerate(boxes):
# Get coordinates on corners of a crop.
(x_min, y_min, x_max, y_max) = map(int, multiply_by_ratio(ratio_x, ratio_y, crop))
image_crop = run_preprocesing_on_crop(grayscale_image[y_min:y_max, x_min:x_max], (W, H))
# Run inference with the recognition model.
result = recognition_compiled_model([image_crop])[recognition_output_layer]
# Squeeze the output to remove unnecessary dimension.
recognition_results_test = np.squeeze(result)
# Read an annotation based on probabilities from the output layer.
annotation = list()
for letter in recognition_results_test:
parsed_letter = letters[letter.argmax()]
# Returning 0 index from `argmax` signalizes an end of a string.
if parsed_letter == letters[0]:
break
annotation.append(parsed_letter)
annotations.append("".join(annotation))
cropped_image = Image.fromarray(image[y_min:y_max, x_min:x_max])
cropped_images.append(cropped_image)
boxes_with_annotations = list(zip(boxes, annotations))
結果の表示¶
画像の検出されたテキストボックスと OCR 結果を表示¶
認識されたテキストの周囲にボックスを描画し、テキスト認識モデルからの OCR 結果を表示することで、結果を視覚化します。
plt.figure(figsize=(12, 12))
plt.imshow(convert_result_to_image(image, resized_image, boxes_with_annotations, conf_labels=True));
境界ボックスごとに OCR 結果を表示¶
画像によっては、セル上にボックスが表示されている画像では OCR 結果が読み取れない場合があります。以下のコードを使用して、抽出されたボックスとボックスごとの OCR 結果を表示します。
for cropped_image, annotation in zip(cropped_images, annotations):
display(cropped_image, Markdown("".join(annotation)))
building
noyce
2200
n
center
robert
アノテーションをプレーンテキスト形式で印刷する¶
入力画像内の位置に基づいて、検出されたテキストのアノテーションを左上隅から印刷します。
[
annotation
for _, annotation in sorted(zip(boxes, annotations), key=lambda x: x[0][0] ** 2 + x[0][1] ** 2)
]
['robert', 'n', 'noyce', 'building', '2200', 'center']