Hello オブジェクト検出

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。

OpenVINO™ でオブジェクト検出モデルを使用する非常に基本的な入門編です。

Open Model Zoo の horizontal-text-detection-0001 モデルが使用されます。画像内の横書きテキストを検出し、[100, 5] の形状形のデータのブロブを返します。検出された各テキストボックスは [x_min, y_min, x_max, y_max, conf] 形式で保存されます。ここで、(x_min, y_min) は境界ボックスの左上隅の座標、(x_max, y_max) は境界ボックスの右下隅の座標です。右境界ボックスの隅と conf は、予測されたクラスの信頼度です。

目次

• インポート

• モデルの重みをダウンロード

• 推論デバイスの選択

• モデルのロード

• 画像のロード

• 推論の実行

• 結果を可視化

# Install openvino package
%pip install -q "openvino>=2023.1.0"

Note: you may need to restart the kernel to use updated packages.

インポート

import cv2
import matplotlib.pyplot as plt
import numpy as np
import openvino as ov
from pathlib import Path

# 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 download_file

モデルの重みをダウンロード

base_model_dir = Path("./model").expanduser()

model_name = "horizontal-text-detection-0001"
model_xml_name = f'{model_name}.xml'
model_bin_name = f'{model_name}.bin'

model_xml_path = base_model_dir / model_xml_name
model_bin_path = base_model_dir / model_bin_name

if not model_xml_path.exists():
    model_xml_url = "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/horizontal-text-detection-0001/FP32/horizontal-text-detection-0001.xml"
    model_bin_url = "https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.3/models_bin/1/horizontal-text-detection-0001/FP32/horizontal-text-detection-0001.bin"

    download_file(model_xml_url, model_xml_name, base_model_dir)
    download_file(model_bin_url, model_bin_name, base_model_dir)
else:
    print(f'{model_name} already downloaded to {base_model_dir}')

model/horizontal-text-detection-0001.xml:   0%|          | 0.00/680k [00:00<?, ?B/s]

model/horizontal-text-detection-0001.bin:   0%|          | 0.00/7.39M [00:00<?, ?B/s]

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

core = ov.Core()
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')

モデルのロード

core = ov.Core()

model = core.read_model(model=model_xml_path)
compiled_model = core.compile_model(model=model, device_name="CPU")

input_layer_ir = compiled_model.input(0)
output_layer_ir = compiled_model.output("boxes")

画像のロード

# Download the image from the openvino_notebooks storage
image_filename = download_file(
    "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/intel_rnb.jpg",
    directory="data"
)

# Text detection models expect an image in BGR format.
image = cv2.imread(str(image_filename))

# N,C,H,W = batch size, number of channels, height, width.
N, C, H, W = input_layer_ir.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));

data/intel_rnb.jpg:   0%|          | 0.00/288k [00:00<?, ?B/s]

推論の実行

# Create an inference request.
boxes = compiled_model([input_image])[output_layer_ir]

# Remove zero only boxes.
boxes = boxes[~np.all(boxes == 0, axis=1)]

結果を可視化

# For each detection, the description is in the [x_min, y_min, x_max, y_max, conf] format:
# The image passed here is in BGR format with changed width and height. To display it in colors expected by matplotlib, use cvtColor function
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)}

    # Fetch the image shapes to calculate a ratio.
    (real_y, real_x), (resized_y, resized_x) = bgr_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 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 corner position of each box by x and y ratio.
            # If the bounding box is found at the top of the image,
            # position the upper box bar little lower to make it visible on the image.
            (x_min, y_min, x_max, y_max) = [
                int(max(corner_position * ratio_y, 10)) if idx % 2
                else int(corner_position * ratio_x)
                for idx, corner_position in enumerate(box[:-1])
            ]

            # Draw a box based on the position, parameters in rectangle function are: image, start_point, end_point, color, thickness.
            rgb_image = cv2.rectangle(rgb_image, (x_min, y_min), (x_max, y_max), colors["green"], 3)

            # Add text to the image based on position and confidence.
            # Parameters in text function are: image, text, bottom-left_corner_textfield, font, font_scale, color, thickness, line_type.
            if conf_labels:
                rgb_image = cv2.putText(
                    rgb_image,
                    f"{conf:.2f}",
                    (x_min, y_min - 10),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    0.8,
                    colors["red"],
                    1,
                    cv2.LINE_AA,
                )

    return rgb_image

plt.figure(figsize=(10, 6))
plt.axis("off")
plt.imshow(convert_result_to_image(image, resized_image, boxes, conf_labels=False));