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* notebooks-update * notebooks-update * fix * Update 121-convert-to-openvino-with-output.rst * Update 121-convert-to-openvino-with-output.rst * fix * table of content fix * fix * fix * fix * fix * Update tutorials.md * fix * fix * Update 115-async-api-with-output.rst * Update 227-whisper-subtitles-generation-with-output.rst
221 lines
7.1 KiB
ReStructuredText
221 lines
7.1 KiB
ReStructuredText
Hello Object Detection
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======================
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A very basic introduction to using object detection models with
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OpenVINO™.
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The
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`horizontal-text-detection-0001 <https://docs.openvino.ai/2023.0/omz_models_model_horizontal_text_detection_0001.html>`__
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model from `Open Model Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__ is used. It
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detects horizontal text in images and returns a blob of data in the
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shape of ``[100, 5]``. Each detected text box is stored in the
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``[x_min, y_min, x_max, y_max, conf]`` format, where the
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``(x_min, y_min)`` are the coordinates of the top left bounding box
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corner, ``(x_max, y_max)`` are the coordinates of the bottom right
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bounding box corner and ``conf`` is the confidence for the predicted
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class.
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**Table of contents:**
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- `Imports <#imports>`__
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- `Download model weights <#download-model-weights>`__
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- `Select inference device <#select-inference-device>`__
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- `Load the Model <#load-the-model>`__
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- `Load an Image <#load-an-image>`__
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- `Do Inference <#do-inference>`__
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- `Visualize Results <#visualize-results>`__
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.. code:: ipython3
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# Install openvino package
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!pip install -q "openvino==2023.1.0.dev20230811"
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Imports
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########################################
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.. code:: ipython3
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import openvino as ov
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from pathlib import Path
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import sys
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sys.path.append("../utils")
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from notebook_utils import download_file
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Download model weights
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#######################################################
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.. code:: ipython3
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base_model_dir = Path("./model").expanduser()
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model_name = "horizontal-text-detection-0001"
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model_xml_name = f'{model_name}.xml'
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model_bin_name = f'{model_name}.bin'
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model_xml_path = base_model_dir / model_xml_name
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model_bin_path = base_model_dir / model_bin_name
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if not model_xml_path.exists():
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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"
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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"
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download_file(model_xml_url, model_xml_name, base_model_dir)
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download_file(model_bin_url, model_bin_name, base_model_dir)
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else:
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print(f'{model_name} already downloaded to {base_model_dir}')
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.. parsed-literal::
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model/horizontal-text-detection-0001.xml: 0%| | 0.00/680k [00:00<?, ?B/s]
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.. parsed-literal::
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model/horizontal-text-detection-0001.bin: 0%| | 0.00/7.39M [00:00<?, ?B/s]
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Select inference device
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###########################################################
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Select device from dropdown list for running inference using OpenVINO:
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.. code:: ipython3
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import ipywidgets as widgets
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core = ov.Core()
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device = widgets.Dropdown(
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options=core.available_devices + ["AUTO"],
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value='AUTO',
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description='Device:',
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disabled=False,
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)
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device
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.. parsed-literal::
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Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
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Load the Model
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###############################################
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.. code:: ipython3
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core = ov.Core()
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model = core.read_model(model=model_xml_path)
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compiled_model = core.compile_model(model=model, device_name="CPU")
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input_layer_ir = compiled_model.input(0)
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output_layer_ir = compiled_model.output("boxes")
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Load an Image
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##############################################
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.. code:: ipython3
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# Text detection models expect an image in BGR format.
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image = cv2.imread("../data/image/intel_rnb.jpg")
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# N,C,H,W = batch size, number of channels, height, width.
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N, C, H, W = input_layer_ir.shape
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# Resize the image to meet network expected input sizes.
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resized_image = cv2.resize(image, (W, H))
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# Reshape to the network input shape.
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input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0)
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plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB));
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.. image:: 004-hello-detection-with-output_files/004-hello-detection-with-output_11_0.png
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Do Inference
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##############################################
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.. code:: ipython3
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# Create an inference request.
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boxes = compiled_model([input_image])[output_layer_ir]
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# Remove zero only boxes.
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boxes = boxes[~np.all(boxes == 0, axis=1)]
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Visualize Results
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##################################################
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.. code:: ipython3
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# For each detection, the description is in the [x_min, y_min, x_max, y_max, conf] format:
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# The image passed here is in BGR format with changed width and height. To display it in colors expected by matplotlib, use cvtColor function
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def convert_result_to_image(bgr_image, resized_image, boxes, threshold=0.3, conf_labels=True):
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# Define colors for boxes and descriptions.
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colors = {"red": (255, 0, 0), "green": (0, 255, 0)}
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# Fetch the image shapes to calculate a ratio.
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(real_y, real_x), (resized_y, resized_x) = bgr_image.shape[:2], resized_image.shape[:2]
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ratio_x, ratio_y = real_x / resized_x, real_y / resized_y
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# Convert the base image from BGR to RGB format.
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rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
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# Iterate through non-zero boxes.
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for box in boxes:
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# Pick a confidence factor from the last place in an array.
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conf = box[-1]
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if conf > threshold:
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# Convert float to int and multiply corner position of each box by x and y ratio.
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# If the bounding box is found at the top of the image,
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# position the upper box bar little lower to make it visible on the image.
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(x_min, y_min, x_max, y_max) = [
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int(max(corner_position * ratio_y, 10)) if idx % 2
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else int(corner_position * ratio_x)
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for idx, corner_position in enumerate(box[:-1])
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]
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# Draw a box based on the position, parameters in rectangle function are: image, start_point, end_point, color, thickness.
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rgb_image = cv2.rectangle(rgb_image, (x_min, y_min), (x_max, y_max), colors["green"], 3)
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# Add text to the image based on position and confidence.
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# Parameters in text function are: image, text, bottom-left_corner_textfield, font, font_scale, color, thickness, line_type.
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if conf_labels:
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rgb_image = cv2.putText(
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rgb_image,
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f"{conf:.2f}",
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(x_min, y_min - 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.8,
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colors["red"],
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1,
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cv2.LINE_AA,
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)
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return rgb_image
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.. code:: ipython3
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plt.figure(figsize=(10, 6))
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plt.axis("off")
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plt.imshow(convert_result_to_image(image, resized_image, boxes, conf_labels=False));
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.. image:: 004-hello-detection-with-output_files/004-hello-detection-with-output_16_0.png
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