004-hello-detection: Introduction to Detection in OpenVINO


Thu Jun 09 2022 16:51:17 GMT+0000 (UTC)

Saved by @OpenVINOtoolkit #python #openvino #openvino-notebook #detection #object-detection

# Imports
import cv2
import matplotlib.pyplot as plt
import numpy as np
from openvino.runtime import Core

# Load the Model
ie = Core()

model = ie.read_model(model="model/horizontal-text-detection-0001.xml")
compiled_model = ie.compile_model(model=model, device_name="CPU")

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

# Load an Image
# Text detection models expects image in BGR format
image = cv2.imread("data/intel_rnb.jpg")

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

# Resize image to meet network expected input sizes
resized_image = cv2.resize(image, (W, H))

# Reshape to network input shape
input_image = np.expand_dims(resized_image.transpose(2, 0, 1), 0)

plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB));

# Do Inference
# Create inference request
boxes = compiled_model([input_image])[output_layer_ir]

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

# Visualize Results
# For each detection, the description has the format: [x_min, y_min, x_max, y_max, conf]
# Image passed here is in BGR format with changed width and height. To display it in colors expected by matplotlib we 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 image shapes to calculate 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 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 confidence factor from last place in array
        conf = box[-1]
        if conf > threshold:
            # Convert float to int and multiply corner position of each box by x and y ratio
            # In case that bounding box is found at the top of the image, 
            # we position upper box bar little lower to make it visible on 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 box based on 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 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(
                    (x_min, y_min - 10),

    return rgb_image

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

A very basic introduction to using object detection models with OpenVINO. We use the horizontal-text-detection-0001 model from the Open Model Zoo. It detects horizontal text in images and returns a blob of data in the shape of [100, 5]. Each detected text box is stored in the format [x_min, y_min, x_max, y_max, conf], where (x_min, y_min) are the coordinates of the top left bounding box corner, (x_max, y_max) are the coordinates of the bottom right bounding box corner and conf is the confidence for the predicted class. If you have not yet installed OpenVINO™, please follow the Installation Guide to install all required dependencies. https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide Link to .bin and .xml files: https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/horizontal-text-detection-0001/model.yml