app.py

import cv2
import matplotlib.pyplot as plt
import numpy as np
from openvino.runtime import Core
import gradio as gr

##### 
#Load pretrained 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")

##### 
#Inference
##### 
def predict(img: np.ndarray, threshold) -> str:
    # input: numpy array of image in RGB (see defaults for https://www.gradio.app/docs/#image)

    # Text detection models expect an image in BGR format.
    image = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    # 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)
    
    
    # Create an inference request.
    boxes = compiled_model([input_image])[output_layer_ir]
    # Remove zero only boxes.
    boxes = boxes[~np.all(boxes == 0, axis=1)]
    print(f'detected {len(boxes)} things')
    result = convert_result_to_image(image, resized_image, boxes, threshold=threshold, conf_labels=False)
    
    #plt.figure(figsize=(10, 6))
    #plt.axis("off")
    #plt.imshow(result)
    #print(f'result is: {type(result)}')
    #print(result.shape)
    #print(result)
    
    result_fp = 'temp_result.jpg'
    cv2.imwrite(result_fp, cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
    return result_fp


# 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

##### 
#Gradio Setup
##### 

title = "Text Detection"
description = "Text Detection with OpenVino model"
examples = ['test.jpg']
interpretation='default'
enable_queue=True

gr.Interface(
    fn=predict,
    inputs=[
        gr.inputs.Image(),
        gr.Slider(minimum=0, maximum=1, value=.3)
    ],
    outputs=gr.outputs.Image(type='filepath'),
    title=title,
    description=description,
    #examples=examples,
    interpretation=interpretation,
    enable_queue=enable_queue
    ).launch()