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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) -> 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(im_cv, 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)]
result = convert_result_to_image(image, resized_image, boxes, conf_labels=False)
plt.figure(figsize=(10, 6))
plt.axis("off")
plt.imshow(result)
# 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 = "Image Detection with OpenVino model"
examples = ['test.jpg']
interpretation='default'
enable_queue=True
gr.Interface(
fn=predict,
inputs=gr.inputs.Image(),
outputs=gr.outputs.Image(),
title=title,
description=description,
#examples=examples,
interpretation=interpretation,
enable_queue=enable_queue
).launch() |