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# Imports
from collections import namedtuple
from itertools import groupby
from pathlib import Path

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

# Settings
# Directories where data will be placed
model_folder = "model"
data_folder = "data"
charlist_folder = f"{data_folder}/charlists"

# Precision used by model
precision = "FP16"

Language = namedtuple(
    typename="Language", field_names=["model_name", "charlist_name", "demo_image_name"]
)
chinese_files = Language(
    model_name="handwritten-simplified-chinese-recognition-0001",
    charlist_name="chinese_charlist.txt",
    demo_image_name="handwritten_chinese_test.jpg",
)
japanese_files = Language(
    model_name="handwritten-japanese-recognition-0001",
    charlist_name="japanese_charlist.txt",
    demo_image_name="handwritten_japanese_test.png",
)

# Select Language
# Select language by using either language='chinese' or language='japanese'
language = "chinese"

languages = {"chinese": chinese_files, "japanese": japanese_files}

selected_language = languages.get(language)

# Download Model
path_to_model_weights = Path(f'{model_folder}/intel/{selected_language.model_name}/{precision}/{selected_language.model_name}.bin')
if not path_to_model_weights.is_file():
    download_command = f'omz_downloader --name {selected_language.model_name} --output_dir {model_folder} --precision {precision}'
    print(download_command)
    ! $download_command

# Load Network and Execute
ie = Core()
path_to_model = path_to_model_weights.with_suffix(".xml")
model = ie.read_model(model=path_to_model)

# Select Device Name
# To check available device names run the line below
# print(ie.available_devices)

compiled_model = ie.compile_model(model=model, device_name="CPU")

# Fetch Information About Input and Output Layers
recognition_output_layer = compiled_model.output(0)
recognition_input_layer = compiled_model.input(0)

# Load an Image
# Read file name of demo file based on the selected model

file_name = selected_language.demo_image_name

# Text detection models expects an image in grayscale format
# IMPORTANT!!! This model allows to read only one line at time

# Read image
image = cv2.imread(filename=f"{data_folder}/{file_name}", flags=cv2.IMREAD_GRAYSCALE)

# Fetch shape
image_height, _ = image.shape

# B,C,H,W = batch size, number of channels, height, width
_, _, H, W = recognition_input_layer.shape

# Calculate scale ratio between input shape height and image height to resize image
scale_ratio = H / image_height

# Resize image to expected input sizes
resized_image = cv2.resize(
    image, None, fx=scale_ratio, fy=scale_ratio, interpolation=cv2.INTER_AREA
)

# Pad image to match input size, without changing aspect ratio
resized_image = np.pad(
    resized_image, ((0, 0), (0, W - resized_image.shape[1])), mode="edge"
)

# Reshape to network the input shape
input_image = resized_image[None, None, :, :]

# Visualise Input Image
plt.figure(figsize=(20, 1))
plt.axis("off")
plt.imshow(resized_image, cmap="gray", vmin=0, vmax=255);

# Prepare Charlist
# Get dictionary to encode output, based on model documentation
used_charlist = selected_language.charlist_name

# With both models, there should be blank symbol added at index 0 of each charlist
blank_char = "~"

with open(f"{charlist_folder}/{used_charlist}", "r", encoding="utf-8") as charlist:
    letters = blank_char + "".join(line.strip() for line in charlist)

# Run Inference
# Run inference on the model
predictions = compiled_model([input_image])[recognition_output_layer]

# Process Output Data
# Remove batch dimension
predictions = np.squeeze(predictions)

# Run argmax to pick the symbols with the highest probability
predictions_indexes = np.argmax(predictions, axis=1)

# Use groupby to remove concurrent letters, as required by CTC greedy decoding
output_text_indexes = list(groupby(predictions_indexes))

# Remove grouper objects
output_text_indexes, _ = np.transpose(output_text_indexes, (1, 0))

# Remove blank symbols
output_text_indexes = output_text_indexes[output_text_indexes != 0]

# Assign letters to indexes from output array
output_text = [letters[letter_index] for letter_index in output_text_indexes]

# Print Output
plt.figure(figsize=(20, 1))
plt.axis("off")
plt.imshow(resized_image, cmap="gray", vmin=0, vmax=255)

print("".join(output_text))
star

Fri Jun 17 2022 05:10:39 GMT+0000 (UTC) https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/209-handwritten-ocr/209-handwritten-ocr.ipynb

#python #openvino #openvino-notebooks #deeplearning #accelerated-inference ##nlp #ocr #chinese #japanese #handwritten

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