# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import cv2
import random
import numpy as np
import paddle
import importlib.util
import sys
import subprocess
def print_dict(d, logger, delimiter=0):
"""
Recursively visualize a dict and
indenting acrrording by the relationship of keys.
"""
for k, v in sorted(d.items()):
if isinstance(v, dict):
logger.info("{}{} : ".format(delimiter * " ", str(k)))
print_dict(v, logger, delimiter + 4)
elif isinstance(v, list) and len(v) >= 1 and isinstance(v[0], dict):
logger.info("{}{} : ".format(delimiter * " ", str(k)))
for value in v:
print_dict(value, logger, delimiter + 4)
else:
logger.info("{}{} : {}".format(delimiter * " ", k, v))
def get_check_global_params(mode):
check_params = [
"use_gpu",
"max_text_length",
"image_shape",
"image_shape",
"character_type",
"loss_type",
]
if mode == "train_eval":
check_params = check_params + [
"train_batch_size_per_card",
"test_batch_size_per_card",
]
elif mode == "test":
check_params = check_params + ["test_batch_size_per_card"]
return check_params
def _check_image_file(path):
img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff", "gif", "pdf"}
return any([path.lower().endswith(e) for e in img_end])
def get_image_file_list(img_file, infer_list=None):
imgs_lists = []
if infer_list and not os.path.exists(infer_list):
raise Exception("not found infer list {}".format(infer_list))
if infer_list:
with open(infer_list, "r") as f:
lines = f.readlines()
for line in lines:
image_path = line.strip().split("\t")[0]
image_path = os.path.join(img_file, image_path)
imgs_lists.append(image_path)
else:
if img_file is None or not os.path.exists(img_file):
raise Exception("not found any Crop_img file in {}".format(img_file))
img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff", "gif", "pdf"}
if os.path.isfile(img_file) and _check_image_file(img_file):
imgs_lists.append(img_file)
elif os.path.isdir(img_file):
for single_file in os.listdir(img_file):
file_path = os.path.join(img_file, single_file)
if os.path.isfile(file_path) and _check_image_file(file_path):
imgs_lists.append(file_path)
if len(imgs_lists) == 0:
raise Exception("not found any Crop_img file in {}".format(img_file))
imgs_lists = sorted(imgs_lists)
return imgs_lists
def binarize_img(img):
if len(img.shape) == 3 and img.shape[2] == 3:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # conversion to grayscale image
# use cv2 threshold binarization
_, gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
return img
def alpha_to_color(img, alpha_color=(255, 255, 255)):
if len(img.shape) == 3 and img.shape[2] == 4:
B, G, R, A = cv2.split(img)
alpha = A / 255
R = (alpha_color[0] * (1 - alpha) + R * alpha).astype(np.uint8)
G = (alpha_color[1] * (1 - alpha) + G * alpha).astype(np.uint8)
B = (alpha_color[2] * (1 - alpha) + B * alpha).astype(np.uint8)
img = cv2.merge((B, G, R))
return img
def check_and_read(img_path):
if os.path.basename(img_path)[-3:].lower() == "gif":
gif = cv2.VideoCapture(img_path)
ret, frame = gif.read()
if not ret:
logger = logging.getLogger("ppocr")
logger.info("Cannot read {}. This gif image maybe corrupted.")
return None, False
if len(frame.shape) == 2 or frame.shape[-1] == 1:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
imgvalue = frame[:, :, ::-1]
return imgvalue, True, False
elif os.path.basename(img_path)[-3:].lower() == "pdf":
from paddle.utils import try_import
fitz = try_import("fitz")
from PIL import Image
imgs = []
with fitz.open(img_path) as pdf:
for pg in range(0, pdf.page_count):
page = pdf[pg]
mat = fitz.Matrix(2, 2)
pm = page.get_pixmap(matrix=mat, alpha=False)
# if width or height > 2000 pixels, don't enlarge the image
if pm.width > 2000 or pm.height > 2000:
pm = page.get_pixmap(matrix=fitz.Matrix(1, 1), alpha=False)
img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
imgs.append(img)
return imgs, False, True
return None, False, False
def load_vqa_bio_label_maps(label_map_path):
with open(label_map_path, "r", encoding="utf-8") as fin:
lines = fin.readlines()
old_lines = [line.strip() for line in lines]
lines = ["O"]
for line in old_lines:
# "O" has already been in lines
if line.upper() in ["OTHER", "OTHERS", "IGNORE"]:
continue
lines.append(line)
labels = ["O"]
for line in lines[1:]:
labels.append("B-" + line)
labels.append("I-" + line)
label2id_map = {label.upper(): idx for idx, label in enumerate(labels)}
id2label_map = {idx: label.upper() for idx, label in enumerate(labels)}
return label2id_map, id2label_map
def set_seed(seed=1024):
random.seed(seed)
np.random.seed(seed)
paddle.seed(seed)
def check_install(module_name, install_name):
spec = importlib.util.find_spec(module_name)
if spec is None:
print(f"Warnning! The {module_name} module is NOT installed")
print(
f"Try install {module_name} module automatically. You can also try to install manually by pip install {install_name}."
)
python = sys.executable
try:
subprocess.check_call(
[python, "-m", "pip", "install", install_name],
stdout=subprocess.DEVNULL,
)
print(f"The {module_name} module is now installed")
except subprocess.CalledProcessError as exc:
raise Exception(f"Install {module_name} failed, please install manually")
else:
print(f"{module_name} has been installed.")
class AverageMeter:
def __init__(self):
self.reset()
def reset(self):
"""reset"""
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
"""update"""
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count