Spaces:
Running
Running
File size: 5,524 Bytes
29f689c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
import logging
import os
import cv2
import numpy as np
import importlib.util
import sys
import subprocess
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):
imgs_lists = []
if img_file is None or not os.path.exists(img_file):
raise Exception("not found any img file in {}".format(img_file))
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 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("openrec")
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":
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 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
|