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from __future__ import annotations
import logging
import os
from pathlib import Path
from typing import Tuple
import cv2
import numpy as np
import torch
from PIL import Image, ImageEnhance
from easyocrlite.model import CRAFT
from easyocrlite.utils.download_utils import prepare_model
from easyocrlite.utils.image_utils import (
adjust_result_coordinates,
boxed_transform,
normalize_mean_variance,
resize_aspect_ratio,
)
from easyocrlite.utils.detect_utils import (
extract_boxes,
extract_regions_from_boxes,
box_expand,
greedy_merge,
)
from easyocrlite.types import BoxTuple, RegionTuple
import easyocrlite.utils.utils as utils
logger = logging.getLogger(__name__)
MODULE_PATH = (
os.environ.get("EASYOCR_MODULE_PATH")
or os.environ.get("MODULE_PATH")
or os.path.expanduser("~/.EasyOCR/")
)
class ReaderLite(object):
def __init__(
self,
gpu=True,
model_storage_directory=None,
download_enabled=True,
verbose=True,
quantize=True,
cudnn_benchmark=False,
):
self.verbose = verbose
model_storage_directory = Path(
model_storage_directory
if model_storage_directory
else MODULE_PATH + "/model"
)
self.detector_path = prepare_model(
model_storage_directory, download_enabled, verbose
)
self.quantize = quantize
self.cudnn_benchmark = cudnn_benchmark
if gpu is False:
self.device = "cpu"
if verbose:
logger.warning(
"Using CPU. Note: This module is much faster with a GPU."
)
elif not torch.cuda.is_available():
self.device = "cpu"
if verbose:
logger.warning(
"CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU."
)
elif gpu is True:
self.device = "cuda"
else:
self.device = gpu
self.detector = CRAFT()
state_dict = torch.load(self.detector_path, map_location=self.device)
if list(state_dict.keys())[0].startswith("module"):
state_dict = {k[7:]: v for k, v in state_dict.items()}
self.detector.load_state_dict(state_dict)
if self.device == "cpu":
if self.quantize:
try:
torch.quantization.quantize_dynamic(
self.detector, dtype=torch.qint8, inplace=True
)
except:
pass
else:
self.detector = torch.nn.DataParallel(self.detector).to(self.device)
import torch.backends.cudnn as cudnn
cudnn.benchmark = self.cudnn_benchmark
self.detector.eval()
def process(
self,
image_path: str,
max_size: int = 960,
expand_ratio: float = 1.0,
sharp: float = 1.0,
contrast: float = 1.0,
text_confidence: float = 0.7,
text_threshold: float = 0.4,
link_threshold: float = 0.4,
slope_ths: float = 0.1,
ratio_ths: float = 0.5,
center_ths: float = 0.5,
dim_ths: float = 0.5,
space_ths: float = 1.0,
add_margin: float = 0.1,
min_size: float = 0.01,
) -> Tuple[BoxTuple, list[np.ndarray]]:
image = Image.open(image_path).convert('RGB')
tensor, inverse_ratio = self.preprocess(
image, max_size, expand_ratio, sharp, contrast
)
scores = self.forward_net(tensor)
boxes = self.detect(scores, text_confidence, text_threshold, link_threshold)
image = np.array(image)
region_list, box_list = self.postprocess(
image,
boxes,
inverse_ratio,
slope_ths,
ratio_ths,
center_ths,
dim_ths,
space_ths,
add_margin,
min_size,
)
# get cropped image
image_list = []
for region in region_list:
x_min, x_max, y_min, y_max = region
crop_img = image[y_min:y_max, x_min:x_max, :]
image_list.append(
(
((x_min, y_min), (x_max, y_min), (x_max, y_max), (x_min, y_max)),
crop_img,
)
)
for box in box_list:
transformed_img = boxed_transform(image, np.array(box, dtype="float32"))
image_list.append((box, transformed_img))
# sort by top left point
image_list = sorted(image_list, key=lambda x: (x[0][0][1], x[0][0][0]))
return image_list
def preprocess(
self,
image: Image.Image,
max_size: int,
expand_ratio: float = 1.0,
sharp: float = 1.0,
contrast: float = 1.0,
) -> torch.Tensor:
if sharp != 1:
enhancer = ImageEnhance.Sharpness(image)
image = enhancer.enhance(sharp)
if contrast != 1:
enhancer = ImageEnhance.Contrast(image)
image = enhancer.enhance(contrast)
image = np.array(image)
image, target_ratio = resize_aspect_ratio(
image, max_size, interpolation=cv2.INTER_LINEAR, expand_ratio=expand_ratio
)
inverse_ratio = 1 / target_ratio
x = np.transpose(normalize_mean_variance(image), (2, 0, 1))
x = torch.tensor(np.array([x]), device=self.device)
return x, inverse_ratio
@torch.no_grad()
def forward_net(self, tensor: torch.Tensor) -> torch.Tensor:
scores, feature = self.detector(tensor)
return scores[0]
def detect(
self,
scores: torch.Tensor,
text_confidence: float = 0.7,
text_threshold: float = 0.4,
link_threshold: float = 0.4,
) -> list[BoxTuple]:
# make score and link map
score_text = scores[:, :, 0].cpu().data.numpy()
score_link = scores[:, :, 1].cpu().data.numpy()
# extract box
boxes, _ = extract_boxes(
score_text, score_link, text_confidence, text_threshold, link_threshold
)
return boxes
def postprocess(
self,
image: np.ndarray,
boxes: list[BoxTuple],
inverse_ratio: float,
slope_ths: float = 0.1,
ratio_ths: float = 0.5,
center_ths: float = 0.5,
dim_ths: float = 0.5,
space_ths: float = 1.0,
add_margin: float = 0.1,
min_size: int = 0,
) -> Tuple[list[RegionTuple], list[BoxTuple]]:
# coordinate adjustment
boxes = adjust_result_coordinates(boxes, inverse_ratio)
max_y, max_x, _ = image.shape
# extract region and merge
region_list, box_list = extract_regions_from_boxes(boxes, slope_ths)
region_list = greedy_merge(
region_list,
ratio_ths=ratio_ths,
center_ths=center_ths,
dim_ths=dim_ths,
space_ths=space_ths,
verbose=0
)
# add margin
region_list = [
region.expand(add_margin, (max_x, max_y)).as_tuple()
for region in region_list
]
box_list = [box_expand(box, add_margin, (max_x, max_y)) for box in box_list]
# filter by size
if min_size:
if min_size < 1:
min_size = int(min(max_y, max_x) * min_size)
region_list = [
i for i in region_list if max(i[1] - i[0], i[3] - i[2]) > min_size
]
box_list = [
i
for i in box_list
if max(utils.diff([c[0] for c in i]), utils.diff([c[1] for c in i]))
> min_size
]
return region_list, box_list
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