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Test_Image.py
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Test_Image.py
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# import library
import argparse,sys
from pickle import FALSE, TRUE
import uuid
import os
from os import listdir, mkdir, chdir, rename
from os.path import isfile, join, isdir, exists, basename
import numpy as np
from scipy.ndimage import zoom
import cv2
import tempfile
import shutil
import glob
import re
import csv
import math
from math import sqrt
from scipy.spatial import ConvexHull
from scipy.spatial import distance as dist
from PIL import Image
from difflib import SequenceMatcher
import pandas as pd
import json
#Import pytesseract
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
#Import Craft library
# -*- coding: utf-8 -*-
import sys
import os
import time
import argparse
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from skimage import io
import craft_utils
import imgproc
import file_utils
import json
import zipfile
from craft import CRAFT
from collections import OrderedDict
# https://stackoverflow.com/questions/37119071/scipy-rotate-and-zoom-an-image-without-changing-its-dimensions/37121993#37121993
def clipped_zoom(img, zoom_factor, **kwargs):
h, w = img.shape[:2]
# For multichannel images we don't want to apply the zoom factor to the RGB
# dimension, so instead we create a tuple of zoom factors, one per array
# dimension, with 1's for any trailing dimensions after the width and height.
zoom_tuple = (zoom_factor,) * 2 + (1,) * (img.ndim - 2)
# Zooming out
if zoom_factor < 1:
# Bounding box of the zoomed-out image within the output array
zh = int(np.round(h * zoom_factor))
zw = int(np.round(w * zoom_factor))
top = (h - zh) // 2
left = (w - zw) // 2
# Zero-padding
#out = np.zeros_like(img)
# White-padding
out=np.full_like(img,255)
#out[top:top+zh, left:left+zw] = zoom(img, zoom_tuple, **kwargs)
out[top:top+zh, left:left+zw]=cv2.resize(img, None, fx=zoom_factor, fy=zoom_factor)
# Zooming in
elif zoom_factor > 1:
# Bounding box of the zoomed-in region within the input array
zh = int(np.round(h / zoom_factor))
zw = int(np.round(w / zoom_factor))
top = (h - zh) // 2
left = (w - zw) // 2
out = zoom(img[top:top+zh, left:left+zw], zoom_tuple, **kwargs)
# `out` might still be slightly larger than `img` due to rounding, so
# trim off any extra pixels at the edges
trim_top = ((out.shape[0] - h) // 2)
trim_left = ((out.shape[1] - w) // 2)
out = out[trim_top:trim_top+h, trim_left:trim_left+w]
# If zoom_factor == 1, just return the input array
else:
out = img
return out
# https://stackoverflow.com/questions/39308030/how-do-i-increase-the-contrast-of-an-image-in-python-opencv
def apply_brightness_contrast(input_img, brightness = 0, contrast = 0):
if brightness != 0:
if brightness > 0:
shadow = brightness
highlight = 255
else:
shadow = 0
highlight = 255 + brightness
alpha_b = (highlight - shadow)/255
gamma_b = shadow
buf = cv2.addWeighted(input_img, alpha_b, input_img, 0, gamma_b)
else:
buf = input_img.copy()
if contrast != 0:
f = 131*(contrast + 127)/(127*(131-contrast))
alpha_c = f
gamma_c = 127*(1-f)
buf = cv2.addWeighted(buf, alpha_c, buf, 0, gamma_c)
return buf
def dilate_char(img, kernelsize=3, shape=cv2.MORPH_ELLIPSE):
kernel = cv2.getStructuringElement(shape,(kernelsize,kernelsize))
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
dilation = cv2.dilate(opening,kernel,iterations = 2)
return (255-dilation)
def preprocess_CRAFT(img):
final=dilate_char(255-img,4)
return final
# https://www.pyimagesearch.com/2016/03/21/ordering-coordinates-clockwise-with-python-and-opencv/
def order_points(pts):
# sort the points based on their x-coordinates
xSorted = pts[np.argsort(pts[:, 0]), :]
# grab the left-most and right-most points from the sorted
# x-roodinate points
leftMost = xSorted[:2, :]
rightMost = xSorted[2:, :]
# now, sort the left-most coordinates according to their
# y-coordinates so we can grab the top-left and bottom-left
# points, respectively
leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
(tl, bl) = leftMost
# now that we have the top-left coordinate, use it as an
# anchor to calculate the Euclidean distance between the
# top-left and right-most points; by the Pythagorean
# theorem, the point with the largest distance will be
# our bottom-right point
D = dist.cdist(tl[np.newaxis], rightMost, "euclidean")[0]
(br, tr) = rightMost[np.argsort(D)[::-1], :]
# return the coordinates in top-left, top-right,
# bottom-right, and bottom-left order
return np.array([tl, tr, br, bl], dtype="int32")
# https://gis.stackexchange.com/questions/22895/finding-minimum-area-rectangle-for-given-points
def minimum_bounding_rectangle(points):
"""
Find the smallest bounding rectangle for a set of points.
Returns a set of points representing the corners of the bounding box.
:param points: an nx2 matrix of coordinates
:rval: an nx2 matrix of coordinates
"""
from scipy.ndimage.interpolation import rotate
pi2 = np.pi/2.
# get the convex hull for the points
hull_points = points[ConvexHull(points).vertices]
# calculate edge angles
edges = np.zeros((len(hull_points)-1, 2))
edges = hull_points[1:] - hull_points[:-1]
angles = np.zeros((len(edges)))
angles = np.arctan2(edges[:, 1], edges[:, 0])
angles = np.abs(np.mod(angles, pi2))
angles = np.unique(angles)
# find rotation matrices
# XXX both work
rotations = np.vstack([
np.cos(angles),
np.cos(angles-pi2),
np.cos(angles+pi2),
np.cos(angles)]).T
# rotations = np.vstack([
# np.cos(angles),
# -np.sin(angles),
# np.sin(angles),
# np.cos(angles)]).T
rotations = rotations.reshape((-1, 2, 2))
# apply rotations to the hull
rot_points = np.dot(rotations, hull_points.T)
# find the bounding points
min_x = np.nanmin(rot_points[:, 0], axis=1)
max_x = np.nanmax(rot_points[:, 0], axis=1)
min_y = np.nanmin(rot_points[:, 1], axis=1)
max_y = np.nanmax(rot_points[:, 1], axis=1)
# find the box with the best area
areas = (max_x - min_x) * (max_y - min_y)
best_idx = np.argmin(areas)
# return the best box
x1 = max_x[best_idx]
x2 = min_x[best_idx]
y1 = max_y[best_idx]
y2 = min_y[best_idx]
r = rotations[best_idx]
rval = np.zeros((4, 2))
rval[0] = np.dot([x1, y2], r)
rval[1] = np.dot([x2, y2], r)
rval[2] = np.dot([x2, y1], r)
rval[3] = np.dot([x1, y1], r)
return rval
# Rotation image: https://stackoverflow.com/questions/9041681/opencv-python-rotate-image-by-x-degrees-around-specific-point
def rotate_image(image, angle, center):
image_center = tuple(np.array(center).astype(float))
#print(image_center, angle, 1.0)
rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], borderValue=(255,255,255))
return result
#_________________________________________
#____________________CRAFT_FUNCTION_______
#_________________________________________
def copyStateDict(state_dict):
if list(state_dict.keys())[0].startswith("module"):
start_idx = 1
else:
start_idx = 0
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = ".".join(k.split(".")[start_idx:])
new_state_dict[name] = v
return new_state_dict
def str2bool(v):
return v.lower() in ("yes", "y", "true", "t", "1")
##___CRAFT___TEST_NET___________________
def test_net(net, image, text_threshold, link_threshold, low_text, cuda, poly, refine_net=None):
t0 = time.time()
# resize
img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(image, args.canvas_size, interpolation=cv2.INTER_LINEAR, mag_ratio=args.mag_ratio)
ratio_h = ratio_w = 1 / target_ratio
# preprocessing
x = imgproc.normalizeMeanVariance(img_resized)
x = torch.from_numpy(x).permute(2, 0, 1) # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0)) # [c, h, w] to [b, c, h, w]
if cuda:
x = x.cuda()
# forward pass
with torch.no_grad():
y, feature = net(x)
# make score and link map
score_text = y[0,:,:,0].cpu().data.numpy()
score_link = y[0,:,:,1].cpu().data.numpy()
# refine link
if refine_net is not None:
with torch.no_grad():
y_refiner = refine_net(y, feature)
score_link = y_refiner[0,:,:,0].cpu().data.numpy()
t0 = time.time() - t0
t1 = time.time()
# Post-processing
boxes, polys = craft_utils.getDetBoxes(score_text, score_link, text_threshold, link_threshold, low_text, poly)
# coordinate adjustment
boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
for k in range(len(polys)):
if polys[k] is None: polys[k] = boxes[k]
t1 = time.time() - t1
# render results (optional)
render_img = score_text.copy()
render_img = np.hstack((render_img, score_link))
ret_score_text = imgproc.cvt2HeatmapImg(render_img)
if args.show_time : print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
return boxes, polys, ret_score_text
##________________________________________
#Argument folder
parser = argparse.ArgumentParser(description='OCR-Dotted-Matrix')
parser.add_argument('--image', type=str, help='test image')
parser.add_argument('--label', type=str, help='label')
parser.add_argument('--folder_res', type=str, help='folder result')
#Argument CRAFT
parser.add_argument('--trained_model', default='weights/craft_mlt_25k.pth', type=str, help='pretrained model')
parser.add_argument('--text_threshold', default=0.9, type=float, help='text confidence threshold')
parser.add_argument('--low_text', default=0.3, type=float, help='text low-bound score')
parser.add_argument('--link_threshold', default=0.1, type=float, help='link confidence threshold')
parser.add_argument('--cuda', default=True, type=str2bool, help='Use cuda for inference')
parser.add_argument('--canvas_size', default=1280, type=int, help='image size for inference')
parser.add_argument('--mag_ratio', default=1.5, type=float, help='image magnification ratio')
parser.add_argument('--poly', default=False, action='store_true', help='enable polygon type')
parser.add_argument('--show_time', default=False, action='store_true', help='show processing time')
parser.add_argument('--test_folder', default='/data/', type=str, help='folder path to input images')
parser.add_argument('--refine', default=True, action='store_true', help='enable link refiner')
parser.add_argument('--refiner_model', default='weights/craft_refiner_CTW1500.pth', type=str, help='pretrained refiner model')
args = parser.parse_args()
if __name__=="__main__":
#____________________FOLDER_INITIALIZE____________
if args.image is None:
print("insert test path image...")
sys.exit()
image=args.image
if not exists(image):
print("the specified path does not exist...")
sys.exit()
if not isfile(image):
print("The item at the specified path does not appear to be a file..")
sys.exit()
#SAVE_folder_result
try:
mkdir("result")
except FileExistsError:
# directory already exists
pass
save_path_folder=join("result",args.folder_res)
try:
mkdir(save_path_folder)
except FileExistsError:
# directory already exists
pass
headers = ['Name_original_file',
'Name_preprocess',
'check_label',
'tesseract_LCDDot_FT_500_psm3_result',
'LCDDot_FT_500_psm3_sequence_matcher_ratio_result',
'LCDDot_FT_500_psm3_bool_re_result']
#_______________________________
#____LOADING_NET______________
#_______________________________
net = CRAFT() # initialize
print('Loading weights from checkpoint (' + args.trained_model + ')')
if args.cuda:
net.load_state_dict(copyStateDict(torch.load(args.trained_model)))
else:
net.load_state_dict(copyStateDict(torch.load(args.trained_model, map_location='cpu')))
if args.cuda:
net = net.cuda()
net = torch.nn.DataParallel(net)
cudnn.benchmark = False
net.eval()
#_______________________________
# ____LINK REFINER______________
#_______________________________
refine_net = None
if args.refine:
from refinenet import RefineNet
refine_net = RefineNet()
#print('Loading weights of refiner from checkpoint (' + args.refiner_model + ')')
if args.cuda:
refine_net.load_state_dict(copyStateDict(torch.load(args.refiner_model)))
refine_net = refine_net.cuda()
refine_net = torch.nn.DataParallel(refine_net)
else:
refine_net.load_state_dict(copyStateDict(torch.load(args.refiner_model, map_location='cpu')))
refine_net.eval()
args.poly = True
t = time.time()
#_______________________________
# ____TEST_IMAGES______________
#_______________________________
#for filename in filename_array:
start_time = time.time()
check_label=args.label
filename=os.path.basename(args.image)
save_path=join(save_path_folder,filename)
save_path_CRAFT=join(save_path,"result_CRAFT")
save_path_images=join(save_path,"ritagli")
#_CREATE folder for single image test
try:
mkdir(save_path)
mkdir(save_path_CRAFT)
mkdir(save_path_images)
except FileExistsError:
# directory already exists
pass
img=cv2.imread(args.image,0)
image_basename=basename(filename)
zoomed=clipped_zoom(img,0.7)
cv2.imwrite(join(save_path,"zoomed.jpg"),zoomed)
preprocessed=preprocess_CRAFT(zoomed)
cv2.imwrite(join(save_path,"preprocessed.jpg"),preprocessed)
image = imgproc.loadImage(join(save_path,"preprocessed.jpg"))
bboxes, polys, score_text = test_net(net, image, args.text_threshold, args.link_threshold, args.low_text, args.cuda, args.poly, refine_net)
# save score text
mask_file = save_path_CRAFT + "/res_preprocessed_mask.jpg"
cv2.imwrite(mask_file, score_text)
file_utils.saveResult("preprocessed", image[:,:,::-1], polys, dirname=save_path_CRAFT)
#______________________________________________________
#__________EXTRACT_THE_BOUNDING_BOX____________________
#______________________________________________________
with open(join(save_path_CRAFT,"res_preprocessed.txt")) as fin:
i=0
for line in fin:
if(len(line)!=1):
coord=np.array(line.rstrip().split(',')).astype(int).reshape(-1,2)
if len(coord)>4:
pts=minimum_bounding_rectangle(coord)
coord=order_points(pts)
diffs=[coord[0,0]-coord[-1,0],coord[0,1]-coord[-1,1]]
angle=-(math.atan(diffs[0]/diffs[1])*180/math.pi)
dist1=math.ceil(sqrt((coord[-1,0]-coord[0,0])**2+(coord[-1,1]-coord[0,1])**2))
dist2=math.ceil(sqrt((coord[1,0]-coord[0,0])**2+(coord[1,1]-coord[0,1])**2))
im=zoomed
center=[coord[0,0],coord[0,1]]
rotated=rotate_image(im,angle,center)
cut=rotated[coord[0,1]:coord[0,1]+dist1,coord[0,0]:coord[0,0]+dist2]
if cut.shape[0]>cut.shape[1]:
cut=cv2.rotate(cut,0)
###### Original image (without preprocessing)
im_pil = Image.fromarray(cut)
im_pil.save(join(save_path_images,'_original_'+'{}'.format(i)+'.jpg'), dpi=(300.0, 300.0))
cut_flipped=cv2.rotate(cut,1)
####### PREPROCESSING
# try different parameters of resize and kernel of Morphological Transformations
# for enhancing tesseract recognition
size_pool=((1.2,1.2),(1.5,1.5))
kernel_pool=((3,3),(5,5),(7,7),(9,9))
clipped_zoom_pool=(0.3,0.4,0.5,0.7)
kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)) #kernel per
kernel3 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(7,5)) #kernel per
index_save=0
for fx,fy in size_pool:
image_original=cv2.resize(cut, None, fx=fx, fy=fy, interpolation=cv2.INTER_CUBIC)
for factor in clipped_zoom_pool:
image_original_resize=clipped_zoom(image_original,factor)
_, blackAndWhite = cv2.threshold(image_original_resize,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
for kernel in kernel_pool:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, kernel)
opening_1= cv2.morphologyEx(blackAndWhite, cv2.MORPH_OPEN, kernel)
# Saving
im_pil = Image.fromarray(opening_1)
im_pil.save(join(save_path_images,'_preprocess_'+'{}{}'.format(index_save,i)+'.jpg'), dpi=(300.0, 300.0))
opening_2 = cv2.dilate(255-opening_1, kernel2,iterations = 1)
im_pil = Image.fromarray(255-opening_2)
im_pil.save(join(save_path_images,'_preprocess_kernel2'+'{}{}'.format(index_save,i)+'.jpg'), dpi=(300.0, 300.0))
opening_3 = cv2.dilate(255-opening_1, kernel2,iterations = 1)
im_pil = Image.fromarray(255-opening_3)
im_pil.save(join(save_path_images,'_preprocess_kernel3'+'{}{}'.format(index_save,i)+'.jpg'), dpi=(300.0, 300.0))
index_save+=1
i+=1
######
if i==0:
print("No text was detected (no bounding box) in the image...")
#_________________________________________________________
#__________TEXT_RECOGNITION_WITH_TESSERACT__________________
#_______________________________________________________
if not listdir(save_path_images):
df = pd.DataFrame([[filename,'NONE', check_label.replace(" ", ""),"NONE",0.0,False]], columns=headers)
with open(join(save_path,'result.json'), 'w') as the_file:
the_file.write(df.to_json(orient="records"))
else:
files=[f for f in listdir(save_path_images) if isfile(join(save_path_images,f))]
json_dict={}
Name_array_preprocess=[]
LCDDot_FT_500_psm3_ocr=[]
LCDDot_FT_500_psm3_sequence_matcher_ratio=[]
LCDDot_FT_500_psm3_bool_re=[]
#tessaract ocr
for file in files:
img=cv2.imread(join(save_path_images,file),0)
Name_array_preprocess.append(file)
text_3=pytesseract.image_to_string(img, lang='LCDDot_FT_500', config="-c tessedit_char_whitelist=0123456789ABCDEFGHIJKLMNOPRSTUVWXY.-:").replace('\x0c','').replace(' ','').replace("\n", " ").replace(" ", "")
LCDDot_FT_500_psm3_ocr.append(text_3)
LCDDot_FT_500_psm3_sequence_matcher_ratio.append(round(SequenceMatcher(None, check_label.replace(" ", ""), text_3).ratio(), 2)) #ratio seqence matcher
LCDDot_FT_500_psm3_bool_re.append(bool(re.match(check_label.replace(" ", ""),text_3))) #matche with re
#create data frame to store result of different preprocess
d = {'Name_original_file': filename,
'Name_preprocess': Name_array_preprocess,
'check_label': check_label.replace(" ", ""),
'tesseract_LCDDot_FT_500_psm3_result':LCDDot_FT_500_psm3_ocr,
'LCDDot_FT_500_psm3_sequence_matcher_ratio_result':LCDDot_FT_500_psm3_sequence_matcher_ratio,
'LCDDot_FT_500_psm3_bool_re_result': LCDDot_FT_500_psm3_bool_re,
}
df = pd.DataFrame(data=d)
dfj = df.groupby(["Name_preprocess"]).apply(lambda x: x.to_dict("r")).to_json(orient="records")
#save dataframe in JSON file
with open(join(save_path,'result.json'), 'w') as the_file:
the_file.write(dfj)
print("--- %s seconds ---" % (time.time() - start_time))