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| # from __future__ import division, print_function | |
| # coding=utf-8 | |
| # import sys | |
| import os | |
| # import glob | |
| # import re | |
| import numpy as np | |
| # import datetime | |
| # Keras | |
| # from tensorflow.keras.models import load_model | |
| #from tensorflow.keras.preprocessing import image | |
| # Flask utils | |
| # from flask import Flask, redirect, url_for, request, render_template | |
| # from werkzeug.utils import secure_filename | |
| # from gevent.pywsgi import WSGIServer | |
| #import everytnimg | |
| # from skimage.io import imread, imshow | |
| # from skimage.filters import gaussian, threshold_otsu | |
| # from skimage.feature import canny | |
| # from skimage.transform import probabilistic_hough_line, rotate | |
| # from process_image import process_image | |
| # import glob | |
| # import math | |
| import cv2 | |
| # import numpy as np | |
| # from PIL import Image | |
| # from matplotlib import pyplot as plt | |
| # from matplotlib.patches import Rectangle | |
| #%matplotlib inline | |
| # from collections import OrderedDict | |
| # from PIL import Image | |
| # import pandas as pd | |
| # import seaborn as sns | |
| # import math | |
| #import all from Hough transfrom cell | |
| # from skimage.transform import hough_line, hough_line_peaks | |
| # from skimage.transform import rotate | |
| # from skimage.feature import canny | |
| # from skimage.io import imread | |
| # from skimage.color import rgb2gray | |
| # import matplotlib.pyplot as plt | |
| # from scipy.stats import mode as md | |
| # from myhough import deskew, deskew2 | |
| # from segment_words import sortit,words,createk,hpf,bps,wps,baw | |
| # from myverify import verify | |
| #from detect_frame import detect_frame | |
| # import pathlib | |
| #import more | |
| import tensorflow as tf | |
| from object_detection.utils import config_util | |
| # from object_detection.protos import pipeline_pb2 | |
| # from google.protobuf import text_format | |
| # import os | |
| from object_detection.utils import label_map_util | |
| # from object_detection.utils import visualization_utils as viz_utils | |
| from object_detection.builders import model_builder | |
| # Load pipeline config and build a detection model | |
| WORKSPACE_PATH = 'Tensorflow/workspace' | |
| # SCRIPTS_PATH = 'Tensorflow/scripts' | |
| #APIMODEL_PATH = 'Tensorflow/models' | |
| ANNOTATION_PATH = WORKSPACE_PATH+'/annotations' | |
| # IMAGE_PATH = WORKSPACE_PATH+'/images' | |
| MODEL_PATH = WORKSPACE_PATH+'/models' | |
| PRETRAINED_MODEL_PATH = WORKSPACE_PATH+'/pre-trained-models' | |
| CONFIG_PATH = MODEL_PATH+'/my_ssd_mobnet/pipeline.config' | |
| CHECKPOINT_PATH = MODEL_PATH+'/my_ssd_mobnet/' | |
| # INPUT_IMAGE_PATH = 'Tensorflow/myimages' | |
| # MODEL_PATH = 'E:/RealTimeObjectDetection/model.best.hdf5' | |
| configs = config_util.get_configs_from_pipeline_file(CONFIG_PATH) | |
| detection_model = model_builder.build(model_config=configs['model'], is_training=False) | |
| # Restore checkpoint | |
| ckpt = tf.compat.v2.train.Checkpoint(model=detection_model) | |
| ckpt.restore(os.path.join(CHECKPOINT_PATH, 'ckpt-51')).expect_partial() | |
| def detect_fn(image): | |
| image, shapes = detection_model.preprocess(image) | |
| prediction_dict = detection_model.predict(image, shapes) | |
| detections = detection_model.postprocess(prediction_dict, shapes) | |
| return detections | |
| def detect_frame(frame,isRealTime = False): | |
| image_np = np.array(frame) | |
| cpimg = frame.copy() | |
| input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32) | |
| detections = detect_fn(input_tensor) | |
| print(len(detections)) | |
| num_detections = int(detections.pop('num_detections')) | |
| #print("hello") | |
| #print(num_detections) | |
| #print(len(detections['detection_scores'])) | |
| detections = {key: value[0, :num_detections].numpy() | |
| for key, value in detections.items()} | |
| detections['num_detections'] = num_detections | |
| row,col,dummy = image_np.shape | |
| # detection_classes should be ints. | |
| detections['detection_classes'] = detections['detection_classes'].astype(np.int64) | |
| #print(detections['detection_classes']) | |
| mark = [0]*15 | |
| myletters = [] | |
| for i in range(0,15): | |
| curi=detections['detection_classes'][i] | |
| classi=classes[curi] | |
| print(classes[curi],end='-') | |
| cur=detections['detection_scores'][i] | |
| if(cur<0.2): | |
| continue | |
| print(cur,end=' ') | |
| print(detections['detection_boxes'][i], end=' ') | |
| x0=(detections['detection_boxes'][i][0]) | |
| y0=(detections['detection_boxes'][i][1]) | |
| x1=(detections['detection_boxes'][i][2]) | |
| y1=(detections['detection_boxes'][i][3]) | |
| curarea=(x1-x0)*(y1-y0) | |
| ok=1 | |
| for j in range(0,i): | |
| #print(mark[j]) | |
| if mark[j]==0: | |
| continue | |
| curj=detections['detection_classes'][j] | |
| classj=classes[curj] | |
| if classi=='ি' or classj=='ি': | |
| if classi!=classj: | |
| continue | |
| if classi=='ী' or classj=='ী': | |
| if classi!=classj: | |
| continue | |
| x2=(detections['detection_boxes'][j][0]) | |
| y2=(detections['detection_boxes'][j][1]) | |
| x3=(detections['detection_boxes'][j][2]) | |
| y3=(detections['detection_boxes'][j][3]) | |
| x4=max(x0,x2) | |
| y4=max(y0,y2) | |
| x5=min(x1,x3) | |
| y5=min(y1,y3) | |
| if x4>x5 or y4>y5: | |
| continue | |
| prevarea=(x3-x2)*(y3-y2) | |
| commonarea=(x5-x4)*(y5-y4) | |
| ins1=curarea/commonarea | |
| ins2=prevarea/commonarea | |
| ins=commonarea/(curarea+prevarea-commonarea) | |
| print(ins1,end=' ') | |
| if(ins>=0.5): | |
| ok=0 | |
| cur=detections['detection_classes'][j] | |
| print(classes[cur]) | |
| break | |
| if ok==1: | |
| mark[i]=1 | |
| cur=detections['detection_classes'][i] | |
| #myletters.append(classes[cur]) | |
| print(ok) | |
| #verification | |
| for i in range(0,15): | |
| if mark[i]==0 or avver==0: | |
| continue | |
| if detections['detection_classes'][i]>38: | |
| continue | |
| x0=int(detections['detection_boxes'][i][0]*row) | |
| y0=int(detections['detection_boxes'][i][1]*col) | |
| x1=int(detections['detection_boxes'][i][2]*row) | |
| y1=int(detections['detection_boxes'][i][3]*col) | |
| #print(y0,y1,x0,x1) | |
| currImg = cpimg[x0:x1,y0:y1] | |
| curscore = detections['detection_scores'][i] | |
| curclass = detections['detection_classes'][i] | |
| label,conf = verify(currImg) | |
| #print(ulta[label],conf) | |
| #print(curclass,curscore) | |
| if conf>curscore and ulta[label]!=curclass and ulta[label]!=-1: | |
| detections['detection_classes'][i]=ulta[label] | |
| detections['detection_scores'][i]=conf | |
| for i in range(0,15): | |
| if(detections['detection_scores'][i]<0.2): | |
| continue | |
| if mark[i]==0: | |
| continue | |
| cur=detections['detection_classes'][i] | |
| cur=classes[cur] | |
| y0=(detections['detection_boxes'][i][1]) | |
| y1=(detections['detection_boxes'][i][3]) | |
| pair = (y0,cur,y1) | |
| myletters.append(pair) | |
| myletters.sort(key = lambda x: x[0]) | |
| #print(myletters) | |
| for i in range(len(myletters)-1,-1,-1): | |
| y0=myletters[i][0] | |
| curr=myletters[i][1] | |
| y1=myletters[i][2] | |
| if curr=='ু' or curr=='্র': | |
| mxarea=0 | |
| mxno=i-1 | |
| for j in range(0,len(myletters)): | |
| if i==j: | |
| continue | |
| y2=myletters[j][0] | |
| y3=myletters[j][2] | |
| curcommon = min(y3,y1)-max(y0,y2) | |
| if curcommon>mxarea: | |
| mxarea = curcommon | |
| mxno=j | |
| if mxno!=(i-1): | |
| myletters[i],myletters[i+1]=myletters[i+1],myletters[i] | |
| res_list = [x[1] for x in myletters] | |
| print(res_list) | |
| for i in range(len(res_list)-2, -1, -1): | |
| x=res_list[i] | |
| y=res_list[i+1] | |
| if x=='ে' or x=='ি': | |
| res_list[i],res_list[i+1]=res_list[i+1],res_list[i] | |
| for i in range(len(res_list)-2, -1, -1): | |
| x=res_list[i] | |
| y=res_list[i+1] | |
| print(x,y) | |
| if x=='অ' and y=='া': | |
| print('yo') | |
| res_list[i]='আ' | |
| res_list.pop(i+1) | |
| print(res_list) | |
| for i in res_list: | |
| print(i,end='') | |
| print(' ') | |
| return res_list | |
| # Define a flask app | |
| # app = Flask(__name__) | |
| # Model saved with Keras model.save() | |
| # Load your trained model | |
| # model = load_model(MODEL_PATH) | |
| #model._make_predict_function() # Necessary | |
| # print('Model loaded. Start serving...') | |
| # You can also use pretrained model from Keras | |
| # Check https://keras.io/applications/ | |
| #from keras.applications.resnet50 import ResNet50 | |
| #model = ResNet50(weights='imagenet') | |
| #model.save('') | |
| # print('Model loaded. Check http://127.0.0.1:5000/') | |
| avver=0 | |
| clicked=1 | |
| wp = None; bp = None; | |
| category_index = label_map_util.create_category_index_from_labelmap(ANNOTATION_PATH+'/label_map.pbtxt') | |
| classes=['অ','ই','উ','এ','ও','ক','খ','গ','ঘ','চ','ছ','জ','ঝ','ট','ঠ','ড','ত','থ','দ','ধ','ন','প','ফ','ব','ভ','ম','য','র','ল','শ','ষ','স','হ','ড়','য়','ৎ','ং','ঁ','০','১','২','৩','৪','৫','৭','৮','া','ি','ী','ে','ু','্র','্য'] | |
| labels=[1,2,4,7,9,11,12,13,14,16,17,18,19,21,22,23,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,45,46,47,49,50,51,52,53,54,55,57,58,60,61,62,63,64,66,67] | |
| ulta=[0,-1,1,-1,2,-1,-1,3,-1,4,-1,5,6,7,8,-1,9,10,11,12,-1,13,14,15,-1,-1,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,-1,34,35,36,-1,37,38,39,40,41,42,43,-1,44,45,-1,46,47,48,49,50,-1,51,52] | |
| def model_predict(word): | |
| #img = cv2.imread(img_path,cv2.IMREAD_GRAYSCALE) | |
| ''' | |
| if clicked==1: | |
| bp = 66 | |
| wp = 160 | |
| mode = "GCMODE" | |
| if mode == "GCMODE": | |
| img= hpf(img,kSize = 51) | |
| wp = 127 | |
| img = wps(img,wp) | |
| img = bps(img) | |
| elif mode == "RMODE": | |
| bps() | |
| wps() | |
| elif mode == "SMODE": | |
| bps() | |
| wps() | |
| baw() | |
| img = cv2.fastNlMeansDenoising(img, img, 50.0, 7, 21) | |
| print("\ndone.") | |
| xs=img.shape | |
| if len(xs)==3: | |
| img = img[:,:,0] | |
| img = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2) | |
| angeel = deskew(img) | |
| if angeel!=0: | |
| img = deskew2(img,angeel) | |
| ho,wo=img.shape | |
| area=ho*wo | |
| ara=words(img,25,11,7,area/5000) | |
| ara.reverse() | |
| #cv2.imshow('input image',img) | |
| sz=len(ara) | |
| for i in range(0,sz): | |
| ara[i]=sorted(ara[i], key=lambda entry:entry[0][0]) | |
| cnt2=0 | |
| files = glob.glob('Tensorflow/myimages/*') | |
| for f in files: | |
| os.remove(f) | |
| for i in range(0,sz): | |
| #print(ara[i].shape) | |
| tmp=ara[i] | |
| sz2=len(tmp) | |
| if i%10==0: | |
| cnt2=cnt2+1 | |
| for j in range(0,sz2): | |
| a,b=tmp[j] | |
| b = cv2.adaptiveThreshold(b,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2) | |
| if j<10: | |
| cnt3=0 | |
| elif j<20: | |
| cnt3=1 | |
| else: | |
| cnt3=2 | |
| cv2.imwrite('Tensorflow/myimages/ocr %d%d%d%d.jpg' % (cnt2,i,cnt3,j), b) | |
| #cv2.imshow('Crop %d%d' % (i,j), b) | |
| cv2.waitKey(0) | |
| PATH_TO_TEST_IMAGES_DIR = pathlib.Path('Tensorflow/myimages') | |
| TEST_IMAGE_PATHS = (list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpg"))+list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpeg"))) #+list(PATH_TO_TEST_IMAGES_DIR.glob("*.png")) | |
| print(len(TEST_IMAGE_PATHS)) | |
| final = [] | |
| for image_path in TEST_IMAGE_PATHS: | |
| print("ovi") | |
| print(image_path) | |
| frame = cv2.imread(str(image_path)) | |
| x=str(image_path) | |
| print(x[25]) | |
| # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| final.append((detect_frame(frame),x[25])) | |
| ''' | |
| frame = cv2.fastNlMeansDenoising(word,word, 50.0, 7, 21) | |
| xs = frame.shape | |
| if(len(xs)==3): | |
| frame = frame[:,:,0] | |
| frame= cv2.adaptiveThreshold(frame,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2) | |
| frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR) | |
| # x=str(img_path) | |
| #print(x[25]) | |
| # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| image_np = np.array(frame) | |
| cpimg = frame.copy() | |
| input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32) | |
| image_t, shapes = detection_model.preprocess(input_tensor) | |
| prediction_dict = detection_model.predict(image_t, shapes) | |
| detections = detection_model.postprocess(prediction_dict, shapes) | |
| # print(len(detections)) | |
| num_detections = int(detections.pop('num_detections')) | |
| #print("hello") | |
| #print(num_detections) | |
| #print(len(detections['detection_scores'])) | |
| detections = {key: value[0, :num_detections].numpy() | |
| for key, value in detections.items()} | |
| detections['num_detections'] = num_detections | |
| row,col,dummy = image_np.shape | |
| # detection_classes should be ints. | |
| detections['detection_classes'] = detections['detection_classes'].astype(np.int64) | |
| #print(detections['detection_classes']) | |
| mark = [0]*15 | |
| myletters = [] | |
| for i in range(0,15): | |
| curi=detections['detection_classes'][i] | |
| classi=classes[curi] | |
| # print(classes[curi],end='-') | |
| cur=detections['detection_scores'][i] | |
| if(cur<0.2): | |
| continue | |
| # print(cur,end=' ') | |
| # print(detections['detection_boxes'][i], end=' ') | |
| x0=(detections['detection_boxes'][i][0]) | |
| y0=(detections['detection_boxes'][i][1]) | |
| x1=(detections['detection_boxes'][i][2]) | |
| y1=(detections['detection_boxes'][i][3]) | |
| curarea=(x1-x0)*(y1-y0) | |
| ok=1 | |
| for j in range(0,i): | |
| #print(mark[j]) | |
| if mark[j]==0: | |
| continue | |
| curj=detections['detection_classes'][j] | |
| classj=classes[curj] | |
| if classi=='ি' or classj=='ি': | |
| if classi!=classj: | |
| continue | |
| if classi=='ী' or classj=='ী': | |
| if classi!=classj: | |
| continue | |
| x2=(detections['detection_boxes'][j][0]) | |
| y2=(detections['detection_boxes'][j][1]) | |
| x3=(detections['detection_boxes'][j][2]) | |
| y3=(detections['detection_boxes'][j][3]) | |
| x4=max(x0,x2) | |
| y4=max(y0,y2) | |
| x5=min(x1,x3) | |
| y5=min(y1,y3) | |
| if x4>x5 or y4>y5: | |
| continue | |
| prevarea=(x3-x2)*(y3-y2) | |
| commonarea=(x5-x4)*(y5-y4) | |
| ins1=curarea/commonarea | |
| ins2=prevarea/commonarea | |
| ins=commonarea/(curarea+prevarea-commonarea) | |
| # print(ins1,end=' ') | |
| if(ins>=0.5): | |
| ok=0 | |
| cur=detections['detection_classes'][j] | |
| # print(classes[cur]) | |
| break | |
| if ok==1: | |
| mark[i]=1 | |
| cur=detections['detection_classes'][i] | |
| #myletters.append(classes[cur]) | |
| # print(ok) | |
| #verification | |
| for i in range(0,15): | |
| if mark[i]==0 or avver==0: | |
| continue | |
| if detections['detection_classes'][i]>38: | |
| continue | |
| x0=int(detections['detection_boxes'][i][0]*row) | |
| y0=int(detections['detection_boxes'][i][1]*col) | |
| x1=int(detections['detection_boxes'][i][2]*row) | |
| y1=int(detections['detection_boxes'][i][3]*col) | |
| #print(y0,y1,x0,x1) | |
| currImg = cpimg[x0:x1,y0:y1] | |
| curscore = detections['detection_scores'][i] | |
| curclass = detections['detection_classes'][i] | |
| label,conf = verify(currImg) | |
| #print(ulta[label],conf) | |
| #print(curclass,curscore) | |
| if conf>curscore and ulta[label]!=curclass and ulta[label]!=-1: | |
| detections['detection_classes'][i]=ulta[label] | |
| detections['detection_scores'][i]=conf | |
| for i in range(0,15): | |
| if(detections['detection_scores'][i]<0.2): | |
| continue | |
| if mark[i]==0: | |
| continue | |
| cur=detections['detection_classes'][i] | |
| cur=classes[cur] | |
| y0=(detections['detection_boxes'][i][1]) | |
| y1=(detections['detection_boxes'][i][3]) | |
| pair = (y0,cur,y1) | |
| myletters.append(pair) | |
| myletters.sort(key = lambda x: x[0]) | |
| #print(myletters) | |
| for i in range(len(myletters)-1,-1,-1): | |
| y0=myletters[i][0] | |
| curr=myletters[i][1] | |
| y1=myletters[i][2] | |
| if curr=='ু' or curr=='্র': | |
| mxarea=0 | |
| mxno=i-1 | |
| for j in range(0,len(myletters)): | |
| if i==j: | |
| continue | |
| y2=myletters[j][0] | |
| y3=myletters[j][2] | |
| curcommon = min(y3,y1)-max(y0,y2) | |
| if curcommon>mxarea: | |
| mxarea = curcommon | |
| mxno=j | |
| if mxno!=(i-1): | |
| myletters[i],myletters[i+1]=myletters[i+1],myletters[i] | |
| res_list = [x[1] for x in myletters] | |
| # print(res_list) | |
| for i in range(len(res_list)-2, -1, -1): | |
| x=res_list[i] | |
| y=res_list[i+1] | |
| if x=='ে' or x=='ি': | |
| res_list[i],res_list[i+1]=res_list[i+1],res_list[i] | |
| for i in range(len(res_list)-2, -1, -1): | |
| x=res_list[i] | |
| y=res_list[i+1] | |
| # print(x,y) | |
| if x=='অ' and y=='া': | |
| # print('yo') | |
| res_list[i]='আ' | |
| res_list.pop(i+1) | |
| # print(res_list) | |
| output='' | |
| for i in res_list: | |
| output=output+i | |
| # print(' ') | |
| # time_now = datetime.datetime.now().strftime('%m_%d_%Y_%I_%M_%S_%p') | |
| # # print(time_now) | |
| # date = datetime.date.today().strftime('%Y_%m_%d') | |
| # # print(date) | |
| # folderName = "created/"+date | |
| # if(not os.path.isdir(folderName)): | |
| # os.makedirs(folderName) | |
| # fileName = folderName+ "/" + time_now + ".png" | |
| # cv2.imwrite(fileName,word) | |
| return output | |
| ''' | |
| output='' | |
| for i in range(0,len(final)): | |
| ara=final[i][0] | |
| numb=final[i][1] | |
| if i>0 and numb!=final[i-1][1]: | |
| output= output+'\n' | |
| word = ''.join(ara) | |
| #corrected_word = get_campaign(word) | |
| output= output + word | |
| #print(corrected_word,end='') | |
| output = output + ' ' | |
| return output | |
| ''' | |
| import gradio as gr | |
| demo = gr.Interface(fn=model_predict, inputs= "paint", outputs="text") | |
| demo.launch() | |