Delete full.py

full.py

@@ -1,448 +0,0 @@
-import streamlit as st  #Web App
-from PIL import Image, ImageOps #Image Processing
-import time
-from unittest import result
-from pythainlp.util import isthai
-import numpy as np
-import easyocr as ocr  #OCR
-import editdistance
-from fastbook import *
-from fastai.vision import *
-from glob import glob
-from pathlib import Path
-from sklearn.metrics import precision_recall_fscore_support, accuracy_score, roc_auc_score
-
-
-
-
-st.sidebar.image("./logo.png")
-
-st.sidebar.header("ATK-OCR classification (AOC) Webapp.")
-
-
-activities = ["Detection", "About"]
-choice = st.sidebar.selectbox("Select option..",activities)
-
-
-
-
-
-#set default size as 1280 x 1280
-def img_resize(input_path,img_size): # padding
-  desired_size = img_size
-  im = Image.open(input_path)
-  im = ImageOps.exif_transpose(im) # fix image rotating
-  width, height = im.size # get img_input size
-  if (width == 1280) and (height == 1280):
-    new_im = im
-  else:
-    #im = im.convert('L') #Convert to gray
-    old_size = im.size  # old_size[0] is in (width, height) format
-    ratio = float(desired_size)/max(old_size)
-    new_size = tuple([int(x*ratio) for x in old_size])
-    im = im.resize(new_size, Image.ANTIALIAS)
-    new_im = Image.new("RGB", (desired_size, desired_size))
-    new_im.paste(im, ((desired_size-new_size[0])//2,
-                        (desired_size-new_size[1])//2))
-
-  return new_im
-
-
-checkpoint_path = "./ATK Efficientb_7 FastAI(96%).pkl"
-
-learn_inf = load_learner(checkpoint_path)
-model = learn_inf.model.eval()
-
-
-
-
-def get_detection(img_path):
-  bytes_data = img_path.getvalue() # change fileuploader type to bytes (st.file_uploader)
-  pred = learn_inf.predict(bytes_data)
-  detect_val = ""
-  if pred[0] == "1_Positive":
-    detect_val = "Positive"
-    st.error("Result : {} with {}% confidence".format(detect_val, round(float(pred[2][1]*100),2)))
-  if pred[0] == "0_Negative":
-    detect_val = "Negative"
-    st.success("Result : {} with {}% confidence".format(detect_val, round(float(pred[2][0]*100),2)))
-
-
-  
-@st.cache
-def load_model(): 
-    reader = ocr.Reader(['en'],model_storage_directory='.')
-    return reader 
-
-reader = load_model() #load model
-
-def Get_Idcard_detail(file_path):
-  raw_data = []
-  id_num = {"id_num" : "None"}
-  name = file_path
-  img = Image.open(name)
-  img = ImageOps.exif_transpose(img) # fix image rotating
-
-  width, height = img.size # get img_input size
-  if (width == 1280) and (height == 1280):
-    result = reader.readtext(np.array(img))
-  else:
-    #im = im.convert('L') #Convert to gray
-    old_size = img.size  # old_size[0] is in (width, height) format
-    ratio = float(1280)/max(old_size)
-    new_size = tuple([int(x*ratio) for x in old_size])
-    img = img.resize(new_size, Image.ANTIALIAS)
-    new_im = Image.new("RGB", (1280, 1280))
-    new_im.paste(img, ((1280-new_size[0])//2,
-                        (1280-new_size[1])//2))
-    
-    result = reader.readtext(np.array(new_im))
-
-
-  
-
-  result_text = [] #empty list for results
-  for text in result:
-    result_text.append(text[1])
-
-
-  raw_data = result_text  
-
-
-
-  def get_english(raw_list): # Cut only english var
-    eng_name = []
-    thai_name = []
-
-    for name in raw_list:
-      if isthai(name) == True:
-        thai_name.append(name)
-      else:
-        eng_name.append(name)
-
-    return eng_name
-
-  raw_data = get_english(raw_data)
-
-
-  def Clear_syntax(raw_list):
-
-    Clean_syntax = ["","#","{","}","=","/","@","#","$","—","|","%","-","(",")","¥", "[", "]", "‘",':',';']
-
-    for k in range(len(Clean_syntax)):
-      while (Clean_syntax[k] in raw_list): # remove single symbol
-        raw_list.remove(Clean_syntax[k])
-
-    for l in range(len(raw_list)): 
-      raw_list[l] = raw_list[l].replace("!","l") #split ! --> l (Error OCR Check)
-      raw_list[l] = raw_list[l].replace(",",".") #split ! --> l (Error OCR Check)
-      raw_list[l] = raw_list[l].replace(" ","") #split " " out from str
-      raw_list[l] = raw_list[l].lower() #Set all string to lowercase
-
-    for m in range(len(raw_list)): #Clear symbol in str "Hi/'" --> "Hi"
-      for n in range(len(Clean_syntax)):
-          raw_list[m] = raw_list[m].replace(Clean_syntax[n],"") 
-    return raw_list
-
-  raw_data = Clear_syntax(raw_data)
-
-
-  def get_idnum(raw_list):
-    id_num = {"id_num" : "None"}
-    # 1. normal check 
-    for i in range(len(raw_list)): # check if len(list) = 1, 4, 5, 2, 1 (13 digit idcard) and all is int
-      try:
-        if ((len(raw_list[i]) == 1) and (len(raw_list[i+1]) == 4) and (len(raw_list[i+2]) == 5) and (len(raw_list[i+3]) == 2) and (len(raw_list[i+4]) == 1)) and ((raw_list[i] + raw_list[i+1] + raw_list[i+2] + raw_list[i+3] + raw_list[i+4]).isnumeric()):
-          id_num["id_num"] = (raw_list[i] + raw_list[i+1] + raw_list[i+2] + raw_list[i+3] + raw_list[i+4])
-          break 
-      except:
-        pass
-
-    # 2. Hardcore Check
-    if id_num["id_num"] == "None":
-      id_count = 0
-      index_first = 0
-      index_end = 0
-      for i in range(len(raw_list)):
-        if id_count == 13:
-          index_end = i-1 #ลบ 1 index เพราะ ครบ 13 รอบก่อนหน้านี้
-          #print(f"index_first == {index_first} index_end == {index_end}")
-          #print(f"id = {raw_list[index_first:index_end+1]}")
-          id_num["id_num"] = ''.join(raw_list[index_first:index_end+1]) 
-          break
-        else:
-          if raw_list[i].isnumeric() == True and index_first == 0:
-            id_count += len(raw_list[i])
-            index_first = i
-          elif raw_list[i].isnumeric() == True and index_first != 0:
-            id_count += len(raw_list[i])
-          elif raw_list[i].isnumeric() == False:
-            id_count = 0
-            index_first = 0
-
-    return id_num
-
-  id_num = (get_idnum(raw_data))
-
-      #Complete list name check
-  def list_name_check(raw_list):
-    sum_list = raw_list
-    name_key = ['name', 'lastname']
-
-    #1. name_key check
-    if ("name" in sum_list) and ("lastname" in sum_list): # if name and lastname in list pass it!
-      pass
-    else:
-      for i in range(len(name_key)):
-        for j in range(len(sum_list)):
-          if (editdistance.eval(name_key[i], sum_list[j]) <= 2 ): 
-            sum_list[j] = name_key[i]
-
-    gender_key = ["mr.", "mrs.", 'master', 'miss']
-    #2 gender_key check
-    count = 0 # check for break
-    for i in range(len(gender_key)):
-      for j in range(len(sum_list)):
-        if (count == 0):
-          try:
-            if (sum_list[i] == "name") or (sum_list[i] == "lastname"): # skip "name" and "lastname"
-              pass
-            else:
-              # mr, mrs sensitive case double check with len(gender_key) == len(keyword)
-              if (gender_key[i] == "mr." or gender_key[i] == "mrs.") and (editdistance.eval(gender_key[i], sum_list[j]) <= 3 and (len(gender_key[i]) == len(sum_list[j]))): 
-                sum_list[j] = gender_key[i]
-                count+=1
-                #print(1)
-              elif (gender_key[i] == "master" or gender_key[i] == "miss") and (editdistance.eval(gender_key[i], sum_list[j]) <= 3 ) and (len(gender_key[i]) == len(sum_list[j])):
-                sum_list[j] = gender_key[i]
-                count+=1
-                #print(1)
-          except:
-            if (gender_key[i] == "mr." or gender_key[i] == "mrs.") and (editdistance.eval(gender_key[i], sum_list[j]) <= 2 and (len(gender_key[i]) == len(sum_list[j]))): 
-                sum_list[j] = gender_key[i]
-                count+=1
-                #print(1)
-            elif (gender_key[i] == "master" or gender_key[i] == "miss") and (editdistance.eval(gender_key[i], sum_list[j]) <= 3 ) and (len(gender_key[i]) == len(sum_list[j])):
-                sum_list[j] = gender_key[i]
-                count+=1
-                #print(1)
-        else:
-          break
-
-    return sum_list
-
-  raw_data = list_name_check(raw_data)
-
-  #get_eng_name
-  def get_engname(raw_list):
-    get_data = raw_list
-    engname_list = []
-
-    name_pos = [] 
-    lastname_pos = []
-    mr_pos = []
-    mrs_pos = []
-
-      # check keyword by name, lastname, master, mr, miss, mrs
-    for j in range(len(get_data)): #get "name" , "lastname" index
-      if "name" == get_data[j]:
-        name_pos.append(j)
-      elif "lastname" == get_data[j]:
-        lastname_pos.append(j)
-      elif ("mr." == get_data[j]) or ("master" == get_data[j]):
-        mr_pos.append(j)
-      elif ("miss" == get_data[j]) or ("mrs." == get_data[j]):
-        mrs_pos.append(j)
-
-
-    if len(name_pos) != 0: #get_engname ex --> ['name', 'master', 'tanaanan', 'lastname', 'chalermpan']
-      engname_list = get_data[name_pos[0]:name_pos[0]+6] # select first index กรณีมี "name" มากกว่า 1 ตัว
-    elif len(lastname_pos) != 0:
-      engname_list = get_data[lastname_pos[0]-3:lastname_pos[0]+3] 
-    elif len(mr_pos) != 0:
-      engname_list = get_data[mr_pos[0]-1:mr_pos[0]+5]
-    elif len(mrs_pos) != 0:
-      engname_list = get_data[mrs_pos[0]-1:mrs_pos[0]+5]
-    else:
-      print("Can't find eng name!!") 
-
-    return engname_list
-
-  raw_data = get_engname(raw_data)
-
-
-
-
-  def split_genkey(raw_list): # remove stringname + gender_key ex. "missjate" -> "jate"
-    data = raw_list
-    key = ['mrs.','mr.','master','miss']
-    name = "" #gen_key name
-    name_pos = 0
-    gen_index = 0
-    gen_type = "" #male / female
-    # check keyword
-    for key_val in key:
-        for each_text in data:
-            if (each_text[:len(key_val)] == key_val) or (editdistance.eval(each_text[:len(key_val)],key_val) <= 1 and (len(each_text[:len(key_val)]) == len(key_val))):
-                #each_text = each_text[len(key):]
-                if (each_text == "name") or (each_text == "lastname"):
-                  pass
-                else:
-                  name = (each_text[:len(key_val)])
-                  name_pos = data.index(each_text) # get_index
-                  gen_index = len(key_val)
-                  break
-    if (name_pos != 0): 
-      data[name_pos] = data[name_pos][gen_index:] # split gender_key on list
-      for empty_str in range(data.count('')): # clear "empty string"
-        data.remove('')
-    return data
-
-  raw_data = split_genkey(raw_data)
-
-
-  def clean_name_data(raw_list): # delete all single string and int string
-    for k in range(len(raw_list)):
-      try:
-        while ((len(raw_list[k]) <= 2) or (raw_list[k].isnumeric() == True)): # remove single symbol
-          raw_list.remove(raw_list[k])
-      except IndexError:
-        pass
-    return raw_list
-
-  raw_data = clean_name_data(raw_data)
-
-
-  def name_sum(raw_list):
-    info = {"name" : "None",
-            "lastname" : "None"}
-    key = ['mr.','mrs.', 'master', 'miss', 'mrs','mr']
-    name_pos = 0
-    lastname_pos = 0
-    for key_val in key: # remove gender_key in string
-      if key_val in raw_list:
-        raw_list.remove(key_val)
-    try:
-      for i in range(len(raw_list)):
-        if raw_list[i] == "name":
-          info["name"] = raw_list[i+1]
-          name_pos = i+1
-        elif raw_list[i] == "lastname":
-          info["lastname"] = raw_list[i+1]
-          lastname_pos = i+1
-    except:
-      pass
-
-    # กรณี หาอย่างใดอย่าหนึ่งเจอให้ลองข้ามไปดู 1 index name, "name_val", lastname , "lastname_val"
-    if (info["name"] != "None") and (info["lastname"] == "None"):
-      try:
-        info["lastname"] = raw_list[name_pos+2]
-      except:
-        pass
-    elif (info["lastname"] != "None") and (info["name"] == "None"):
-      try:
-        info["name"] = raw_list[lastname_pos-2]
-      except:
-        pass
-
-    # remove . on "mr." and "mrs."
-    info["name"] = info["name"].replace(".","")
-    info["lastname"] = info["lastname"].replace(".","")
-
-
-    return info
-
-  st.subheader("Process Completed!.....")
-  st.write(id_num)
-  st.write(name_sum(raw_data))
-
-
-
-
-
-
-if choice == "Detection":
-
-  st.title("ATK-OCR classification (AOC) Webapp.")
-
-  #subtitle
-  st.subheader(" Antigen test kit + Identification Card detector.")
-
-  pages_name = ['ATK + Idcard Detect', 'ATK Detect', 'Idcard Detect']
-  page = st.radio('Select option mode :', pages_name)
-
-  #image uploader
-  image = st.file_uploader(label = "upload ATK + Idcard img here.. OwO",type=['png','jpg','jpeg'])
-
-  if image is not None:
-    new_img = img_resize(image, 1280)
-
-    if page == "ATK + Idcard Detect":
-      st.image(new_img)
-      with st.spinner("🤖 ATK + Idcard Working... "):
-          
-          t1 = time.perf_counter()
-          Get_Idcard_detail(image)
-          get_detection(image)
-          t2 = time.perf_counter()
-          st.write('time taken to run: {:.2f} sec'.format(t2-t1))
-
-    elif page == "ATK Detect":
-      st.image(new_img)
-      with st.spinner("🤖 ATK Working... "):
-          t1 = time.perf_counter()
-          get_detection(image)
-          t2 = time.perf_counter()
-          st.write('time taken to run: {:.2f} sec'.format(t2-t1))
-
-    elif page == "Idcard Detect":
-      st.image(new_img)
-      with st.spinner("🤖 Idcard Working... "): 
-          t1 = time.perf_counter()
-          Get_Idcard_detail(image)
-          t2 = time.perf_counter()
-          st.write('time taken to run: {:.2f} sec'.format(t2-t1))
-  else:
-    st.write("## Waiting for image..")
-    st.image('atk_idcard.jpeg')
-
-elif choice =='About' :
-  st.header("About...")
-  st.subheader("AOC คืออะไร ?")
-  st.write("- เป็นระบบที่สามารถคัดกรองผลตรวจเชื้อของ COVID-19 ได้ผ่าน ที่ตรวจ ATK (Antigen Test Kit) ควบคู่กับบัตรประชาชน จากรูปภาพได้โดยอัตโนมัติ")
-
-  st.subheader("AOC ทำอะไรได้บ้าง ?")
-  st.write("- ตรวจจับผลตรวจ ATK (Obj detection) [debugging in progress]")
-  st.write("- ตรวจจับชื่อ-นามสกุล (OCR)")
-  st.write("- ตรวจจับเลขบัตรประชาชน (OCR)")
-
-  st.subheader("AOC ดีกว่ายังไง ?")
-  st.write("จากผลที่ได้จากการเปรียบเทียบกันระหว่าง model (AOC) กับ คน (Baseline) จำนวน 30 ภาพ / คน ได้ผลดังนี้")
-  st.image("./acc_table.png")
-  st.write("จากผลที่ได้สรุปได้ว่า ส่วนที่ผ่าน Baseline และมีประสิทธิภาพดีกว่าคัดกรองด้วยคนคือ ผลตรวจ ATK ได้ผลที่ 100 %, เลขบัตรประชน ได้ผลที่ 100 % และ ความเร็วในการคัดกรอง ได้ผลที่ 4.84 วินาที ซึ่งมีความเร็วมากกว่า 81% เมื่อเทียบกับค���ดกรองด้วยคน ถือว่ามีประสิทธิภาพที่สูงมากในการคัดกรอง และ มีประสิทธิภาพมากกว่าการคัดแยกด้วยมนุษย์")
-  st.write("** ความเร็วที่โมเดลทำได้อาจไม่ตรงตามที่ deploy บนเว็บ เนื่องจากในเว็บ ไม่มี GPU ในการประมวลผลอาจทำให้โมเดลใช้เวลาในการประมวลที่นานกว่าตอนใช้ GPU")
-
-
-  st.subheader("คำแนะนำในการใช้งาน")
-  st.write("- ในการใช้งานให้ถ่ายรูปภาพบัตรประชาชนในแนวตั้งเท่านั้น เนื่องจากถ้าเป็นแนวอื่นอาจทำให้การตรวจจับคลาดเคลื่อนเอาได้")#3
-  st.write("- ภาพไม่ควรมีแสงที่สว่างมากเกืนไป และ มืดเกินไป มิฉะนั้นอาจทำให้การตรวจจับคลาดเคลื่อนเอาได้")#4
-  st.write("- ภาพไม่ควรที่จะอยู่ไกลเกินไป และ ควรมีความชัด มิฉะนั้นอาจทำให้การตรวจจับคลาดเคลื่อน หรือ ไม่สามารถตรวจจับได้")#5
-
-  st.subheader("รายละเอียดเพิ่มเติม")
-  st.write('[Medium blog](https://medium.com/@mjsalyjoh/atk-ocr-classification-aoc-%E0%B8%A3%E0%B8%B0%E0%B8%9A%E0%B8%9A%E0%B8%84%E0%B8%B1%E0%B8%94%E0%B8%81%E0%B8%A3%E0%B8%AD%E0%B8%87%E0%B8%9C%E0%B8%A5%E0%B8%95%E0%B8%A3%E0%B8%A7%E0%B8%88-atk-%E0%B9%81%E0%B8%A5%E0%B8%B0-%E0%B8%9A%E0%B8%B1%E0%B8%95%E0%B8%A3%E0%B8%9B%E0%B8%A3%E0%B8%B0%E0%B8%8A%E0%B8%B2%E0%B8%8A%E0%B8%99-fa32a8d47599)')
-  st.write('[Github Link](https://github.com/Tanaanan/AOC_ATK_OCR_Classification)')
-
-  st.warning("** ระบบ ATK ตอนนี้ใช้เป็น Image classification อยู่เนื่องจาก Object detection ยังมีปัญหาในการ deploy on cloud.. (กำลังอยู่ในขั้นตอน debug!)")
-
-
-st.sidebar.subheader('More image for test..')
-st.sidebar.write('[Github img test set.](https://github.com/Tanaanan/AOC_ATK_OCR_Classification/tree/main/test_set(img))')
-
-st.sidebar.markdown('---')
-st.sidebar.subheader('Recomend / Issues report..')
-st.sidebar.write('[Google form](https://forms.gle/zYpYFKcTpBoFGxN58)')
-
-
-st.sidebar.markdown('---')
-st.sidebar.subheader('Made by Tanaanan .M')
-st.sidebar.write("Contact : mjsalyjoh@gmail.com")