Upload app.py

app.py

@@ -0,0 +1,509 @@
+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
+from icevision import tfms
+from icevision.models import model_from_checkpoint
+import easyocr as ocr  #OCR
+import editdistance
+
+
+st.sidebar.image("./logo.png")
+st.sidebar.header("ATK-OCR classification (AOC) Webapp.")
+def load_image(image_file):
+    img = Image.open(image_file)
+    return img
+
+
+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 = "./AOC_weight_97.4.pth"
+
+checkpoint_and_model = model_from_checkpoint(checkpoint_path, 
+    model_name='ross.efficientdet', 
+    backbone_name='tf_d2',
+    img_size=384, 
+    is_coco=False)
+
+model_type = checkpoint_and_model["model_type"]
+backbone = checkpoint_and_model["backbone"]
+class_map = checkpoint_and_model["class_map"]
+img_size = checkpoint_and_model["img_size"]
+#model_type, backbone, class_map, img_size
+
+model = checkpoint_and_model["model"]
+
+device=next(model.parameters()).device
+
+img_size = checkpoint_and_model["img_size"]
+valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
+
+def get_detection(img_path):
+ 
+  #Get_Idcard_detail(file_path=img_path)
+  img = Image.open(img_path)
+  img = ImageOps.exif_transpose(img) # fix image rotating
+  width, height = img.size # get img_input size
+  if (width == 1280) and (height == 1280):
+    pred_dict  = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=0.6)
+  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))
+    pred_dict  = model_type.end2end_detect(new_im, valid_tfms, model, class_map=class_map, detection_threshold=0.6)
+
+
+    
+    #st.write(new_im.size)
+
+  
+
+  try:
+    labels, acc = pred_dict['detection']['labels'][0], pred_dict['detection']['scores'][0]
+    acc = acc * 100
+    if labels == "Neg":
+      labels = "Negative"
+    elif labels == "Pos":
+      labels = "Positive"
+    st.success(f"Result : {labels} with {round(acc, 2)}% confidence.")
+  except IndexError:
+    st.error("Not found ATK image! ; try to take image again..")
+    labels = "None"
+    acc = 0
+
+def get_img_detection(img_path):
+   
+  #Get_Idcard_detail(file_path=img_path)
+  img = Image.open(img_path)
+  img = ImageOps.exif_transpose(img) # fix image rotating
+  width, height = img.size # get img_input size
+  if (width == 1280) and (height == 1280):
+    new_im = 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))
+  
+  pred_dict  = model_type.end2end_detect(new_im, valid_tfms, model, class_map=class_map, detection_threshold=0.6)
+
+
+  return pred_dict['img']
+
+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 =='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)")
+    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% เมื่อเทียบกับคัดกรองด้วยคน 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)')
+
+    
+     
+       
+elif choice == "Detection":
+    st.header(" Antigen test kit + Identification card detector.")
+    pages_name = ['ATK + Idcard Detect', 'ATK Detect', 'Idcard Detect']
+    page = st.radio('Select option mode :', pages_name)
+
+    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(get_img_detection(image))
+            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(get_img_detection(image))
+            with st.spinner("🤖 ATK Working... "):
+                t1 = time.perf_counter()
+                st.subheader("Process Completed!.....")
+                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')
+
+    st.caption("Made by Tanaanan .M")
+
+
+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 : [email protected]")