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img_test add & move
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@YongRokKim
YongRokKim committed Jan 4, 2024
1 parent 38ee5ba commit 29a97db
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Showing 16 changed files with 104 additions and 48 deletions.
13 changes: 4 additions & 9 deletions BP_Api_File/BP.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -79,14 +79,8 @@
"output_type": "stream",
"text": [
"Image already meets the minimum size requirements. No resizing needed.\n",
"<_io.BufferedReader name='/data/BP/BP_Api_File/food/test/test2.jpg'>\n",
"-------------- <class '_io.BufferedReader'>\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<_io.BufferedReader name='/data/BP/test2.jpg'>\n",
"-------------- <class '_io.BufferedReader'>\n",
"Code: 200\n",
"{'confidence': 0.6900922, 'food_name': '미소된장국', 'food_code': 'D000041', 'food_cal': 38.0, 'food_nat': 517.03, 'food_serving_size': 150.0, 'food_carbs': 2.8, 'food_protein': 2.8, 'food_fat': 1.8, 'food_sugar': 0.7, 'food_cholesterol': 0.0}\n",
"====================\n",
Expand Down Expand Up @@ -184,12 +178,13 @@
" return secrets[setting]\n",
" except KeyError:\n",
" error_msg = \"Set the {} environment variable\".format(setting)\n",
" print(\"error_msg\")\n",
" # raise ImproperlyConfigured(error_msg)\n",
" \n",
"client_id = get_secret(\"kt_client_id\")\n",
"client_secret = get_secret(\"kt_client_secret\")\n",
"\n",
"img_path = \"/data/BP/BP_Api_File/food/test/test2.jpg\"\n",
"img_path = \"/data/BP/test2.jpg\"\n",
"\n",
"# HMAC 기반 signature 생성\n",
"signature = hmac.new(\n",
Expand Down
19 changes: 14 additions & 5 deletions BP_Api_File/OCR_test.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,16 +2,25 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"b'{\"version\":\"V2\",\"requestId\":\"0f02780b-1265-4f3f-b3c6-6354a4741eab\",\"timestamp\":1704177019686,\"images\":[{\"receipt\":{\"meta\":{\"estimatedLanguage\":\"ko\"},\"result\":{\"storeInfo\":{\"name\":{\"text\":\"\\xeb\\xa5\\xb4\\xed\\x95\\x98\\xec\\x9e\\x84\\xec\\x8a\\xa4\\xed\\x84\\xb0\\xeb\\x94\\x94\\xec\\xb9\\xb4\\xed\\x8e\\x98\",\"formatted\":{\"value\":\"\\xeb\\xa5\\xb4\\xed\\x95\\x98\\xec\\x9e\\x84\\xec\\x8a\\xa4\\xed\\x84\\xb0\\xeb\\x94\\x94\\xec\\xb9\\xb4\\xed\\x8e\\x98\"},\"keyText\":\"\\xea\\xb0\\x80\\xeb\\xa7\\xb9\\xec\\xa0\\x90\\xeb\\xaa\\x85\",\"confidenceScore\":0.95034,\"boundingPolys\":[{\"vertices\":[{\"x\":466.0,\"y\":348.0},{\"x\":663.0,\"y\":351.0},{\"x\":662.0,\"y\":384.0},{\"x\":466.0,\"y\":381.0}]}],\"maskingPolys\":[]},\"subName\":{\"text\":\"\\xeb\\xb6\\x80\\xec\\x82\\xb0\\xeb\\xa7\\x9d\\xeb\\xaf\\xb8\\xec\\xa0\\x90\",\"keyText\":\"\",\"confidenceScore\":0.92417,\"boundingPolys\":[{\"vertices\":[{\"x\":669.0,\"y\":351.0},{\"x\":794.0,\"y\":351.0},{\"x\":794.0,\"y\":385.0},{\"x\":669.0,\"y\":385.0}]}],\"maskingPolys\":[]},\"addresses\":[{\"text\":\"\\xeb\\xb6\\x80\\xec\\x82\\xb0\\xea\\xb4\\x91\\xec\\x97\\xad\\xec\\x8b\\x9c \\xec\\x88\\x98\\xec\\x98\\x81\\xea\\xb5\\xac \\xea\\xb3\\xbc\\xec\\xa0\\x95\\xeb\\xa1\\x9c 63 \\xeb\\xa7\\x9d \\xeb\\xaf\\xb8\\xec\\x84\\xbc\\xed\\x85\\x80 4\\xec\\xb8\\xb5\",\"formatted\":{\"value\":\"\\xeb\\xb6\\x80\\xec\\x82\\xb0\\xea\\xb4\\x91\\xec\\x97\\xad\\xec\\x8b\\x9c \\xec\\x88\\x98\\xec\\x98\\x81\\xea\\xb5\\xac \\xea\\xb3\\xbc\\xec\\xa0\\x95\\xeb\\xa1\\x9c 63 \\xeb\\xa7\\x9d \\xeb\\xaf\\xb8\\xec\\x84\\xbc\\xed\\x85\\x80 4\\xec\\xb8\\xb5\"},\"keyText\":\"\\xec\\xa3\\xbc \\xec\\x86\\x8c:\",\"confidenceScore\":0.92462,\"boundingPolys\":[{\"vertices\":[{\"x\":466.0,\"y\":410.0},{\"x\":590.0,\"y\":410.0},{\"x\":590.0,\"y\":442.0},{\"x\":466.0,\"y\":442.0}]},{\"vertices\":[{\"x\":596.0,\"y\":410.0},{\"x\":672.0,\"y\":410.0},{\"x\":672.0,\"y\":442.0},{\"x\":596.0,\"y\":442.0}]},{\"vertices\":[{\"x\":676.0,\"y\":411.0},{\"x\":755.0,\"y\":411.0},{\"x\":755.0,\"y\":444.0},{\"x\":676.0,\"y\":444.0}]},{\"vertices\":[{\"x\":756.0,\"y\":414.0},{\"x\":790.0,\"y\":414.0},{\"x\":790.0,\"y\":439.0},{\"x\":756.0,\"y\":439.0}]},{\"vertices\":[{\"x\":795.0,\"y\":412.0},{\"x\":825.0,\"y\":412.0},{\"x\":825.0,\"y\":444.0},{\"x\":795.0,\"y\":444.0}]},{\"vertices\":[{\"x\":265.0,\"y\":439.0},{\"x\":344.0,\"y\":439.0},{\"x\":344.0,\"y\":469.0},{\"x\":265.0,\"y\":469.0}]},{\"vertices\":[{\"x\":345.0,\"y\":439.0},{\"x\":390.0,\"y\":439.0},{\"x\":390.0,\"y\":468.0},{\"x\":345.0,\"y\":468.0}]}],\"maskingPolys\":[]}],\"tel\":[{\"text\":\"01057018642\",\"formatted\":{\"value\":\"01057018642\"},\"keyText\":\"\\xec\\xa0\\x84\\xed\\x99\\x94\\xeb\\xb2\\x88\\xed\\x98\\xb8\",\"confidenceScore\":0.9302,\"boundingPolys\":[{\"vertices\":[{\"x\":465.0,\"y\":380.0},{\"x\":602.0,\"y\":382.0},{\"x\":601.0,\"y\":407.0},{\"x\":465.0,\"y\":405.0}]}]}]},\"paymentInfo\":{\"cardInfo\":{\"company\":{\"text\":\"\\xeb\\xa1\\xaf\\xeb\\x8d\\xb0\\xec\\xb9\\xb4\\xeb\\x93\\x9c\\xec\\x82\\xac\",\"formatted\":{\"value\":\"\\xeb\\xa1\\xaf\\xeb\\x8d\\xb0\\xec\\xb9\\xb4\\xeb\\x93\\x9c\\xec\\x82\\xac\"},\"keyText\":\"\\xec\\xb9\\xb4 \\xeb\\x93\\x9c \\xec\\x82\\xac\",\"confidenceScore\":0.90842,\"boundingPolys\":[{\"vertices\":[{\"x\":468.0,\"y\":524.0},{\"x\":588.0,\"y\":524.0},{\"x\":588.0,\"y\":551.0},{\"x\":468.0,\"y\":551.0}]}],\"maskingPolys\":[]},\"number\":{\"text\":\"5137920000000000****\",\"formatted\":{\"value\":\"5137920000000000****\"},\"keyText\":\"\\xec\\xb9\\xb4\\xeb\\x93\\x9c\\xeb\\xb2\\x88\\xed\\x98\\xb8:\",\"confidenceScore\":0.88703,\"boundingPolys\":[{\"vertices\":[{\"x\":465.0,\"y\":552.0},{\"x\":705.0,\"y\":552.0},{\"x\":705.0,\"y\":574.0},{\"x\":465.0,\"y\":574.0}]}],\"maskingPolys\":[]}}},\"subResults\":[{\"items\":[{\"name\":{\"text\":\"(\\xec\\xa0\\x95\\xec\\x95\\xa1\\xea\\xb6\\x8c) 50: 00\",\"formatted\":{\"value\":\"(\\xec\\xa0\\x95\\xec\\x95\\xa1\\xea\\xb6\\x8c)50: 00\"},\"keyText\":\"\",\"confidenceScore\":0.47716,\"boundingPolys\":[{\"vertices\":[{\"x\":305.0,\"y\":660.0},{\"x\":408.0,\"y\":660.0},{\"x\":408.0,\"y\":691.0},{\"x\":305.0,\"y\":691.0}]},{\"vertices\":[{\"x\":415.0,\"y\":662.0},{\"x\":452.0,\"y\":662.0},{\"x\":452.0,\"y\":688.0},{\"x\":415.0,\"y\":688.0}]},{\"vertices\":[{\"x\":451.0,\"y\":662.0},{\"x\":482.0,\"y\":662.0},{\"x\":482.0,\"y\":689.0},{\"x\":451.0,\"y\":689.0}]}],\"maskingPolys\":[]},\"price\":{\"price\":{\"text\":\"60,000\",\"formatted\":{\"value\":\"60000\"},\"keyText\":\"\",\"confidenceScore\":0.85618,\"boundingPolys\":[{\"vertices\":[{\"x\":582.0,\"y\":662.0},{\"x\":664.0,\"y\":662.0},{\"x\":664.0,\"y\":691.0},{\"x\":582.0,\"y\":691.0}]}]}}}]}],\"totalPrice\":{\"price\":{\"text\":\"60,000\",\"formatted\":{\"value\":\"60000\"},\"keyText\":\"\\xed\\x95\\xa9\\xea\\xb3\\x84\\xea\\xb8\\x88\\xec\\x95\\xa1\",\"confidenceScore\":0.9458,\"boundingPolys\":[{\"vertices\":[{\"x\":726.0,\"y\":722.0},{\"x\":810.0,\"y\":722.0},{\"x\":810.0,\"y\":750.0},{\"x\":726.0,\"y\":750.0}]}]}},\"subTotal\":[]}},\"uid\":\"3cd663a927e84bba9d0b73d3bec22155\",\"name\":\"demo\",\"inferResult\":\"SUCCESS\",\"message\":\"SUCCESS\",\"validationResult\":{\"result\":\"NO_REQUESTED\"}}]}'\n",
"\n",
"(정액권) 50: 00\n"
"b'{\"version\":\"V2\",\"requestId\":\"09d439ae-b453-44c9-b034-7f5a51e87a86\",\"timestamp\":1704347028917,\"images\":[{\"uid\":\"cf457daf3ff4414a9a7eef5e8663f0e1\",\"name\":\"demo\",\"inferResult\":\"ERROR\",\"message\":\"Recognition failure.\",\"validationResult\":{\"result\":\"NO_REQUESTED\"},\"convertedImageInfo\":{\"width\":1440,\"height\":1440,\"pageIndex\":0,\"longImage\":false}}]}'\n"
]
},
{
"ename": "KeyError",
"evalue": "'receipt'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 54\u001b[0m\n\u001b[1;32m 51\u001b[0m json\u001b[38;5;241m.\u001b[39mdump(result, make_file, indent\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, ensure_ascii\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 53\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 54\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m field \u001b[38;5;129;01min\u001b[39;00m result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimages\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreceipt\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msubResults\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mitems\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[1;32m 55\u001b[0m text \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39mfield[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28mprint\u001b[39m(text)\n",
"\u001b[0;31mKeyError\u001b[0m: 'receipt'"
]
}
],
Expand All @@ -37,7 +46,7 @@
"\n",
"api_url = get_secret('CLOVA_OCR_Invoke_URL')\n",
"secret_key = get_secret('naver_secret_key')\n",
"image_file = '/data/BP/BP_Api_File/not_food_img/not_food4.jpg'\n",
"image_file = '/data/BP/test2.jpg'\n",
"\n",
"request_json = {\n",
" 'images': [\n",
Expand Down
22 changes: 22 additions & 0 deletions BP_Api_File/result_notfood_2.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,22 @@
{
"version": "V2",
"requestId": "09d439ae-b453-44c9-b034-7f5a51e87a86",
"timestamp": 1704347028917,
"images": [
{
"uid": "cf457daf3ff4414a9a7eef5e8663f0e1",
"name": "demo",
"inferResult": "ERROR",
"message": "Recognition failure.",
"validationResult": {
"result": "NO_REQUESTED"
},
"convertedImageInfo": {
"width": 1440,
"height": 1440,
"pageIndex": 0,
"longImage": false
}
}
]
}
98 changes: 64 additions & 34 deletions Server/model_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,33 +32,56 @@ def img_resize(img):
# 이미지를 열고 크기 가져오기
with Image.open(io.BytesIO(img)) as img:
width, height = img.size
print("@hello14 :", width, height)

# 최소 크기 정의
minWidth = 1080 if width > height else 720
minHeight = 720 if width > height else 1080
# 최소 및 최대 크기 정의
minWidth, maxWidth = (720, 2560) if width > height else (720, 1440)
minHeight, maxHeight = (1080, 1440) if width > height else (1080, 2560)
print("@hello14 :", minWidth, minHeight, maxWidth, maxHeight)

# 이미지가 이미 최소 크기 요건을 충족하는지 확인
if width >= minWidth and height >= minHeight:
print("Image already meets the minimum size requirements. No resizing needed.")
return
if minWidth <= width <= maxWidth and minHeight <= height <= maxHeight:
print("Image meets size requirements. No resizing needed.")
img_byte_arr = io.BytesIO()
img.save(img_byte_arr, format='JPEG')
return img_byte_arr.getvalue()

# 새 크기 계산 및 비율 유지
if width / height > 1:
# 넓은 이미지
newHeight = max(height, minHeight)
newWidth = round(newHeight * (width / height))
if newWidth < minWidth:
newWidth = minWidth
newHeight = round(newWidth / (width / height))
else:
# 높은 이미지
newWidth = max(width, minWidth)
newHeight = round(newWidth / (width / height))
if newHeight < minHeight:
newHeight = minHeight
# 이미지 크기 조정
newWidth, newHeight = width, height
if width < minWidth or height < minHeight:
# 이미지 확대
if width / height > 1:
# 넓은 이미지
newHeight = max(height, minHeight)
newWidth = round(newHeight * (width / height))
if newWidth < minWidth:
newWidth = minWidth
newHeight = round(newWidth / (width / height))
else:
# 높은 이미지
newWidth = max(width, minWidth)
newHeight = round(newWidth / (width / height))
if newHeight < minHeight:
newHeight = minHeight
newWidth = round(newHeight * (width / height))
elif width > maxWidth or height > maxHeight:
# 이미지 축소
if width / height > 1:
# 넓은 이미지
newWidth = min(width, maxWidth)
newHeight = round(newWidth / (width / height))
if newHeight > maxHeight:
newHeight = maxHeight
newWidth = round(newHeight * (width / height))
else:
# 높은 이미지
newHeight = min(height, maxHeight)
newWidth = round(newHeight / (width / height))
if newWidth > maxWidth:
newWidth = maxWidth
newHeight = round(newWidth / (width / height))

# 이미지 크기 조정 및 저장
# 조정된 크기 출력 및 이미지 크기 조정
print("@hello15 : Resized Image Size:", newWidth, newHeight)
resized_img = img.resize((newWidth, newHeight))
img_byte_arr = io.BytesIO()
resized_img.save(img_byte_arr, format='JPEG')
Expand Down Expand Up @@ -104,9 +127,10 @@ def food_api(img,secret_file):

if response.ok:
json_data = json.loads(response.text)
code = json_data['code']
print("@hello13 :",json_data)
# code = json_data['code']
data = json_data['data']
print(f"Code: {code}")
# print(f"Code: {code}")
return data
#.json 형식 출력
# print(f"Data: {data}")
Expand Down Expand Up @@ -194,22 +218,24 @@ def predict():
"foodNames": [],
# kt predict food info
"ktFoodsInfo": {}
}
},
# "image":[file]
}

# API키 연결 .json 파일
# =====================================================
secret_file = "secrets.json"
# ====================================================
# print("@hello2")
print("@hello2")
try:
# print("@helloimg",type(img))
print("@helloimg",type(img))
resized_img = img_resize(img)
ocr_result = OCR_api(img, secret_file)
# print("@hello3")
print("@hello10 :",ocr_result)
print("@hello3")
if ocr_result['images'][0]['inferResult'] == 'ERROR':
# print("@hello6",ocr_result['images'][0]['inferResult'] )
resized_img = img_resize(img)
# print('@hello7')
print("@hello6",ocr_result['images'][0]['inferResult'] )
print('@hello7')
food_result = food_api(resized_img, secret_file)
od_result = get_prediction(img)
print('+++'*20)
Expand All @@ -223,13 +249,17 @@ def predict():
# print(food_result)
# print("="*20)
for region_num in food_result[0]:
if food_result[0][region_num]['prediction_top1']['confidence'] >=0.5:
# res['predict']['ktFoodsInfo'][region_num] = food_result[0][region_num]['prediction_top1']
# res['predict']['foodNames'].append(food_result[0][region_num]['prediction_top1']["food_name"])
if food_result[0][region_num]['prediction_top1']['confidence'] >=0.3:
res['predict']['ktFoodsInfo'][region_num] = food_result[0][region_num]['prediction_top1']
res['predict']['foodNames'].append(food_result[0][region_num]['prediction_top1']["food_name"])

for item in od_result:
if od_result[item]['Food_name'] not in res['predict']['foodNames'] and od_result[item]['highest_confidence'] >=0.3:
res['predict']['foodNames'].append(od_result[item]['Food_name'])
# res['predict']['foodNames'].append(od_result[item]['Food_name'])
if od_result[item]['highest_confidence'] >=0.5:
if od_result[item]['Food_name'] not in res['predict']['foodNames']:
res['predict']['foodNames'].append(od_result[item]['Food_name'])
else:
# print('@hello4')
if ocr_result['images'][0]['receipt']['result']['subResults']:
Expand Down
0 Img_069_0005.jpg → img_file/Img_069_0005.jpg
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0 images (4).jpg → img_file/images (4).jpg
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0 test2.jpg → img_file/test2.jpg
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