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import numpy as np
import torch
import math
import easyocr
import cv2
import os
import base64
import json
import requests
from llama_cpp import Llama
from PIL import Image
from dotenv import load_dotenv
from utils import *
load_dotenv()
SCALE_FACTOR = 4
MAX_SIZE = 5_000_000
MAX_SIDE = 8_000
# ENGINE = ['easyocr']
# ENGINE = ['anthropic', 'claude-3-5-sonnet-20240620']
ENGINE = ['llama_cpp/v2/vision', 'qwen-vl-next_b2583']
def main() -> None:
model_weights = torch.load(relative_path('vision_model.pt'))
model = NeuralNet()
model.load_state_dict(model_weights)
model.to(DEVICE)
model.eval()
with torch.no_grad():
file_path = input('Enter file path: ')
with Image.open(file_path) as image:
image_size = image.size
image = image.resize(IMAGE_SIZE, Image.Resampling.LANCZOS)
image = TRANSFORM(image).to(DEVICE)
output = model(image).tolist()[0]
data = {
'top': {
'left': {
'x': output[0] * image_size[0],
'y': output[1] * image_size[1],
},
'right': {
'x': output[2] * image_size[0],
'y': output[3] * image_size[1],
},
},
'bottom': {
'left': {
'x': output[4] * image_size[0],
'y': output[5] * image_size[1],
},
'right': {
'x': output[6] * image_size[0],
'y': output[7] * image_size[1],
},
},
'curvature': {
'top': {
'x': output[8] * image_size[0],
'y': output[9] * image_size[1],
},
'bottom': {
'x': output[10] * image_size[0],
'y': output[11] * image_size[1],
},
},
}
print(f"{data=}")
image = cv2.imread(file_path)
size_x = ((data['top']['right']['x'] - data['top']['left']['x']) +
(data['bottom']['right']['x'] - data['bottom']['left']['x'])) / 2
size_y = ((data['top']['right']['y'] - data['top']['left']['y']) +
(data['bottom']['right']['y'] - data['bottom']['left']['y'])) / 2
margin_x = size_x * MARGIN
margin_y = size_y * MARGIN
points = np.array([
(max(data['top']['left']['x'] - margin_x, 0),
max(data['top']['left']['y'] - margin_y, 0)),
(min(data['top']['right']['x'] + margin_x, image_size[0]),
max(data['top']['right']['y'] - margin_y, 0)),
(min(data['bottom']['right']['x'] + margin_x, image_size[0]),
min(data['bottom']['right']['y'] + margin_y, image_size[1])),
(max(data['bottom']['left']['x'] - margin_x, 0),
min(data['bottom']['left']['y'] + margin_y, image_size[1])),
(data['curvature']['top']['x'],
max(data['curvature']['top']['y'] - margin_y, 0)),
(data['curvature']['bottom']['x'],
min(data['curvature']['bottom']['y'] + margin_y, image_size[1])),
], dtype=np.float32)
points_float: list[list[float]] = points.tolist()
max_height = int(max([ # y: top left - bottom left, top right - bottom right, curvature top - curvature bottom
abs(points_float[0][1] - points_float[3][1]),
abs(points_float[1][1] - points_float[4][1]),
abs(points_float[2][1] - points_float[5][1]),
])) * SCALE_FACTOR
max_width = int(max([ # x: top left - top right, bottom left - bottom right
abs(points_float[0][0] - points_float[1][0]),
abs(points_float[3][0] - points_float[2][0]),
])) * SCALE_FACTOR
destination_points = np.array([
[0, 0],
[max_width - 1, 0],
[max_width - 1, max_height - 1],
[0, max_height - 1],
[max_width // 2, 0],
[max_width // 2, max_height - 1],
], dtype=np.float32)
homography, _ = cv2.findHomography(points, destination_points)
warped_image = cv2.warpPerspective(image, homography, (max_width, max_height))
cv2.imwrite('_warped_image.png', warped_image)
del data
if ENGINE[0] == 'easyocr':
reader = easyocr.Reader(['de', 'fr', 'en'], gpu=True)
result = reader.readtext('_warped_image.png')
os.remove('_warped_image.png')
text = '\n'.join([r[1] for r in result])
ingredients = {}
elif ENGINE[0] == 'anthropic':
decrease_size('_warped_image.png', '_warped_image.webp', MAX_SIZE, MAX_SIDE)
os.remove('_warped_image.png')
with open('_warped_image.webp', 'rb') as f:
base64_image = base64.b64encode(f.read()).decode('utf-8')
response = requests.post(
url='https://api.anthropic.com/v1/messages',
headers={
'x-api-key': os.environ['ANTHROPIC_API_KEY'],
'anthropic-version': '2023-06-01',
'content-type': 'application/json',
},
data=json.dumps({
'model': ENGINE[1],
'max_tokens': 1024,
'messages': [
{
'role': 'user', 'content': [
{
'type': 'image',
'source': {
'type': 'base64',
'media_type': 'image/webp',
'data': base64_image,
},
},
{
'type': 'text',
'text': PROMPT_CLAUDE,
},
],
},
],
}),
)
os.remove('_warped_image.webp')
try:
data = response.json()
ingredients = json.loads('{' + data['content'][0]['text'].split('{', 1)[-1].rsplit('}', 1)[0] + '}')
except Exception as e:
print(data)
raise e
text = ''
elif ENGINE[0] == 'llama_cpp/v2/vision':
decrease_size('_warped_image.png', '_warped_image.webp', MAX_SIZE, MAX_SIDE)
# os.remove('_warped_image.png')
response = requests.post(
url='http://127.0.0.1:11434/llama_cpp/v2/vision',
headers={
'x-version': '2024-05-21',
'content-type': 'application/json',
},
data=json.dumps({
'task': PROMPT_VISION,
'model': ENGINE[1],
'image_path': relative_path('_warped_image.webp'),
}),
)
os.remove('_warped_image.webp')
text: str = response.json()['text']
ingredients = {}
else:
raise ValueError(f'Unknown engine: {ENGINE[0]}')
if text != '':
if DEVICE == 'cuda':
n_gpu_layers = -1
else:
n_gpu_layers = 0
llm = Llama(
model_path=relative_path('llm.Q4_K_M.gguf'),
n_gpu_layers=n_gpu_layers,
)
llm_result = llm.create_chat_completion(
messages=[
{
'role': 'system',
'content': SYSTEM_PROMPT,
},
{
'role': 'user',
'content': PROMPT_LLM.replace('{{old_data}}', text),
},
],
max_tokens=1024,
temperature=0,
# grammar=GRAMMAR,
)
try:
ingredients = json.loads(
'{' + llm_result['choices'][0]['message']['content'].split('{', 1)[-1].rsplit('}', 1)[0] + '}')
except Exception as e:
print(f"{llm_result=}")
raise e
animal_ingredients = [item for item in ingredients['Zutaten'] if item in ANIMAL]
sometimes_animal_ingredients = [item for item in ingredients['Zutaten'] if item in SOMETIMES_ANIMAL]
milk_ingredients = ([item for item in ingredients['Zutaten'] if item in MILK]
+ [item for item in ingredients['Verunreinigungen'] if item in MILK])
gluten_ingredients = ([item for item in ingredients['Zutaten'] if item in GLUTEN]
+ [item for item in ingredients['Verunreinigungen'] if item in GLUTEN])
print('=' * 64)
print('Zutaten: ' + ', '.join(ingredients['Zutaten']))
print('=' * 64)
print(('Kann Spuren von ' + ', '.join(ingredients['Verunreinigungen']) + ' enthalten.')
if len(ingredients['Verunreinigungen']) > 0 else 'ohne Verunreinigungen')
print('=' * 64)
print('Gefundene tierische Zutaten: '
+ (', '.join(animal_ingredients) if len(animal_ingredients) > 0 else 'keine'))
print('=' * 64)
print('Gefundene potenzielle tierische Zutaten: '
+ (', '.join(sometimes_animal_ingredients) if len(sometimes_animal_ingredients) > 0 else 'keine'))
print('=' * 64)
print('Gefundene Milchprodukte: ' + (', '.join(milk_ingredients) if len(milk_ingredients) > 0 else 'keine'))
print('=' * 64)
print('Gefundene Gluten: ' + (', '.join(gluten_ingredients) if len(gluten_ingredients) > 0 else 'keine'))
print('=' * 64)
print(LEGAL_NOTICE)
print('=' * 64)
if __name__ == '__main__':
main()
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