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import base64
import requests
import json
import numpy as np
import re
import argparse
# TODO : OpenAI API Key
api_key = "YOUR_API_KEY"
# TODO : Path to your image
image_path_1 = "FIRST_IMAGE_PATH.png"
image_path_2 = "SECOND_IMAGE_PATH.png"
# TODO : User preference Text
user_preference = "USER_PREFERNCE_TEXT"
# Function to encode the image
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
example_json ="""
{
"realism_and_3d_geometric_consistency": {
"grade": 8,
"comment": "The renders appear to have appropriate 3D geometry and lighting that is fairly consistent with real-world expectations. The proportions and perspective look realistic."
},
"functionality_and_activity_based_alignment": {
"grade": 7,
"comment": "The room includes a workspace, sleeping area, and living area as per the user preference. The L-shaped couch facing the bed partially meets the requirement for watching TV comfortably. However, there does not appear to be a TV depicted in the render, so it's not entirely clear if the functionality for TV watching is fully supported."
},
"layout_and_furniture": {
"grade": 7,
"comment": "The room has a bed that’s not centered and with space at the foot, and a large desk with a chair. However, it's unclear if the height of the bed meets the user's preference, and the layout does not clearly show the full-length mirror in relation to the wardrobe, so its placement in accordance to user preferences is uncertain."
},
"color_scheme_and_material_choices": {
"grade": 9,
"comment": "The room adheres to a light color scheme with blue and white tones as preferred by the user, without a nautical feel. The bed and other furniture choices are aligned with the color scheme specified."
},
"overall_aesthetic_and_atmosphere": {
"grade": 8,
"comment": "The room's general aesthetic is bright, clean, and relatively minimalistic, which could align with the user's preference for a light color scheme and a modern look. The chandelier is present as opposed to bright, hospital-like lighting."
}
}
"""
# Getting the base64 string
base64_image_1 = encode_image(image_path_1)
base64_image_2 = encode_image(image_path_2)
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}"
}
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": f"""
Give a grade from 1 to 10 or unknown to the following room renders based on how well they correspond together to the user preference (in triple backquotes) in the following aspects:
- Realism and 3D Geometric Consistency
- Functionality and Activity-based Alignment
- Layout and furniture
- Color Scheme and Material Choices
- Overall Aesthetic and Atmosphere
User Preference:
```{user_preference}```
Return the results in the following JSON format:
```json
{example_json}
```
"""
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image_1}"
}
},
{
"type": "image_url",
"image_url": {
"url" : f"data:image/jpeg;base64,{base64_image_2}"
}
}
]
}
],
"max_tokens": 1024
}
grades = {
"realism_and_3d_geometric_consistency": [],
"functionality_and_activity_based_alignment": [],
"layout_and_furniture": [],
"color_scheme_and_material_choices": [],
"overall_aesthetic_and_atmosphere": []
}
for _ in range(3):
response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
grading_str = response.json()["choices"][0]["message"]["content"]
print(grading_str)
print("-" * 50)
pattern = r'```json(.*?)```'
matches = re.findall(pattern, grading_str, re.DOTALL)
json_content = matches[0].strip() if matches else None
if json_content is None:
grading = json.loads(grading_str)
else:
grading = json.loads(json_content)
for key in grades:
grades[key].append(grading[key]["grade"])
#Save the mean and std of the grades
for key in grades:
grades[key] = {"mean": round(sum(grades[key])/len(grades[key]), 2), "std": round(np.std(grades[key]), 2)}
#Save the grades
with open(f"{'_'.join(image_path_1.split('_')[:-1])}_grades.json", "w") as f:
json.dump(grades, f)
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