File size: 5,107 Bytes
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from PIL import Image
from google import genai
from google.genai import types
from pydantic import BaseModel
import json
from tqdm import tqdm
# ----------------------------------------------------------------------
# Optional: A structured schema for obstacle analysis
# If you don't want JSON output, remove the config section below.
# ----------------------------------------------------------------------
class ObstacleAnswer(BaseModel):
image_name: str
q1: str
# q2: str
# ----------------------------------------------------------------------
# Prompt used for Gemini 3 Pro (same 8 questions you used before)
# ----------------------------------------------------------------------
QUESTIONS = """
Answer the following question about obstacles in the image:
1. Identify the obstacle on the sidewalk or walkable path ahead.
"""
# 2. Identify the single object most likely to be hit by a pedestrian moving straight ahead, and assign it a formal Out-of-Place Score (0–100) based only on its position.
# Scoring scale:
# 0 = perfectly expected position (default/home location)
# 50 = somewhat out of place from where it is typically expected
# 100 = completely out of place and highly surprising
SYSTEM_MESSAGE = (
"I am fully blind. You are a mobility assistant who analyzes the scene "
"and describes obstacles for safe navigation. Be concise and accurate."
)
# ----------------------------------------------------------------------
# Gemini 3 Pro client setup
# ----------------------------------------------------------------------
GEMINI_API_KEY = "AIzaSyCjz1zbRQ_57ovEBPN2rlbfPYm2qVOEiuY"
client = genai.Client(api_key=GEMINI_API_KEY)
MODEL_ID = "gemini-3-pro-preview"
# ----------------------------------------------------------------------
# Function to analyze one image
# ----------------------------------------------------------------------
def analyze_image_with_gemini(img_path: str, structured: bool = False):
# Load and shrink image (Gemini requirement)
image = Image.open(img_path)
image.thumbnail([512, 512])
# Prepare message contents
contents = [
SYSTEM_MESSAGE,
image,
QUESTIONS,
]
# If structured JSON output is requested
if structured:
response = client.models.generate_content(
model=MODEL_ID,
contents=contents,
config=types.GenerateContentConfig(
response_mime_type="application/json",
response_schema=ObstacleAnswer,
),
)
else:
response = client.models.generate_content(
model=MODEL_ID,
contents=contents
)
return response
# ----------------------------------------------------------------------
# Example: process a folder of images
# ----------------------------------------------------------------------
def process_folder(image_dir, output_txt, structured=False):
with open(output_txt, "a", encoding="utf-8") as f_out:
# first_flag=True
for fname in tqdm(sorted(os.listdir(image_dir))):
# if first_flag:
# first_flag=False
# continue
if not fname.lower().endswith((".png", ".jpg", ".jpeg", ".heic")):
continue
# if fname not in ["WheelieBin_Set2_Pos5_OOPS0.png"]:
# continue
img_path = os.path.join(image_dir, fname)
print(f"Processing: {img_path}")
try:
response = analyze_image_with_gemini(img_path, structured=structured)
if structured:
# Parse JSON → update image_name → write JSONL
try:
data = json.loads(response.text)
data["image_name"] = fname # <-- overwrite with actual filename
f_out.write(json.dumps(data) + "\n")
except Exception as parse_err:
print(f"JSON parse error for {fname}: {parse_err}")
f_out.write(json.dumps({
"image_name": fname,
"error": "JSON parse error",
"raw_response": response.text
}) + "\n")
else:
# Raw text response
f_out.write(f"IMAGE: {img_path}\n")
f_out.write(response.text.strip() + "\n\n")
f_out.flush()
except Exception as e:
print(f"Error processing {img_path}: {e}")
f_out.write(f"IMAGE: {img_path}\nERROR: {e}\n\n")
# exit()
# ----------------------------------------------------------------------
# Example usage
# ----------------------------------------------------------------------
if __name__ == "__main__":
process_folder(
image_dir="/scratch/ds5725/OOPS/images",
output_txt="gemini_results_fix_q1.jsonl",
structured=True # Set True if you want JSON following ObstacleAnswer schema
)
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