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ab9dacf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 | import os, sys
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import json
import re
import base64
import numpy as np
import torch
import time
try:
import colorama
except Exception:
colorama = None
from util.prompt_generation import generate_question_prompt
import argparse
try:
from colorama import Fore, Style, init
except Exception: # colorama is optional (cosmetic terminal colors)
class _NoColor:
def __getattr__(self, _):
return ""
Fore = Style = _NoColor()
def init(*a, **k):
return None
import cv2
from pathlib import Path
import tempfile
import os
from util.concate_image import concatenate_image
from PIL import Image
import io
SAMPLE_FRAMES = 16
def fix_seed():
import random
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(42)
def generate_input_images_base64(mp4_path, num_frames=16):
"""
Extracts `num_frames` frames from the video and returns base64-encoded images in memory.
"""
cap = cv2.VideoCapture(mp4_path)
if not cap.isOpened():
raise ValueError(f"Cannot open video file: {mp4_path}")
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_indices = np.linspace(0, total_frames - 1, num=num_frames, dtype=int)
results = []
for i in frame_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, i)
success, frame = cap.read()
if not success:
continue
_, buffer = cv2.imencode('.jpg', frame) # encode frame to PNG format in memory
base64_image = base64.b64encode(buffer).decode('utf-8')
results.append({"image_base64": base64_image})
cap.release()
return results
# return as a list:
# [{"image": "path/to/image1.jpg"}, {"image": "path/to/image2.jpg"}, ...]
return [{"image": str(output_dir / f"{i:04d}.jpg")} for i in range(saved_count)]
def evaluate_model_image(category_name, item, model_name, model_series):
fix_seed()
# only look for the correct category
item_copy = item.copy()
question = item.get("question", "")
options = item.get("options", [])
image = item.get("image", None)
for format in ["direct", "cot"]:
question_prompt = generate_question_prompt(format, category_name, question, options, model_category="general")
print(f"Evaluating item {item.get('idx', item.get('id', '?'))}")
input_content = [{"image": image}, {"text":question_prompt}]
if model_series == "gemini":
from util.gemini import generate_content
response = generate_content(model_name, input_content)
elif model_series == "gpt":
from util.gpt import generate_content
response = generate_content(model_name, input_content)
elif model_series == "claude":
from util.claude import generate_content
response = generate_content(model_name, input_content)
item_copy[f"{format}_reply"] = response
return item_copy
def evaluate_model_interleaved(category_name, item, model_name, model_series):
# only look for the correct category
item_copy = item.copy()
question = item.get("question", "")
options = item.get("options", [])
video = item.get("video", None)
image = item.get("image", None)
for format in ["direct", "cot"]:
(first_prompt, second_prompt, third_prompt) = generate_question_prompt(format, category_name, question, options, model_category="general")
print(f"Evaluating item {item.get('idx', item.get('id', '?'))}")
# randomly generate a number to save the video images
random_number = np.random.randint(1000, 9999)
video_images = generate_input_images_base64(video, SAMPLE_FRAMES)
input_content = [{"text": first_prompt}] + video_images + [{"text": second_prompt}] + [{"image": image}] + [{"text": third_prompt}]
if model_series == "gemini":
from util.gemini import generate_content
response = generate_content(model_name, input_content)
elif model_series == "gpt":
from util.gpt import generate_content
response = generate_content(model_name, input_content)
elif model_series == "claude":
from util.claude import generate_content
response = generate_content(model_name, input_content)
item_copy[f"{format}_reply"] = response
return item_copy
def evaluate_model_video(category_name, item, model_name, model_series):
item_copy = item.copy()
question = item.get("question", "")
options = item.get("options", [])
video = item.get("video", None)
for format in ["direct", "cot"]:
(first_prompt, second_prompt) = generate_question_prompt(format, category_name, question, options, model_category="general")
print(f"{Fore.YELLOW} Evaluating item {item.get('idx', item.get('id', '?'))}{Style.RESET_ALL}")
# randomly generate a number to save the video images
random_number = np.random.randint(1000, 9999)
input_images = generate_input_images_base64(video, SAMPLE_FRAMES)
input_content = [{"text": first_prompt}] + input_images + [{"text": second_prompt}]
if model_series == "gemini":
from util.gemini import generate_content
response = generate_content(model_name, input_content)
elif model_series == "gpt":
from util.gpt import generate_content
response = generate_content(model_name, input_content)
elif model_series == "claude":
from util.claude import generate_content
response = generate_content(model_name, input_content)
item_copy[f"{format}_reply"] = response
return item_copy
if __name__ == "__main__":
# only evaluate model within the certain category in the input json file
parser = argparse.ArgumentParser(description="Evaluate model on a specific category from a JSON file.")
parser.add_argument("--model_name", type=str, required=True, help="The specific model name (e.g., claude-sonnet-4-20250514)")
parser.add_argument("--input_json_path", type=str, required=True, help="Path to the input JSON file.")
parser.add_argument("--category_name", type=str, required=False, default="", help="Optional. Routing is auto-detected from each item's category.")
parser.add_argument("--model_series", type=str, required=True, choices=["gemini", "gpt", "claude"], help="Model family/series.")
parser.add_argument("--output_path", type=str, default=None, help="Path to save evaluation results. Default is auto-generated.")
parser.add_argument("--evaluate_output_category", type=str, default=None, help="Path to save evaluation results. Default is auto-generated.")
args = parser.parse_args()
model_name = args.model_name
input_json_path = args.input_json_path
category_name = args.category_name
model_series = args.model_series
evaluate_category = args.evaluate_output_category
if not evaluate_category:
evaluate_category = os.path.splitext(os.path.basename(input_json_path))[0]
evaluate_output_path = f"final_{model_name}_evaluate_{evaluate_category}.json"
evaluate_output = []
# open the input json file
with open(input_json_path, 'r') as f:
input_data = json.load(f)
# evaluate each entry in input_data
for idx, item in enumerate(input_data):
category = item.get("category", "")
if category in ["pointing", "trajectory", "bbox"]:
output_item = evaluate_model_image(category, item, model_name, model_series)
elif category in ["path planning", "relative direction"]:
output_item = evaluate_model_interleaved(category, item, model_name, model_series)
elif category in ["object localization", "next action prediction", "task progress reasoning"]:
output_item = evaluate_model_video(category, item, model_name, model_series)
elif item.get("video"):
output_item = evaluate_model_video(category, item, model_name, model_series)
elif item.get("image"):
output_item = evaluate_model_image(category, item, model_name, model_series)
else:
output_item = item.copy()
evaluate_output.append(output_item)
# save the output to a json file
with open(evaluate_output_path, 'w') as f:
json.dump(evaluate_output, f, indent=4) |