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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
try:
from vlmeval.config import supported_VLM
except Exception: # only needed for local ("image") models
supported_VLM = None
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
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
from util.concate_image import concatenate_image
from PIL import Image
from io import BytesIO
import tempfile
import argparse
import os
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 concate_image_to_video(images, img_path):
"""
Appends an external image (from img_path) to the list of PIL images.
"""
extra_img = Image.open(img_path).convert("RGB") # open and ensure RGB
images.append(extra_img)
return images
def generate_input_images_memory(mp4_path, num_frames=SAMPLE_FRAMES):
"""
Extracts `num_frames` frames from a video and returns them as PIL.Image objects 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)
images = []
for i in frame_indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, i)
success, frame = cap.read()
if not success:
continue
# Convert OpenCV BGR image to RGB, then to PIL.Image
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(rgb)
images.append(image)
cap.release()
return images
def evaluate_model_video(category_name, model_name, item):
fix_seed()
item_copy = item.copy()
question = item.get("question", "")
options = item.get("options", [])
video = item.get("video", None)
for format in ["direct"]:
question_prompt = generate_question_prompt(format, category_name, question, options, model_category="image")
print(f"{Fore.YELLOW}Evaluating item{Style.RESET_ALL}")
print(f"{Fore.BLUE}Question Prompt: {question_prompt}{Style.RESET_ALL}")
# print the input question prompt
random_number = np.random.randint(0, 9999)
input_images_open = generate_input_images_memory(video, SAMPLE_FRAMES)
# conbine the input_images
# debug this function
combined_image = concatenate_image(input_images_open, rows=2, columns=8)
# Use tempfile to avoid collisions between process
with tempfile.NamedTemporaryFile(suffix=".jpg", prefix="temp_combined_image_", delete=False) as tmp_file:
temp_image_path = tmp_file.name
combined_image.save(temp_image_path, format="JPEG")
print(f"{Fore.GREEN}Combined image saved as {temp_image_path}{Style.RESET_ALL}")
response = supported_VLM[model_name]().generate([temp_image_path, question_prompt])
# Optional: clean up after inference
Path(temp_image_path).unlink(missing_ok=True)
print(f"{Fore.CYAN}Response is: {response}{Style.RESET_ALL}")
item_copy[f"{format}_reply"] = response
return item_copy
def evaluate_model_interleaved(category_name, model_name, item):
fix_seed()
# load input_json file
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"]:
question_prompt = generate_question_prompt(format, category_name, question, options, model_category="image")
print(f"{Fore.YELLOW}Evaluating item: {Style.RESET_ALL}")
print(f"{Fore.BLUE}Question Prompt: {question_prompt}{Style.RESET_ALL}")
# print the input question prompt
random_number = np.random.randint(0, 9999)
input_images_open = generate_input_images_memory(video, SAMPLE_FRAMES)
# conbine the input_images
input_images_open_new = concate_image_to_video(input_images_open, image)
# debug this function
combined_image = concatenate_image(input_images_open_new, rows=5, columns=7)
# Use tempfile to avoid collisions between process
with tempfile.NamedTemporaryFile(suffix=".jpg", prefix="temp_combined_image_", delete=False) as tmp_file:
temp_image_path = tmp_file.name
combined_image.save(temp_image_path, format="JPEG")
print(f"{Fore.GREEN}Combined image saved as {temp_image_path}{Style.RESET_ALL}")
response = supported_VLM[model_name]().generate([temp_image_path, question_prompt])
# Optional: clean up after inference
Path(temp_image_path).unlink(missing_ok=True)
print(f"{Fore.YELLOW}Response for item: {response}{Style.RESET_ALL}")
item_copy[f"{format}_reply"] = response
return item_copy
def evaluate_model_image(category_name, model_name, item):
fix_seed()
item_copy = item.copy()
question = item.get("question", "")
options = item.get("options", [])
image = item.get("image", None)
for format in ["direct"]:
question_prompt = generate_question_prompt(format, category_name, question, options, model_category="image")
print(f"{Fore.YELLOW}Evaluating item: {Style.RESET_ALL}")
# print the input question prompt
# if failure, try a few more attempts
response = "error"
for attempt in range(3):
try:
response = supported_VLM[model_name]().generate([image, question_prompt])
break
except:
print(f"{Fore.RED}Error generating response for item, attempt {attempt + 1}. Retrying...{Style.RESET_ALL}")
time.sleep(2 ** attempt)
continue # retry the same item
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 VLM model on video QA tasks.")
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=False, default="image", help="Unused for local image models; kept for CLI symmetry.")
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
print(f"{Fore.GREEN}Evaluating model: {model_name} on category: {category_name}{Style.RESET_ALL}")
evaluate_output_path =f"final_{model_name}_evaluate_{evaluate_category}.json"
tmp_dir = "tmp"
os.makedirs(tmp_dir, exist_ok=True)
tmp_path = os.path.join(tmp_dir, evaluate_output_path)
# Load input
with open(input_json_path, "r", encoding="utf-8") as f:
input_data = json.load(f)
# === RESUME LOGIC ===
evaluate_output = []
processed_ids = set()
if os.path.exists(tmp_path):
try:
with open(tmp_path, "r", encoding="utf-8") as f:
evaluate_output = json.load(f)
# Build fast lookup of processed items: prefer explicit idx, else ordinal
for pos, item in enumerate(evaluate_output):
key = item.get("idx", pos)
processed_ids.add(key)
print(f"{Fore.YELLOW}[Resume] Loaded {len(evaluate_output)} partial results from {tmp_path}.{Style.RESET_ALL}")
except Exception as e:
print(f"{Fore.RED}[Resume] Could not load {tmp_path}: {e}. Starting fresh.{Style.RESET_ALL}")
evaluate_output = []
processed_ids = set()
# Iterate and skip already processed
for pos, item in enumerate(input_data):
key = item.get("idx", pos)
if key in processed_ids:
continue # already done
subcat = item.get("category", "")
if subcat in ["pointing", "trajectory", "bbox"]:
output_item = evaluate_model_image(subcat, model_name, item)
elif subcat in ["path planning", "relative direction"]:
output_item = evaluate_model_interleaved(subcat, model_name, item)
elif subcat in ["object localization", "next action prediction", "task progress reasoning"]:
output_item = evaluate_model_video(subcat, model_name, item)
elif item.get("video"):
output_item = evaluate_model_video(subcat, model_name, item)
elif item.get("image"):
output_item = evaluate_model_image(subcat, model_name, item)
else:
output_item = item
evaluate_output.append(output_item)
processed_ids.add(key)
# Save at tmp
with open(tmp_path, "w", encoding="utf-8") as f:
json.dump(evaluate_output, f, indent=4, ensure_ascii=False)
# Write final output
with open(evaluate_output_path, "w", encoding="utf-8") as f:
json.dump(evaluate_output, f, indent=4, ensure_ascii=False)
print(f"{Fore.CYAN} Done. Saved final results to: {evaluate_output_path}{Style.RESET_ALL}")
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