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# Copyright (c) Kangan Qian. All rights reserved.
# Authors: Kangan Qian (Tsinghua University, Xiaomi Corporation)
# Description: Function agent for autonomous driving visual processing
from pathlib import Path
import pickle
import numpy as np
import math
import matplotlib.pyplot as plt
from PIL import Image
from skimage.draw import polygon
from third_party.yoloworld_demo import get_2dbox_open_vocabulary_detector
from third_party.depth_demo import get_3d_location
class FuncAgent:
def __init__(self, data_dict=None, json_data_dict=None) -> None:
"""
Initialize function agent for visual processing tasks
Args:
data_dict: Dictionary containing scene data
json_data_dict: Dictionary containing JSON metadata
"""
self.data = data_dict
self.json_data_dict = json_data_dict
self.short_trajectory_description = False
# Define available visual functions
self.visual_func_infos = [
get_open_world_vocabulary_detection_info,
get_3d_loc_in_cam_info,
resize_image_info,
crop_image_info,
]
def get_open_world_vocabulary_detection(self, object_names: list, cam_type: str):
"""
Detect objects in an image using open vocabulary detection
Args:
object_names: List of objects to detect
cam_type: Camera type to process
Returns:
Tuple of prompts and detected bounding boxes
"""
cam_path_info_list = self.json_data_dict['image']
for cam_path_info in cam_path_info_list:
if cam_type == cam_path_info.split('/')[1]:
cur_cam_type_index = cam_path_info_list.index(cam_path_info)
choosed_image_path = cam_path_info_list[cur_cam_type_index]
prompts, detected_2d_boxs = get_2dbox_open_vocabulary_detector(
text=object_names,
image_path=choosed_image_path
)
return prompts, detected_2d_boxs
def get_open_world_vocabulary_detection_info(self, object_names: list, image_path: str):
"""
Detect objects in an image using open vocabulary detection
Args:
object_names: List of objects to detect
image_path: Path to the image file
Returns:
Tuple of prompts and detected bounding boxes
"""
prompts, detected_2d_boxs = get_2dbox_open_vocabulary_detector(
text=object_names,
image_path=image_path
)
return prompts, detected_2d_boxs
def get_3d_loc_in_cam_info(self, object_names: list, image_path: str):
"""
Get 3D locations of objects in camera coordinates
Args:
object_names: List of objects to locate
image_path: Path to the image file
Returns:
Tuple of prompts and 3D locations
"""
prompts, detected_loc_3d = get_3d_location(
text=object_names,
image_path=image_path
)
return prompts, detected_loc_3d
def get_ego_states(self):
"""Get ego vehicle state information"""
return get_ego_prompts(self.data)
# Image processing functions and their metadata
resize_image_info = {
"name": "resize_image",
"description": "Resizes an image to specified dimensions with interpolation support",
"parameters": {
"type": "object",
"properties": {
"input_path": {"type": "string", "description": "Input image file path"},
"output_path": {"type": "string", "description": "Output path for resized image"},
"target_size": {
"type": "array",
"items": {"type": "integer"},
"minItems": 2,
"maxItems": 2,
"description": "Target dimensions [width, height]"
},
"interpolation": {
"type": "integer",
"description": "Interpolation method (e.g., Image.BILINEAR for bilinear interpolation)"
}
},
"required": ["input_path", "output_path", "target_size"]
}
}
def resize_image(input_path, output_path, target_size, interpolation=Image.BILINEAR):
"""
Resize an image to specified dimensions
Args:
input_path: Path to input image file
output_path: Path to save resized image
target_size: Target dimensions (width, height)
interpolation: Interpolation method (default: bilinear)
"""
with Image.open(input_path) as img:
resized_img = img.resize(target_size, interpolation)
resized_img.save(output_path)
crop_image_info = {
"name": "crop_image",
"description": "Crops a rectangular region from an image",
"parameters": {
"type": "object",
"properties": {
"input_path": {"type": "string", "description": "Input image file path"},
"output_path": {"type": "string", "description": "Output path for cropped image"},
"box": {
"type": "array",
"items": {"type": "integer"},
"minItems": 4,
"maxItems": 4,
"description": "Crop region coordinates [left, upper, right, lower]"
}
},
"required": ["input_path", "output_path", "box"]
}
}
def crop_image(input_path, output_path, box):
"""
Crop a region from an image
Args:
input_path: Path to input image file
output_path: Path to save cropped image
box: Crop region coordinates (left, upper, right, lower)
"""
with Image.open(input_path) as img:
cropped_img = img.crop(box)
cropped_img.save(output_path)
rotate_image_info = {
"name": "rotate_image",
"description": "Rotates an image by specified degrees with canvas expansion support",
"parameters": {
"type": "object",
"properties": {
"input_path": {"type": "string", "description": "Input image file path"},
"output_path": {"type": "string", "description": "Output path for rotated image"},
"degrees": {"type": "number", "description": "Rotation angle in degrees (clockwise)"},
"expand": {
"type": "boolean",
"description": "Whether to expand canvas to fit rotation (default: False)"
},
"fill_color": {
"type": "array",
"items": {"type": "integer"},
"minItems": 3,
"maxItems": 3,
"description": "RGB fill color for expanded areas (default: [255,255,255])"
}
},
"required": ["input_path", "output_path", "degrees"]
}
}
def rotate_image(input_path, output_path, degrees, expand=False, fill_color=(255, 255, 255)):
"""
Rotate an image by specified degrees
Args:
input_path: Path to input image file
output_path: Path to save rotated image
degrees: Rotation angle in degrees
expand: Whether to expand canvas to fit rotation
fill_color: Fill color for expanded areas
"""
with Image.open(input_path) as img:
rotated_img = img.rotate(degrees, expand=expand, fillcolor=fill_color)
rotated_img.save(output_path)
adjust_brightness_info = {
"name": "adjust_brightness",
"description": "Adjusts image brightness using enhancement factor",
"parameters": {
"type": "object",
"properties": {
"input_path": {"type": "string", "description": "Input image file path"},
"output_path": {"type": "string", "description": "Output path for adjusted image"},
"factor": {
"type": "number",
"description": "Brightness multiplier (1.0=original, >1.0=brighter, <1.0=darker)"
}
},
"required": ["input_path", "output_path", "factor"]
}
}
def adjust_brightness(input_path, output_path, factor):
"""
Adjust image brightness
Args:
input_path: Path to input image file
output_path: Path to save adjusted image
factor: Brightness multiplier (1.0=original, >1.0=brighter, <1.0=darker)
"""
with Image.open(input_path) as img:
enhancer = ImageEnhance.Brightness(img)
bright_img = enhancer.enhance(factor)
bright_img.save(output_path)
get_open_world_vocabulary_detection_info = {
"name": "get_open_world_vocabulary_detection",
"description": "Detects objects in an image using open vocabulary detection",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "list",
"description": "List of objects to detect",
},
"image_path": {
"type": "str",
"description": "Path to the image file"
}
},
"required": ["text", "image_path"],
},
}
get_3d_loc_in_cam_info = {
"name": "get_3d_loc_in_cam",
"description": "Calculates 3D locations of objects in camera coordinates",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "list",
"description": "List of objects to locate",
},
"image_path": {
"type": "str",
"description": "Path to the image file"
}
},
"required": ["text", "image_path"],
},
} |