├── .gitignore
├── LICENSE.md
├── Makefile
├── README.md
├── controller
├── __init__.py
├── abs
│ ├── robot_wrapper.py
│ └── skill_item.py
├── assets
│ ├── Roboto-Medium.ttf
│ ├── gear
│ │ └── model.pth
│ ├── minispec_syntax.txt
│ └── tello
│ │ ├── guides.txt
│ │ ├── high_level_skills copy.json
│ │ ├── high_level_skills.json
│ │ ├── plan_examples.txt
│ │ ├── plan_examples_back_up.txt
│ │ ├── prompt_plan.txt
│ │ └── prompt_probe.txt
├── gear_wrapper.py
├── llm_controller.py
├── llm_planner.py
├── llm_wrapper.py
├── minispec_interpreter.py
├── shared_frame.py
├── skillset.py
├── tello_wrapper.py
├── utils.py
├── virtual_robot_wrapper.py
├── vision_skill_wrapper.py
├── yolo_client.py
└── yolo_grpc_client.py
├── docker
├── env.list
├── router
│ └── Dockerfile
└── yolo
│ └── Dockerfile
├── proto
├── generate.sh
├── generated
│ ├── README.md
│ └── __init__.py
└── hyrch_serving.proto
├── serving
├── router
│ ├── router.py
│ └── service_manager.py
├── webui
│ ├── drone-pov.html
│ ├── header.html
│ ├── install_requirements.sh
│ └── typefly.py
└── yolo
│ └── yolo_service.py
└── test
├── aiohttp-client.py
├── gpt-latency-measurement.py
├── images
└── kitchen.webp
├── interpreter-test.py
├── stop-tello.py
├── tello-test.py
├── yolo-grpc-test.py
├── yolo-test-raw.py
└── yolo-test.py
/.gitignore:
--------------------------------------------------------------------------------
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2 | __pycache__/
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4 | *$py.class
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83 | ipython_config.py
84 |
85 | # pyenv
86 | # For a library or package, you might want to ignore these files since the code is
87 | # intended to run in multiple environments; otherwise, check them in:
88 | # .python-version
89 |
90 | # pipenv
91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
94 | # install all needed dependencies.
95 | #Pipfile.lock
96 |
97 | # poetry
98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
99 | # This is especially recommended for binary packages to ensure reproducibility, and is more
100 | # commonly ignored for libraries.
101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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114 |
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117 | celerybeat.pid
118 |
119 | # SageMath parsed files
120 | *.sage.py
121 |
122 | # Environments
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125 | env/
126 | venv/
127 | ENV/
128 | env.bak/
129 | venv.bak/
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132 | .spyderproject
133 | .spyproject
134 |
135 | # Rope project settings
136 | .ropeproject
137 |
138 | # mkdocs documentation
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140 |
141 | # mypy
142 | .mypy_cache/
143 | .dmypy.json
144 | dmypy.json
145 |
146 | # Pyre type checker
147 | .pyre/
148 |
149 | # pytype static type analyzer
150 | .pytype/
151 |
152 | # Cython debug symbols
153 | cython_debug/
154 |
155 | # PyCharm
156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
158 | # and can be added to the global gitignore or merged into this file. For a more nuclear
159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder.
160 | #.idea/
161 |
162 | *.pt
163 | .vscode/
164 | images/
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166 | proto/generated/*.py
167 | test.py
168 | cache/
169 | chat_log.txt
170 | results/
--------------------------------------------------------------------------------
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675 |
--------------------------------------------------------------------------------
/Makefile:
--------------------------------------------------------------------------------
1 | .PHONY: stop, start, remove, open, build
2 |
3 | SERVICE_LIST = router yolo
4 | GPU_OPTIONS=--gpus all
5 |
6 | validate_service:
7 | ifeq ($(filter $(SERVICE),$(SERVICE_LIST)),)
8 | @echo Invalid SERVICE: [$(SERVICE)], valid values are [$(SERVICE_LIST)]
9 | $(error Invalid SERVICE, valid values are [$(SERVICE_LIST)])
10 | endif
11 |
12 | stop: validate_service
13 | @echo "=> Stopping typefly-$(SERVICE)..."
14 | @-docker stop -t 0 typefly-$(SERVICE) > /dev/null 2>&1
15 | @-docker rm -f typefly-$(SERVICE) > /dev/null 2>&1
16 |
17 | start: validate_service
18 | @make stop SERVICE=$(SERVICE)
19 | @echo "=> Starting typefly-$(SERVICE)..."
20 | docker run -td --privileged --net=host $(GPU_OPTIONS) --ipc=host \
21 | --env-file ./docker/env.list \
22 | --name="typefly-$(SERVICE)" typefly-$(SERVICE):0.1
23 |
24 | remove: validate_service
25 | @echo "=> Removing typefly-$(SERVICE)..."
26 | @-docker image rm -f typefly-$(SERVICE):0.1 > /dev/null 2>&1
27 | @-docker rm -f typefly-$(SERVICE) > /dev/null 2>&1
28 |
29 | open: validate_service
30 | @echo "=> Opening bash in typefly-$(SERVICE)..."
31 | @docker exec -it typefly-$(SERVICE) bash
32 |
33 | build: validate_service
34 | @echo "=> Building typefly-$(SERVICE)..."
35 | @make stop SERVICE=$(SERVICE)
36 | @make remove SERVICE=$(SERVICE)
37 | @echo -n "=>"
38 | docker build -t typefly-$(SERVICE):0.1 -f ./docker/$(SERVICE)/Dockerfile .
39 | @echo -n "=>"
40 | @make start SERVICE=$(SERVICE)
41 |
42 | typefly:
43 | bash ./serving/webui/install_requirements.sh
44 | cd ./proto && bash generate.sh
45 | python3 ./serving/webui/typefly.py --use_virtual_robot
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # TypeFly
2 | TypeFly aims to generate robot task plan using large language model (LLM) and our custom programming language `MiniSpec`. Link to our [full Paper](https://drive.google.com/file/d/1COrozqEIk6v8DLxI3vCgoSUEWpnsc2mu/view) and [webpage](https://typefly.github.io/).
3 |
4 | Also, check out the demo video here: [Demo 1: Find edible or drinkable items](http://www.youtube.com/watch?v=HEJYaTLWKfY), [Demo 2: Find a specific chair](http://www.youtube.com/watch?v=QwnBniFaINE).
5 |
6 | ## Hardware Requirement
7 | TypeFly works with DJI Tello drone by default. Since Tello drone requires your device to connect to its wifi and TypeFly requires Internet connection, you need to have both wifi adapter and ethernet adapter to run TypeFly.
8 | To support other drones, you need to implement the `RobotWrapper` interface in `controller/abs/drone_wrapper.py`.
9 |
10 | ## OPENAI API KEY Requirement
11 | TypeFly use GPT-4 API as the remote LLM planner, please make sure you have set the `OPENAI_API_KEY` environment variable.
12 |
13 | ## Vision Encoder
14 | TypeFly uses YOLOv8 to generate the scene description. We provide the implementation of gRPC YOLO service and a optional http router to serve as a scheduler when working with multiple drones. We recommand using [docker](https://docs.docker.com/engine/install/ubuntu/) to run the YOLO and router. To deploy the YOLO servive with docker, please install the [Nvidia Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html), then run the following command:
15 | ```bash
16 | make SERVICE=yolo build
17 | ```
18 | Optional: To deploy the router, please run the following command:
19 | ```bash
20 | make SERVICE=router build
21 | ```
22 |
23 | ## TypeFly Web UI
24 | To play with the TypeFly web UI, please run the following command:
25 | ```bash
26 | make typefly
27 | ```
28 | This will start the web UI at `http://localhost:50001` with your default camera (please make sure your device has a camera) and a virtual drone wrapper. You should be able to see the image capture window displayed with YOLO detection results. You can test the planning ability of TypeFly by typing in the chat box.
29 |
30 | To work with a real drone, please disable the `--use_virtual_robot` flag in `Makefile`.
31 |
32 | Here we assume your YOLO and router are deployed on the same machine running the TypeFly webui, if not, please define the environment variables `VISION_SERVICE_IP`, which is the IP address where you deploy your YOLO (or router) service, before running the webui.
33 |
34 | ## Task Execution
35 | Here are some examples of task descriptions, the `[Q]` prefix indicates TypeFly will output an answer to the question:
36 | - `Can you find something edible?`
37 | - `Can you see a person behind you?`
38 | - `[Q] Tell me how many people you can see?`
39 |
--------------------------------------------------------------------------------
/controller/__init__.py:
--------------------------------------------------------------------------------
1 | from .yolo_client import YoloClient
2 | from .llm_controller import LLMController
3 | from .skillset import SkillSet, SkillItem, SkillArg
--------------------------------------------------------------------------------
/controller/abs/robot_wrapper.py:
--------------------------------------------------------------------------------
1 | from abc import ABC, abstractmethod
2 | from enum import Enum
3 |
4 | class RobotType(Enum):
5 | VIRTUAL = 0
6 | TELLO = 1
7 | GEAR = 2
8 |
9 | class RobotWrapper(ABC):
10 | movement_x_accumulator = 0
11 | movement_y_accumulator = 0
12 | rotation_accumulator = 0
13 | @abstractmethod
14 | def connect(self):
15 | pass
16 |
17 | @abstractmethod
18 | def keep_active(self):
19 | pass
20 |
21 | @abstractmethod
22 | def takeoff(self) -> bool:
23 | pass
24 |
25 | @abstractmethod
26 | def land(self):
27 | pass
28 |
29 | @abstractmethod
30 | def start_stream(self):
31 | pass
32 |
33 | @abstractmethod
34 | def stop_stream(self):
35 | pass
36 |
37 | @abstractmethod
38 | def get_frame_reader(self):
39 | pass
40 |
41 | @abstractmethod
42 | def move_forward(self, distance: int) -> bool:
43 | pass
44 |
45 | @abstractmethod
46 | def move_backward(self, distance: int) -> bool:
47 | pass
48 |
49 | @abstractmethod
50 | def move_left(self, distance: int) -> bool:
51 | pass
52 |
53 | @abstractmethod
54 | def move_right(self, distance: int) -> bool:
55 | pass
56 |
57 | @abstractmethod
58 | def move_up(self, distance: int) -> bool:
59 | pass
60 |
61 | @abstractmethod
62 | def move_down(self, distance: int) -> bool:
63 | pass
64 |
65 | @abstractmethod
66 | def turn_ccw(self, degree: int) -> bool:
67 | pass
68 |
69 | @abstractmethod
70 | def turn_cw(self, degree: int) -> bool:
71 | pass
72 |
--------------------------------------------------------------------------------
/controller/abs/skill_item.py:
--------------------------------------------------------------------------------
1 | from abc import ABC, abstractmethod
2 | from typing import List, Union, Tuple
3 |
4 | class SkillArg:
5 | def __init__(self, arg_name: str, arg_type: type):
6 | self.arg_name = arg_name
7 | self.arg_type = arg_type
8 |
9 | def __repr__(self):
10 | return f"{self.arg_name}:{self.arg_type.__name__}"
11 |
12 | class SkillItem(ABC):
13 | @abstractmethod
14 | def get_name(self) -> str:
15 | pass
16 |
17 | @abstractmethod
18 | def get_skill_description(self) -> str:
19 | pass
20 |
21 | @abstractmethod
22 | def get_argument(self) -> List[SkillArg]:
23 | pass
24 |
25 | @abstractmethod
26 | def __repr__(self) -> str:
27 | pass
28 |
29 | @abstractmethod
30 | def execute(self, arg_list: List[Union[int, float, str]]) -> Tuple[Union[int, float, bool, str], bool]:
31 | pass
32 |
33 | abbr_dict = {}
34 | def generate_abbreviation(self, word):
35 | split = word.split('_')
36 | abbr = ''.join([part[0] for part in split])[0:2]
37 |
38 | if abbr not in self.abbr_dict:
39 | self.abbr_dict[abbr] = word
40 | return abbr
41 |
42 | split = ''.join([part for part in split])[1:]
43 |
44 | count = 0
45 | while abbr in self.abbr_dict:
46 | abbr = abbr[0] + split[count]
47 | count += 1
48 |
49 | self.abbr_dict[abbr] = word
50 | return abbr
51 |
52 | def parse_args(self, args_str_list: List[Union[int, float, str]], allow_positional_args: bool = False):
53 | """Parses the string of arguments and converts them to the expected types."""
54 | # Check the number of arguments
55 | if len(args_str_list) != len(self.args):
56 | raise ValueError(f"Expected {len(self.args)} arguments, but got {len(args_str_list)}.")
57 |
58 | parsed_args = []
59 | for i, arg in enumerate(args_str_list):
60 | # if arg is not a string, skip parsing
61 | if not isinstance(arg, str):
62 | parsed_args.append(arg)
63 | continue
64 | # Allow positional arguments
65 | if arg.startswith('$') and allow_positional_args:
66 | parsed_args.append(arg)
67 | continue
68 | try:
69 | if self.args[i].arg_type == bool:
70 | parsed_args.append(arg.strip().lower() == 'true')
71 | else:
72 | parsed_args.append(self.args[i].arg_type(arg.strip()))
73 | except ValueError as e:
74 | raise ValueError(f"Error parsing argument {i + 1}. Expected type {self.args[i].arg_type.__name__}, but got value '{arg.strip()}'. Original error: {e}")
75 | return parsed_args
--------------------------------------------------------------------------------
/controller/assets/Roboto-Medium.ttf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/typefly/TypeFly/6f40a91bd3e1dee971090c4d33b707c2a8dc5045/controller/assets/Roboto-Medium.ttf
--------------------------------------------------------------------------------
/controller/assets/gear/model.pth:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/typefly/TypeFly/6f40a91bd3e1dee971090c4d33b707c2a8dc5045/controller/assets/gear/model.pth
--------------------------------------------------------------------------------
/controller/assets/minispec_syntax.txt:
--------------------------------------------------------------------------------
1 | ::= { [';'] | ';' }
2 | ::= | |
3 | ::= |
4 | ::= '{' '}'
5 | ::= ['(' ')']
6 | ::= '='
7 | ::= '?' '{' '}'
8 | ::= { '&' | '|' }
9 | ::= '>' | '<' | '==' | '!='
10 | ::= { }
11 | ::= { ',' }
12 | ::= '->'
13 | ::= |
14 | ::= |
15 | ::= '_'
16 | ::= | | |
--------------------------------------------------------------------------------
/controller/assets/tello/guides.txt:
--------------------------------------------------------------------------------
1 | Handling Typos and Ambiguous Language: When encountering typos or vague language in user inputs, strive to clarify and correct these ambiguities. If a typo is evident, adjust the input to reflect the most likely intended meaning. For vague or unclear expressions, seek additional information from the user by returning a question to the user.
2 | Analytical Scene and Task Assessment: Evaluate the scene and task critically. If a specific condition or requirement is clearly deduced from the scene description, generate the corresponding action directly.
3 | Relevance to Current Scene: When considering tasks, assess their relevance to the current scene. If a task does not pertain to the existing scene, disregard any details from the current scene in formulating your response.
4 | Extraction of Key Information: Extract 'object_name' arguments or answers to questions primarily from the 'scene description'. If the necessary information is not present in the current scene, especially when the user asks the robot to move first or check somewhere else, use the probe 'p,' followed by the 'question', to determine the 'object_name' or answer subsequently.
5 | Handling Replan: When previous response and execution status are provided, it means part of the task has been executed. In this case, the system should replan the remaining task based on the current scene and the previous response.
6 | Under no circumstances should the system navigate the robot to hurt itself or others. If the task involves potential harm, the system should refuse to execute the task and provide a suitable explanation to the user.
7 |
--------------------------------------------------------------------------------
/controller/assets/tello/high_level_skills copy.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "skill_name": "scan",
4 | "skill_description": "Rotate to find object $1 when it's *not* in current scene",
5 | "definition": "8{?iv($1)==True{->True}tc(45)}->False;"
6 | },
7 | {
8 | "skill_name": "scan_abstract",
9 | "skill_description": "Rotate to find an abstract object by a description $1 when it's *not* in current scene",
10 | "definition": "8{_1=p($1);?_1!=False{->_1}tc(45)}->False;"
11 | },
12 | {
13 | "skill_name": "orienting",
14 | "skill_description": "Rotate to align with object $1",
15 | "definition": "4{_1=ox($1);?_1>0.6{tc(15)};?_1<0.4{tu(15)};_2=ox($1);?_2<0.6&_2>0.4{->True}}->False;"
16 | },
17 | {
18 | "skill_name": "approach",
19 | "skill_description": "Approach forward",
20 | "definition": "mf(100);"
21 | },
22 | {
23 | "skill_name": "goto",
24 | "skill_description": "Go to object $1",
25 | "definition": "orienting($1);approach();"
26 | }
27 | ]
--------------------------------------------------------------------------------
/controller/assets/tello/high_level_skills.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "skill_name": "scan",
4 | "skill_description": "Rotate to find object $1 when it's *not* in current scene",
5 | "definition": "8{?iv($1)==True{->True}tc(45)}->False;"
6 | },
7 | {
8 | "skill_name": "scan_abstract",
9 | "skill_description": "Rotate to find an abstract object by a description $1 when it's *not* in current scene",
10 | "definition": "8{_1=p($1);?_1!=False{->_1}tc(45)}->False;"
11 | }
12 | ]
--------------------------------------------------------------------------------
/controller/assets/tello/plan_examples.txt:
--------------------------------------------------------------------------------
1 | Example 1:
2 | Scene: []
3 | Task: [A] Find a bottle, tell me it's height and take a picture of it.
4 | Reason: no bottle instance in the scene, so we use scan to find bottle, then go and use object_height to get the height and log to output the height, finally use picture to take a picture of the bottle
5 | Response: ?s('bottle')==True{g('bottle');_2=oh('bottle');l(_2);tp};
6 |
7 | Example 2:
8 | Scene: [apple x:0.28 y:0.52 width:0.13 height:0.2]
9 | Task: [A] Find an apple.
10 | Reason: there is an apple instance in the scene, we just go to it
11 | Response: g('apple');
12 |
13 | Example 3:
14 | Scene: [apple x:0.28 y:0.15 width:0.2 height:0.19]
15 | Task: [Q] Is there an apple and an orange on your left?
16 | Reason: turn left 90 degrees, then use is_visible to check whether there is an apple on your left
17 | Response: tu(90);?iv('apple')==True&iv('orange'){l('Yes');->True}l('No');->False;
18 |
19 | Example 4:
20 | Scene: [chair x:0.58 y:0.5 width:0.43 height:0.7,laptop x:0.58 y:0.5 width:0.43 height:0.7]
21 | Task: [A] Turn around and go to the chair behind you.
22 | Reason: the chair is not the target because we want the one behind you. So we turn 180 degrees then go to the general object chair, since chair is a large object, we use 80cm as the distance.
23 | Response: tc(180);g('chair');
24 |
25 | Example 5:
26 | Scene: [chair x:0.32 y:0.35 width:0.56 height:0.4]
27 | Task: [A] Find and go any edible object.
28 | Reason: edible object is abstract and there is no edible object in the scene, so we use scan_abstract to find the edible object
29 | Response: _1=sa('Any edible target here?');?_1!=False{g(_1)};
30 |
31 | Example 6:
32 | Scene: [chair x:0.28 y:0.12 width:0.43 height:0.67,laptop x:0.78 y:0.45 width:0.23 height:0.25]
33 | Task: [A] Turn with 30 degrees step until you can see some animal.
34 | Reason: we use loop and probe to find animal
35 | Response: 12{_1=p('Any animal target here?');?_1!=False{l(_1);->True}tc(30)}->False;
36 |
37 | Example 7:
38 | Scene: [chair x:0.28 y:0.12 width:0.43 height:0.67,laptop x:0.28 y:0.12 width:0.43 height:0.67]
39 | Task: [A] If you can see a chair, go find a person, else go find an orange.
40 | Reason: From the scene, we can see a chair, so we use scan to find a person. Since person is a large object, we use 60cm as the distance
41 | Response: ?s('person')==True{g('person');->True}->False;
42 |
43 | Example 8:
44 | Scene: [chair x:0.48 y:0.22 width:0.23 height:0.17,chair x:0.18 y:0.12 width:0.33 height:0.27]
45 | Task: [A] Go to
46 | Reason: The task is too vague, so we use log to output the advice
47 | Response: l('Please give me more information.');
48 |
49 | Example 9:
50 | Scene: [chair x:0.18 y:0.5 width:0.43 height:0.7, chair x:0.6 y:0.3 width:0.08 height:0.09, book x:0.62 y:0.26 width:0.23 height:0.17]
51 | Task: [A] Go to the chair with book on it.
52 | Reason: There are two chairs, the second one is closer to the book, so we go to the second chair
53 | Response: g('chair[0.6]');
54 |
55 | Example 10:
56 | Scene: [apple x:0.74 y:0.3 width:0.09 height:0.1, apple x:0.3 y:0.5 width:0.15 height:0.12]
57 | Task: [A] Go to biggest apple
58 | Reason: from the scene, we tell directly that the right apple is the biggest apple
59 | Response: g('apple[0.3]');
60 |
61 | Example 11:
62 | Scene: [apple x:0.74 y:0.3 width:0.09 height:0.1]
63 | Task: [A] Find a chair with a laptop on it.
64 | Reason: Using scan abstract to find the chair with a laptop on it
65 | Response: _1=sa('Any chair with a laptop on it?');?_1!=False{g(_1)};
66 |
67 | Example 12:
68 | Scene: [sports ball x:0.74 y:0.3 width:0.09 height:0.1]
69 | Task: [A] Follow the ball for 17s.
70 | Reason: There is a ball in the scene, so we try to align with it and follow it for 17s
71 | Response: _1=ow('sports ball');500{?time()>17{->True}_2=ox('sports ball');?_2>0.55{tc(15)}?_2<0.45{tu(15)}_3=ow('sports ball');?_3>_1*1.2{mb(40)}?_3<_1*0.8{mf(40)}d(0.25)}
72 |
73 | Example 13:
74 | Scene: [chair x:0.18 y:0.5 width:0.43 height:0.7, chair x:0.6 y:0.3 width:0.08 height:0.09, book x:0.62 y:0.26 width:0.23 height:0.17]
75 | Task: [Q] Which chair is closer to the book?
76 | Reason: The second chair (on the right) is closer to the book
77 | Response: l('the right chair');
78 |
79 | Example 14:
80 | Scene: []
81 | Task: [A] Move in a 'V' shape.
82 | Reason: Go forward, then turn 135 degrees to the left, finally go forward again.
83 | Response: mf(100);tu(135);mf(100);
--------------------------------------------------------------------------------
/controller/assets/tello/plan_examples_back_up.txt:
--------------------------------------------------------------------------------
1 | Example 1:
2 | Scene: []
3 | Task: [A] Find a bottle, tell me it's height and take a picture of it.
4 | Reason: no bottle instance in the scene, so we use scan to find bottle, then go and use object_height to get the height and log to output the height, finally use picture to take a picture of the bottle
5 | Response: ?s('bottle')==True{g('bottle');_2=oh('bottle');l(_2);tp};
6 |
7 | Example 2:
8 | Scene: [apple_5]
9 | Task: [A] Find an apple.
10 | Reason: there is an apple instance in the scene, we just go to the apple_5
11 | Response: g('apple_5');
12 |
13 | Example 3:
14 | Scene: [apple_3]
15 | Task: [Q] Is there an apple and an orange on your left?
16 | Reason: turn left 90 degrees, then use is_visible to check whether there is an apple on your left
17 | Response: tu(90);?iv('apple')==True&iv('orange'){l('Yes');->True}l('No');->False;
18 |
19 | Example 4:
20 | Scene: [chair_13,laptop_2]
21 | Task: [A] Go to the chair behind you.
22 | Reason: the chair_13 is not the target because we want the one behind you. So we turn 180 degrees then go to the general object chair, since chair is a large object, we use 80cm as the distance.
23 | Response: tc(180);g('chair');
24 |
25 | Example 5:
26 | Scene: [chair_3,laptop_1,bottle_5]
27 | Task: [A] Find and go any edible object.
28 | Reason: edible object is abstract and there is no edible object in the scene, so we use scan_abstract to find the edible object
29 | Response: _1=sa('Any edible target here?');?_1!=False{g(_1)};
30 |
31 | Example 6:
32 | Scene: [chair_3,laptop_9]
33 | Task: [A] Turn around with 30 degrees step until you can see some animal.
34 | Reason: we use loop and probe to find animal
35 | Response: 12{_1=p('Any animal target here?');?_1!=False{l(_1);->True}tc(30)}->False;
36 |
37 | Example 7:
38 | Scene: [chair_3,laptop_9]
39 | Task: [A] If you can see a chair, go find a person, else go find an orange.
40 | Reason: From the scene, we can see a chair, so we use scan to find a person. Since person is a large object, we use 60cm as the distance
41 | Response: _1=s('person');?_1==True{g('person');->True}->False;
42 |
43 | Example 8:
44 | Scene: [chair_3,laptop_9]
45 | Task: [A] Go to
46 | Reason: The task is too vague, so we use log to output the advice
47 | Response: l('Please give me more information.');
48 |
49 | Example 9:
50 | Scene: [chair_1 x:0.58 y:0.5 width:0.43 height:0.7, apple_1 x:0.6 y:0.3 width:0.08 height:0.09]
51 | Task: [A] Turn around and go to the apple
52 | Reason: after turning around, we will do replan. We found that the chair is blocking the apple, so we use moving_up to get over the chair and then go to the apple
53 | Response: mu(40);g('apple');
54 |
55 | Example 10:
56 | Scene: [apple_1 x:0.34 y:0.3 width:0.09 height:0.1, apple_2 x:0.3 y:0.5 width:0.15 height:0.12]
57 | Task: [A] Go to biggest apple
58 | Reason: from the scene, we tell directly that apple_2 is the biggest apple
59 | Response: g('apple_2');
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/controller/assets/tello/prompt_plan.txt:
--------------------------------------------------------------------------------
1 | You are a robot pilot and you should follow the user's instructions to generate a MiniSpec plan to fulfill the task or give advice on user's input if it's not clear or not reasonable.
2 |
3 | Your response should carefully consider the 'system skills description', the 'scene description', the 'task description', and both the 'previous response' and the 'execution status' if they are provided.
4 | The 'system skills description' describes the system's capabilities which include low-level and high-level skills. Low-level skills, while fixed, offer direct function calls to control the robot and acquire vision information. High-level skills, built with our language 'MiniSpec', are more flexible and can be used to build more complex skills. Whenever possible, please prioritize the use of high-level skills, invoke skills using their designated abbreviations, and ensure that 'object_name' refers to a specific type of object. If a skill has no argument, you can call it without parentheses.
5 |
6 | Description of the two skill sets:
7 | - High-level skills:
8 | {system_skill_description_high}
9 | - Low-level skills:
10 | {system_skill_description_low}
11 |
12 | The 'scene description' is an object list of the current view, containing their names with ID, location, and size (location and size are floats between 0~1). This may not be useful if the task is about the environment outside the view.
13 | The 'task description' is a natural language sentence, describing the user's instructions. It may start with "[A]" or "[Q]". "[A]" sentences mean you should generate an execution plan for the robot. "[Q]" sentences mean you should use 'log' to show a literal answer at the end of the plan execution. Please carefully reason about the 'task description', you should interpret it and generate a detailed multi-step plan to achieve it as much as you can
14 | The 'execution history' is the actions have been taken from previous response. When they are provided, that means the robot is doing replanning, and the user wants to continue the task based on the task and history. You should reason about the 'execution history' and generate a new response accordingly.
15 |
16 | Here are some extra guides for you to better understand the task and generate a better response:
17 | {guides}
18 |
19 | Here is a list of example 'response' for different 'scene description' and 'task description', and their explanations:
20 | {plan_examples}
21 |
22 | Here is the 'scene description':
23 | {scene_description}
24 |
25 | Here is the 'task description':
26 | {task_description}
27 |
28 | Here is the 'execution history' (has value if replanning):
29 | {execution_history}
30 |
31 | Please generate the response only with a single sentence of MiniSpec program.
32 | 'response':
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/controller/assets/tello/prompt_probe.txt:
--------------------------------------------------------------------------------
1 | """
2 | You are given a scene description and a question. You should output the answer to the question based on the scene description.
3 | The scene description contains listed objects with their respective names, locations, and sizes.
4 | The question is a string that asks about the scene or the objects in the scene.
5 | For yes-or-no questions, output with 'True' or 'False' only.
6 | For object identification, output the object's name (if there are multiple same objects, output the target one with x value). If the object is not in the list, output with 'False'.
7 | For counting questions, output the exact number of target objects.
8 | For general questions, output a brief, single-sentence answer.
9 |
10 | Input Format:
11 | Scene Description:[List of Objects with Attributes]
12 | Question:[A String]
13 |
14 | Output Format:
15 | [A String]
16 |
17 | Here are some examples:
18 | Example 1:
19 | Scene Description:[person x:0.59 y:0.55 width:0.81 height:0.91, bottle x:0.85 y:0.54 width:0.21 height:0.93]
20 | Question:'Any drinkable target here?'
21 | Output:bottle
22 |
23 | Example 2:
24 | Scene Description:[]
25 | Question:'Any table in the room?'
26 | Output:False
27 |
28 | Example 3:
29 | Scene Description:[chair x:0.1 y:0.35 width:0.56 height:0.41, chair x:0.49 y:0.59 width:0.61 height:0.35]
30 | Question:'How many chairs you can see?'
31 | Output:2
32 |
33 | Example 4:
34 | Scene Description:[bottle x:0.1 y:0.35 width:0.56 height:0.41, chair x:0.49 y:0.59 width:0.61 height:0.35]
35 | Question:'Any edible target here?'
36 | Output:False
37 |
38 | Example 5:
39 | Scene Description:[chair x:0.18 y:0.5 width:0.43 height:0.7, chair x:0.6 y:0.3 width:0.08 height:0.09, book x:0.62 y:0.26 width:0.23 height:0.17]
40 | Question:'Any chair with a laptop on it?'
41 | Output:chair[0.6]
42 | """
43 | Scene Description:{scene_description}
44 | Question:{question}
45 | Please give the content of results only, don't include 'Output:' in the results.
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/controller/gear_wrapper.py:
--------------------------------------------------------------------------------
1 | import time, os
2 | from typing import Tuple
3 | from .abs.robot_wrapper import RobotWrapper
4 | from podtp import Podtp
5 | import torch
6 | import torch.nn as nn
7 | import numpy as np
8 |
9 | DEFAULT_NO_VALID_READING = 0
10 | SAFE_DISTANCE_THRESHOLD = 250
11 | SIDE_DISTANCE_THRESHOLD = 65
12 | JUMP_DISTANCE_THRESHOLD = 60
13 |
14 | CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
15 |
16 | def clean_sensor_data(raw_data):
17 | cleaned_data = raw_data[:] # Create a copy of the raw data for cleaning
18 |
19 | for i in range(len(cleaned_data)):
20 | if cleaned_data[i] < 0:
21 | valid_previous = None
22 | valid_next = None
23 |
24 | # Find the previous valid value
25 | for j in range(i-1, -1, -1):
26 | if cleaned_data[j] >= 0:
27 | valid_previous = cleaned_data[j]
28 | break
29 |
30 | # Find the next valid value
31 | for k in range(i+1, len(cleaned_data)):
32 | if cleaned_data[k] >= 0:
33 | valid_next = cleaned_data[k]
34 | break
35 |
36 | # Decide what value to assign to the bad reading
37 | if valid_previous is not None and valid_next is not None:
38 | # Average if both previous and next valid values are found
39 | cleaned_data[i] = (valid_previous + valid_next) / 2
40 | elif valid_previous is not None:
41 | # Use the previous if only it is available
42 | cleaned_data[i] = valid_previous
43 | elif valid_next is not None:
44 | # Use the next if only it is available
45 | cleaned_data[i] = valid_next
46 | else:
47 | # If no valid readings are available, handle it with a default value or recheck
48 | cleaned_data[i] = DEFAULT_NO_VALID_READING
49 |
50 | return cleaned_data
51 |
52 | # Define the model
53 | class DirectionPredictor(nn.Module):
54 | def __init__(self):
55 | super(DirectionPredictor, self).__init__()
56 | self.flatten = nn.Flatten()
57 | self.linear_relu_stack = nn.Sequential(
58 | nn.Linear(24, 64),
59 | nn.ReLU(),
60 | nn.Linear(64, 64),
61 | nn.ReLU(),
62 | nn.Linear(64, 3)
63 | )
64 | self.mean = 381.9494323730469
65 | self.std = 306.9201965332031
66 |
67 | def forward(self, x):
68 | x = self.flatten(x)
69 | logits = self.linear_relu_stack(x)
70 | return logits
71 |
72 | class GearWrapper(RobotWrapper):
73 | def __init__(self):
74 | self.stream_on = False
75 | config = {
76 | 'ip': '192.168.8.169',
77 | 'ip1': '192.168.8.195',
78 | 'port': 80,
79 | 'stream_port': 81
80 | }
81 | self.robot = Podtp(config)
82 | self.move_speed_x = 2.5
83 | self.move_speed_y = 2.8
84 | self.unlock_count = 0
85 | self.model = DirectionPredictor()
86 | self.model.load_state_dict(torch.load(os.path.join(CURRENT_DIR, 'assets/gear/model.pth')))
87 | self.model.eval()
88 |
89 | def keep_active(self):
90 | self.unlock_count += 1
91 | if self.unlock_count > 100:
92 | self.robot.send_ctrl_lock(False)
93 | self.unlock_count = 0
94 |
95 | def connect(self):
96 | if not self.robot.connect():
97 | raise ValueError("Could not connect to the robot")
98 | if not self.robot.send_ctrl_lock(False):
99 | raise ValueError("Could not unlock the robot control")
100 |
101 | def takeoff(self) -> bool:
102 | return True
103 |
104 | def land(self):
105 | pass
106 |
107 | def start_stream(self):
108 | self.robot.start_stream()
109 | self.stream_on = True
110 |
111 | def stop_stream(self):
112 | self.robot.stop_stream()
113 | self.stream_on = False
114 |
115 | def get_frame_reader(self):
116 | if not self.stream_on:
117 | return None
118 | return self.robot.sensor_data
119 |
120 | def move_forward(self, distance: int) -> Tuple[bool, bool]:
121 | print(f"-> Moving forward {distance} cm")
122 | self.robot.send_command_hover(0, 0, 0, 0)
123 | small_move = distance <= 15
124 | while distance > 0:
125 | if small_move:
126 | self.robot.send_command_hover(0, self.move_speed_x, 0, 0)
127 | else:
128 | array = self.robot.sensor_data.depth.data
129 | left_distance = clean_sensor_data(array[0, :])
130 | front_distance = clean_sensor_data(array[2, :])
131 | right_distance = clean_sensor_data(array[7, :])
132 | if max(front_distance) < 50:
133 | self.move_backward(10)
134 |
135 | x = np.concatenate((left_distance, front_distance, right_distance))
136 | x = torch.tensor(x, dtype=torch.float32)
137 | x = (x - self.model.mean) / self.model.std
138 | y = self.model(x.unsqueeze(0)).squeeze(0)
139 | command = torch.argmax(y).item() - 1
140 |
141 | left_margin = min(left_distance)
142 | right_margin = min(right_distance)
143 | if left_margin > SIDE_DISTANCE_THRESHOLD and right_margin > SIDE_DISTANCE_THRESHOLD:
144 | vy = 0
145 | elif left_margin > SIDE_DISTANCE_THRESHOLD:
146 | vy = -1.5
147 | elif right_margin > SIDE_DISTANCE_THRESHOLD:
148 | vy = 1.5
149 | else:
150 | if abs(left_margin - right_margin) > 80:
151 | if left_margin < right_margin:
152 | vy = 1.5
153 | else:
154 | vy = -1.5
155 |
156 | if command == 0:
157 | self.robot.send_command_hover(0, self.move_speed_x, vy, 0)
158 | elif command == 1:
159 | self.turn_ccw(30)
160 | elif command == -1:
161 | self.turn_cw(30)
162 | time.sleep(0.1)
163 | distance -= 2
164 | self.robot.send_command_hover(0, 0, 0, 0)
165 | return True, False
166 |
167 | def move_backward(self, distance: int) -> Tuple[bool, bool]:
168 | print(f"-> Moving backward {distance} cm")
169 | self.robot.send_command_hover(0, 0, 0, 0)
170 | while distance > 0:
171 | self.robot.send_command_hover(0, -self.move_speed_x, 0, 0)
172 | time.sleep(0.1)
173 | distance -= 2
174 | self.robot.send_command_hover(0, 0, 0, 0)
175 | return True, False
176 |
177 | def move_left(self, distance: int) -> Tuple[bool, bool]:
178 | print(f"-> Moving left {distance} cm")
179 | self.robot.send_command_hover(0, 0, 0, 0)
180 | while distance > 0:
181 | self.robot.send_command_hover(0, 0, -self.move_speed_y, 0)
182 | time.sleep(0.1)
183 | distance -= 2
184 | self.robot.send_command_hover(0, 0, 0, 0)
185 | return True, False
186 |
187 | def move_right(self, distance: int) -> Tuple[bool, bool]:
188 | print(f"-> Moving right {distance} cm")
189 | self.robot.send_command_hover(0, 0, 0, 0)
190 | while distance > 0:
191 | self.robot.send_command_hover(0, 0, self.move_speed_y, 0)
192 | time.sleep(0.1)
193 | distance -= 2
194 | self.robot.send_command_hover(0, 0, 0, 0)
195 | return True, False
196 |
197 | def move_up(self, distance: int) -> Tuple[bool, bool]:
198 | print(f"-> Moving up {distance} cm")
199 | return True, False
200 |
201 | def move_down(self, distance: int) -> Tuple[bool, bool]:
202 | print(f"-> Moving down {distance} cm")
203 | return True, False
204 |
205 | def turn_ccw(self, degree: int) -> Tuple[bool, bool]:
206 | print(f"-> Turning CCW {degree} degrees")
207 | self.robot.send_command_hover(0, 0, 0, 0)
208 | self.robot.send_command_position(0, 0, 0, degree)
209 | time.sleep(1 + degree / 50.0)
210 | self.robot.send_command_hover(0, 0, 0, 0)
211 | # if degree >= 90:
212 | # print("-> Turning CCW over 90 degrees")
213 | # return True, True
214 | return True, False
215 |
216 | def turn_cw(self, degree: int) -> Tuple[bool, bool]:
217 | print(f"-> Turning CW {degree} degrees")
218 | self.robot.send_command_hover(0, 0, 0, 0)
219 | self.robot.send_command_position(0, 0, 0, -degree)
220 | time.sleep(1 + degree / 50.0)
221 | self.robot.send_command_hover(0, 0, 0, 0)
222 | # if degree >= 90:
223 | # print("-> Turning CW over 90 degrees")
224 | # return True, True
225 | return True, False
226 |
227 | def move_in_circle(self, cw) -> Tuple[bool, bool]:
228 | if cw:
229 | vy = -8
230 | vr = -12
231 | else:
232 | vy = 8
233 | vr = 12
234 | for i in range(50):
235 | self.robot.send_command_hover(0, 0, vy, vr)
236 | time.sleep(0.1)
237 | self.robot.send_command_hover(0, 0, 0, 0)
238 | return True, False
239 |
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/controller/llm_controller.py:
--------------------------------------------------------------------------------
1 | from PIL import Image
2 | import queue, time, os, json
3 | from typing import Optional, Tuple
4 | import asyncio
5 | import uuid
6 | from enum import Enum
7 |
8 | from .shared_frame import SharedFrame, Frame
9 | from .yolo_client import YoloClient
10 | from .yolo_grpc_client import YoloGRPCClient
11 | from .tello_wrapper import TelloWrapper
12 | from .virtual_robot_wrapper import VirtualRobotWrapper
13 | from .abs.robot_wrapper import RobotWrapper
14 | from .vision_skill_wrapper import VisionSkillWrapper
15 | from .llm_planner import LLMPlanner
16 | from .skillset import SkillSet, LowLevelSkillItem, HighLevelSkillItem, SkillArg
17 | from .utils import print_t, input_t
18 | from .minispec_interpreter import MiniSpecInterpreter, Statement
19 | from .abs.robot_wrapper import RobotType
20 |
21 | CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
22 |
23 | class LLMController():
24 | def __init__(self, robot_type, use_http=False, message_queue: Optional[queue.Queue]=None):
25 | self.shared_frame = SharedFrame()
26 | if use_http:
27 | self.yolo_client = YoloClient(shared_frame=self.shared_frame)
28 | else:
29 | self.yolo_client = YoloGRPCClient(shared_frame=self.shared_frame)
30 | self.vision = VisionSkillWrapper(self.shared_frame)
31 | self.latest_frame = None
32 | self.controller_active = True
33 | self.controller_wait_takeoff = True
34 | self.message_queue = message_queue
35 | if message_queue is None:
36 | self.cache_folder = os.path.join(CURRENT_DIR, 'cache')
37 | else:
38 | self.cache_folder = message_queue.get()
39 |
40 | if not os.path.exists(self.cache_folder):
41 | os.makedirs(self.cache_folder)
42 |
43 | match robot_type:
44 | case RobotType.TELLO:
45 | print_t("[C] Start Tello drone...")
46 | self.drone: RobotWrapper = TelloWrapper()
47 | case RobotType.GEAR:
48 | print_t("[C] Start Gear robot car...")
49 | from .gear_wrapper import GearWrapper
50 | self.drone: RobotWrapper = GearWrapper()
51 | case _:
52 | print_t("[C] Start virtual drone...")
53 | self.drone: RobotWrapper = VirtualRobotWrapper()
54 |
55 | self.planner = LLMPlanner(robot_type)
56 |
57 | # load low-level skills
58 | self.low_level_skillset = SkillSet(level="low")
59 | self.low_level_skillset.add_skill(LowLevelSkillItem("move_forward", self.drone.move_forward, "Move forward by a distance", args=[SkillArg("distance", int)]))
60 | self.low_level_skillset.add_skill(LowLevelSkillItem("move_backward", self.drone.move_backward, "Move backward by a distance", args=[SkillArg("distance", int)]))
61 | self.low_level_skillset.add_skill(LowLevelSkillItem("move_left", self.drone.move_left, "Move left by a distance", args=[SkillArg("distance", int)]))
62 | self.low_level_skillset.add_skill(LowLevelSkillItem("move_right", self.drone.move_right, "Move right by a distance", args=[SkillArg("distance", int)]))
63 | self.low_level_skillset.add_skill(LowLevelSkillItem("move_up", self.drone.move_up, "Move up by a distance", args=[SkillArg("distance", int)]))
64 | self.low_level_skillset.add_skill(LowLevelSkillItem("move_down", self.drone.move_down, "Move down by a distance", args=[SkillArg("distance", int)]))
65 | self.low_level_skillset.add_skill(LowLevelSkillItem("turn_cw", self.drone.turn_cw, "Rotate clockwise/right by certain degrees", args=[SkillArg("degrees", int)]))
66 | self.low_level_skillset.add_skill(LowLevelSkillItem("turn_ccw", self.drone.turn_ccw, "Rotate counterclockwise/left by certain degrees", args=[SkillArg("degrees", int)]))
67 | self.low_level_skillset.add_skill(LowLevelSkillItem("delay", self.skill_delay, "Wait for specified seconds", args=[SkillArg("seconds", float)]))
68 | self.low_level_skillset.add_skill(LowLevelSkillItem("is_visible", self.vision.is_visible, "Check the visibility of target object", args=[SkillArg("object_name", str)]))
69 | self.low_level_skillset.add_skill(LowLevelSkillItem("object_x", self.vision.object_x, "Get object's X-coordinate in (0,1)", args=[SkillArg("object_name", str)]))
70 | self.low_level_skillset.add_skill(LowLevelSkillItem("object_y", self.vision.object_y, "Get object's Y-coordinate in (0,1)", args=[SkillArg("object_name", str)]))
71 | self.low_level_skillset.add_skill(LowLevelSkillItem("object_width", self.vision.object_width, "Get object's width in (0,1)", args=[SkillArg("object_name", str)]))
72 | self.low_level_skillset.add_skill(LowLevelSkillItem("object_height", self.vision.object_height, "Get object's height in (0,1)", args=[SkillArg("object_name", str)]))
73 | self.low_level_skillset.add_skill(LowLevelSkillItem("object_dis", self.vision.object_distance, "Get object's distance in cm", args=[SkillArg("object_name", str)]))
74 | self.low_level_skillset.add_skill(LowLevelSkillItem("probe", self.planner.probe, "Probe the LLM for reasoning", args=[SkillArg("question", str)]))
75 | self.low_level_skillset.add_skill(LowLevelSkillItem("log", self.skill_log, "Output text to console", args=[SkillArg("text", str)]))
76 | self.low_level_skillset.add_skill(LowLevelSkillItem("take_picture", self.skill_take_picture, "Take a picture"))
77 | self.low_level_skillset.add_skill(LowLevelSkillItem("re_plan", self.skill_re_plan, "Replanning"))
78 |
79 | self.low_level_skillset.add_skill(LowLevelSkillItem("goto", self.skill_goto, "goto the object", args=[SkillArg("object_name[*x-value]", str)]))
80 | self.low_level_skillset.add_skill(LowLevelSkillItem("time", self.skill_time, "Get current execution time", args=[]))
81 | # load high-level skills
82 | self.high_level_skillset = SkillSet(level="high", lower_level_skillset=self.low_level_skillset)
83 |
84 | type_folder_name = 'tello'
85 | if robot_type == RobotType.GEAR:
86 | type_folder_name = 'gear'
87 | with open(os.path.join(CURRENT_DIR, f"assets/{type_folder_name}/high_level_skills.json"), "r") as f:
88 | json_data = json.load(f)
89 | for skill in json_data:
90 | self.high_level_skillset.add_skill(HighLevelSkillItem.load_from_dict(skill))
91 |
92 | Statement.low_level_skillset = self.low_level_skillset
93 | Statement.high_level_skillset = self.high_level_skillset
94 | self.planner.init(high_level_skillset=self.high_level_skillset, low_level_skillset=self.low_level_skillset, vision_skill=self.vision)
95 |
96 | self.current_plan = None
97 | self.execution_history = None
98 | self.execution_time = time.time()
99 |
100 | def skill_time(self) -> Tuple[float, bool]:
101 | return time.time() - self.execution_time, False
102 |
103 | def skill_goto(self, object_name: str) -> Tuple[None, bool]:
104 | print(f'Goto {object_name}')
105 | if '[' in object_name:
106 | x = float(object_name.split('[')[1].split(']')[0])
107 | else:
108 | x = self.vision.object_x(object_name)[0]
109 |
110 | print(f'>> GOTO x {x} {type(x)}')
111 |
112 | if x > 0.55:
113 | self.drone.turn_cw(int((x - 0.5) * 70))
114 | elif x < 0.45:
115 | self.drone.turn_ccw(int((0.5 - x) * 70))
116 |
117 | self.drone.move_forward(110)
118 | return None, False
119 |
120 | def skill_take_picture(self) -> Tuple[None, bool]:
121 | img_path = os.path.join(self.cache_folder, f"{uuid.uuid4()}.jpg")
122 | Image.fromarray(self.latest_frame).save(img_path)
123 | print_t(f"[C] Picture saved to {img_path}")
124 | self.append_message((img_path,))
125 | return None, False
126 |
127 | def skill_log(self, text: str) -> Tuple[None, bool]:
128 | self.append_message(f"[LOG] {text}")
129 | print_t(f"[LOG] {text}")
130 | return None, False
131 |
132 | def skill_re_plan(self) -> Tuple[None, bool]:
133 | return None, True
134 |
135 | def skill_delay(self, s: float) -> Tuple[None, bool]:
136 | time.sleep(s)
137 | return None, False
138 |
139 | def append_message(self, message: str):
140 | if self.message_queue is not None:
141 | self.message_queue.put(message)
142 |
143 | def stop_controller(self):
144 | self.controller_active = False
145 |
146 | def get_latest_frame(self, plot=False):
147 | image = self.shared_frame.get_image()
148 | if plot and image:
149 | self.vision.update()
150 | YoloClient.plot_results_oi(image, self.vision.object_list)
151 | return image
152 |
153 | def execute_minispec(self, minispec: str):
154 | interpreter = MiniSpecInterpreter(self.message_queue)
155 | interpreter.execute(minispec)
156 | self.execution_history = interpreter.execution_history
157 | ret_val = interpreter.ret_queue.get()
158 | return ret_val
159 |
160 | def execute_task_description(self, task_description: str):
161 | if self.controller_wait_takeoff:
162 | self.append_message("[Warning] Controller is waiting for takeoff...")
163 | return
164 | self.append_message('[TASK]: ' + task_description)
165 | ret_val = None
166 | while True:
167 | self.current_plan = self.planner.plan(task_description, execution_history=self.execution_history)
168 | self.append_message(f'[Plan]: \\\\')
169 | try:
170 | self.execution_time = time.time()
171 | ret_val = self.execute_minispec(self.current_plan)
172 | except Exception as e:
173 | print_t(f"[C] Error: {e}")
174 |
175 | # disable replan for debugging
176 | break
177 | if ret_val.replan:
178 | print_t(f"[C] > Replanning <: {ret_val.value}")
179 | continue
180 | else:
181 | break
182 | self.append_message(f'\n[Task ended]')
183 | self.append_message('end')
184 | self.current_plan = None
185 | self.execution_history = None
186 |
187 | def start_robot(self):
188 | print_t("[C] Connecting to robot...")
189 | self.drone.connect()
190 | print_t("[C] Starting robot...")
191 | self.drone.takeoff()
192 | self.drone.move_up(25)
193 | print_t("[C] Starting stream...")
194 | self.drone.start_stream()
195 | self.controller_wait_takeoff = False
196 |
197 | def stop_robot(self):
198 | print_t("[C] Drone is landing...")
199 | self.drone.land()
200 | self.drone.stop_stream()
201 | self.controller_wait_takeoff = True
202 |
203 | def capture_loop(self, asyncio_loop):
204 | print_t("[C] Start capture loop...")
205 | frame_reader = self.drone.get_frame_reader()
206 | while self.controller_active:
207 | self.drone.keep_active()
208 | self.latest_frame = frame_reader.frame
209 | frame = Frame(frame_reader.frame,
210 | frame_reader.depth if hasattr(frame_reader, 'depth') else None)
211 |
212 | if self.yolo_client.is_local_service():
213 | self.yolo_client.detect_local(frame)
214 | else:
215 | # asynchronously send image to yolo server
216 | asyncio_loop.call_soon_threadsafe(asyncio.create_task, self.yolo_client.detect(frame))
217 | time.sleep(0.10)
218 | # Cancel all running tasks (if any)
219 | for task in asyncio.all_tasks(asyncio_loop):
220 | task.cancel()
221 | self.drone.stop_stream()
222 | self.drone.land()
223 | asyncio_loop.stop()
224 | print_t("[C] Capture loop stopped")
--------------------------------------------------------------------------------
/controller/llm_planner.py:
--------------------------------------------------------------------------------
1 | import os, ast
2 | from typing import Optional
3 |
4 | from .skillset import SkillSet
5 | from .llm_wrapper import LLMWrapper, GPT3, GPT4
6 | from .vision_skill_wrapper import VisionSkillWrapper
7 | from .utils import print_t
8 | from .minispec_interpreter import MiniSpecValueType, evaluate_value
9 | from .abs.robot_wrapper import RobotType
10 |
11 | CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
12 |
13 | class LLMPlanner():
14 | def __init__(self, robot_type: RobotType):
15 | self.llm = LLMWrapper()
16 | self.model_name = GPT4
17 |
18 | type_folder_name = 'tello'
19 | if robot_type == RobotType.GEAR:
20 | type_folder_name = 'gear'
21 |
22 | # read prompt from txt
23 | with open(os.path.join(CURRENT_DIR, f"./assets/{type_folder_name}/prompt_plan.txt"), "r") as f:
24 | self.prompt_plan = f.read()
25 |
26 | with open(os.path.join(CURRENT_DIR, f"./assets/{type_folder_name}/prompt_probe.txt"), "r") as f:
27 | self.prompt_probe = f.read()
28 |
29 | with open(os.path.join(CURRENT_DIR, f"./assets/{type_folder_name}/guides.txt"), "r") as f:
30 | self.guides = f.read()
31 |
32 | with open(os.path.join(CURRENT_DIR, f"./assets/{type_folder_name}/plan_examples.txt"), "r") as f:
33 | self.plan_examples = f.read()
34 |
35 | def set_model(self, model_name):
36 | self.model_name = model_name
37 |
38 | def init(self, high_level_skillset: SkillSet, low_level_skillset: SkillSet, vision_skill: VisionSkillWrapper):
39 | self.high_level_skillset = high_level_skillset
40 | self.low_level_skillset = low_level_skillset
41 | self.vision_skill = vision_skill
42 |
43 | def plan(self, task_description: str, scene_description: Optional[str] = None, error_message: Optional[str] = None, execution_history: Optional[str] = None):
44 | # by default, the task_description is an action
45 | if not task_description.startswith("["):
46 | task_description = "[A] " + task_description
47 |
48 | if scene_description is None:
49 | scene_description = self.vision_skill.get_obj_list()
50 | prompt = self.prompt_plan.format(system_skill_description_high=self.high_level_skillset,
51 | system_skill_description_low=self.low_level_skillset,
52 | guides=self.guides,
53 | plan_examples=self.plan_examples,
54 | error_message=error_message,
55 | scene_description=scene_description,
56 | task_description=task_description,
57 | execution_history=execution_history)
58 | print_t(f"[P] Planning request: {task_description}")
59 | return self.llm.request(prompt, self.model_name, stream=False)
60 |
61 | def probe(self, question: str) -> MiniSpecValueType:
62 | prompt = self.prompt_probe.format(scene_description=self.vision_skill.get_obj_list(), question=question)
63 | print_t(f"[P] Execution request: {question}")
64 | return evaluate_value(self.llm.request(prompt, self.model_name)), False
--------------------------------------------------------------------------------
/controller/llm_wrapper.py:
--------------------------------------------------------------------------------
1 | import os
2 | import openai
3 | from openai import Stream, ChatCompletion
4 |
5 | GPT3 = "gpt-3.5-turbo-16k"
6 | GPT4 = "gpt-4"
7 | LLAMA3 = "meta-llama/Meta-Llama-3-8B-Instruct"
8 |
9 | CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
10 | chat_log_path = os.path.join(CURRENT_DIR, "assets/chat_log.txt")
11 |
12 | class LLMWrapper:
13 | def __init__(self, temperature=0.0):
14 | self.temperature = temperature
15 | self.llama_client = openai.OpenAI(
16 | # base_url="http://10.66.41.78:8000/v1",
17 | base_url="http://localhost:8000/v1",
18 | api_key="token-abc123",
19 | )
20 | self.gpt_client = openai.OpenAI(
21 | api_key=os.environ.get("OPENAI_API_KEY"),
22 | )
23 |
24 | def request(self, prompt, model_name=GPT4, stream=False) -> str | Stream[ChatCompletion.ChatCompletionChunk]:
25 | if model_name == LLAMA3:
26 | client = self.llama_client
27 | else:
28 | client = self.gpt_client
29 |
30 | response = client.chat.completions.create(
31 | model=model_name,
32 | messages=[{"role": "user", "content": prompt}],
33 | temperature=self.temperature,
34 | stream=stream,
35 | )
36 |
37 | # save the message in a txt
38 | with open(chat_log_path, "a") as f:
39 | f.write(prompt + "\n---\n")
40 | if not stream:
41 | f.write(response.model_dump_json(indent=2) + "\n---\n")
42 |
43 | if stream:
44 | return response
45 |
46 | return response.choices[0].message.content
--------------------------------------------------------------------------------
/controller/minispec_interpreter.py:
--------------------------------------------------------------------------------
1 | from typing import List, Tuple, Union
2 | import re, queue
3 | from enum import Enum
4 | import time
5 | from typing import Optional
6 | from threading import Thread
7 | from queue import Queue
8 | from openai import ChatCompletion, Stream
9 | from .skillset import SkillSet
10 | from .utils import split_args, print_t
11 |
12 |
13 | def print_debug(*args):
14 | print(*args)
15 | # pass
16 |
17 | MiniSpecValueType = Union[int, float, bool, str, None]
18 |
19 | def evaluate_value(value: str) -> MiniSpecValueType:
20 | if value.isdigit():
21 | return int(value)
22 | elif value.replace('.', '', 1).isdigit():
23 | return float(value)
24 | elif value == 'True':
25 | return True
26 | elif value == 'False':
27 | return False
28 | elif value == 'None' or len(value) == 0:
29 | return None
30 | else:
31 | return value.strip('\'"')
32 |
33 | class MiniSpecReturnValue:
34 | def __init__(self, value: MiniSpecValueType, replan: bool):
35 | self.value = value
36 | self.replan = replan
37 |
38 | def from_tuple(t: Tuple[MiniSpecValueType, bool]):
39 | return MiniSpecReturnValue(t[0], t[1])
40 |
41 | def default():
42 | return MiniSpecReturnValue(None, False)
43 |
44 | def __repr__(self) -> str:
45 | return f'value={self.value}, replan={self.replan}'
46 |
47 | class ParsingState(Enum):
48 | CODE = 0
49 | ARGUMENTS = 1
50 | CONDITION = 2
51 | LOOP_COUNT = 3
52 | SUB_STATEMENTS = 4
53 |
54 | class MiniSpecProgram:
55 | def __init__(self, env: Optional[dict] = None, mq: queue.Queue = None) -> None:
56 | self.statements: List[Statement] = []
57 | self.depth = 0
58 | self.finished = False
59 | self.ret = False
60 | if env is None:
61 | self.env = {}
62 | else:
63 | self.env = env
64 | self.current_statement = Statement(self.env)
65 | self.mq = mq
66 |
67 | def parse(self, code_instance: Stream[ChatCompletion.ChatCompletionChunk] | List[str], exec: bool = False) -> bool:
68 | for chunk in code_instance:
69 | if isinstance(chunk, str):
70 | code = chunk
71 | else:
72 | code = chunk.choices[0].delta.content
73 | if code == None or len(code) == 0:
74 | continue
75 | if self.mq:
76 | self.mq.put(code + '\\\\')
77 | for c in code:
78 | if self.current_statement.parse(c, exec):
79 | if len(self.current_statement.action) > 0:
80 | print_debug("Adding statement: ", self.current_statement, exec)
81 | self.statements.append(self.current_statement)
82 | self.current_statement = Statement(self.env)
83 | if c == '{':
84 | self.depth += 1
85 | elif c == '}':
86 | if self.depth == 0:
87 | self.finished = True
88 | return True
89 | self.depth -= 1
90 | return False
91 |
92 | def eval(self) -> MiniSpecReturnValue:
93 | print_debug(f'Eval program: {self}, finished: {self.finished}')
94 | ret_val = MiniSpecReturnValue.default()
95 | count = 0
96 | while not self.finished:
97 | if len(self.statements) <= count:
98 | time.sleep(0.1)
99 | continue
100 | ret_val = self.statements[count].eval()
101 | if ret_val.replan or self.statements[count].ret:
102 | print_debug(f'RET from {self.statements[count]} with {ret_val} {self.statements[count].ret}')
103 | self.ret = True
104 | return ret_val
105 | count += 1
106 | if count < len(self.statements):
107 | for i in range(count, len(self.statements)):
108 | ret_val = self.statements[i].eval()
109 | if ret_val.replan or self.statements[i].ret:
110 | print_debug(f'RET from {self.statements[i]} with {ret_val} {self.statements[i].ret}')
111 | self.ret = True
112 | return ret_val
113 | return ret_val
114 |
115 | def __repr__(self) -> str:
116 | s = ''
117 | for statement in self.statements:
118 | s += f'{statement}; '
119 | return s
120 |
121 | class Statement:
122 | execution_queue: Queue['Statement'] = None
123 | low_level_skillset: SkillSet = None
124 | high_level_skillset: SkillSet = None
125 | def __init__(self, env: dict) -> None:
126 | self.code_buffer: str = ''
127 | self.parsing_state: ParsingState = ParsingState.CODE
128 | self.condition: Optional[str] = None
129 | self.loop_count: Optional[int] = None
130 | self.action: str = ''
131 | self.allow_digit: bool = False
132 | self.executable: bool = False
133 | self.ret: bool = False
134 | self.sub_statements: Optional[MiniSpecProgram] = None
135 | self.env = env
136 | self.read_argument: bool = False
137 |
138 | def get_env_value(self, var) -> MiniSpecValueType:
139 | if var not in self.env:
140 | raise Exception(f'Variable {var} is not defined')
141 | return self.env[var]
142 |
143 | def parse(self, code: str, exec: bool = False) -> bool:
144 | for c in code:
145 | match self.parsing_state:
146 | case ParsingState.CODE:
147 | if c == '?' and not self.read_argument:
148 | self.action = 'if'
149 | self.parsing_state = ParsingState.CONDITION
150 | elif c == ';' or c == '}' or c == ')':
151 | if c == ')':
152 | self.code_buffer += c
153 | self.read_argument = False
154 | self.action = self.code_buffer
155 | print_debug(f'SP Action: {self.code_buffer}')
156 | self.executable = True
157 | if exec and self.action != '':
158 | self.execution_queue.put(self)
159 | return True
160 | else:
161 | if c == '(':
162 | self.read_argument = True
163 | if c.isalpha() or c == '_':
164 | self.allow_digit = True
165 | self.code_buffer += c
166 | if c.isdigit() and not self.allow_digit:
167 | self.action = 'loop'
168 | self.parsing_state = ParsingState.LOOP_COUNT
169 | case ParsingState.CONDITION:
170 | if c == '{':
171 | print_debug(f'SP Condition: {self.code_buffer}')
172 | self.condition = self.code_buffer
173 | self.executable = True
174 | if exec:
175 | self.execution_queue.put(self)
176 | self.sub_statements = MiniSpecProgram(self.env)
177 | self.parsing_state = ParsingState.SUB_STATEMENTS
178 | else:
179 | self.code_buffer += c
180 | case ParsingState.LOOP_COUNT:
181 | if c == '{':
182 | print_debug(f'SP Loop: {self.code_buffer}')
183 | self.loop_count = int(self.code_buffer)
184 | self.executable = True
185 | if exec:
186 | self.execution_queue.put(self)
187 | self.sub_statements = MiniSpecProgram(self.env)
188 | self.parsing_state = ParsingState.SUB_STATEMENTS
189 | else:
190 | self.code_buffer += c
191 | case ParsingState.SUB_STATEMENTS:
192 | if self.sub_statements.parse([c]):
193 | return True
194 | return False
195 |
196 | def eval(self) -> MiniSpecReturnValue:
197 | print_debug(f'Statement eval: {self} {self.action} {self.condition} {self.loop_count}')
198 | while not self.executable:
199 | time.sleep(0.1)
200 | if self.action == 'if':
201 | ret_val = self.eval_condition(self.condition)
202 | if ret_val.replan:
203 | return ret_val
204 | if ret_val.value:
205 | print_debug(f'-> eval condition statement: {self.sub_statements}')
206 | ret_val = self.sub_statements.eval()
207 | if ret_val.replan or self.sub_statements.ret:
208 | self.ret = True
209 | return ret_val
210 | else:
211 | return MiniSpecReturnValue.default()
212 | elif self.action == 'loop':
213 | print_debug(f'-> eval loop statement: {self.loop_count} {self.sub_statements}')
214 | ret_val = MiniSpecReturnValue.default()
215 | for _ in range(self.loop_count):
216 | print_debug(f'-> loop iteration: {ret_val}')
217 | ret_val = self.sub_statements.eval()
218 | if ret_val.replan or self.sub_statements.ret:
219 | self.ret = True
220 | return ret_val
221 | return ret_val
222 | else:
223 | self.ret = False
224 | return self.eval_expr(self.action)
225 |
226 | # def eval_action(self, action: str) -> MiniSpecReturnValue:
227 | # action = action.strip()
228 | # print_debug(f'Eval action: {action}')
229 |
230 | # if '=' in action:
231 | # var, expr = action.split('=')
232 | # print_debug(f'Assignment: Var: {var.strip()}, Val: {expr.strip()}')
233 | # expr = expr.strip()
234 | # ret_val = self.eval_function(expr.strip())
235 | # if not ret_val.replan:
236 | # self.env[var.strip()] = ret_val.value
237 | # return ret_val
238 | # elif action.startswith('->'):
239 | # self.ret = True
240 | # return self.eval_expr(action.lstrip("->"))
241 | # else:
242 | # return self.eval_function(action)
243 |
244 | def eval_function(self, func: str) -> MiniSpecReturnValue:
245 | print_debug(f'Eval function: {func}')
246 | # append to execution state queue
247 | func = func.split('(', 1)
248 | name = func[0].strip()
249 | if len(func) == 2:
250 | args = func[1].strip()[:-1]
251 | args = split_args(args)
252 | for i in range(0, len(args)):
253 | args[i] = args[i].strip().strip('\'"')
254 | if args[i].startswith('_'):
255 | args[i] = self.get_env_value(args[i])
256 | else:
257 | args = []
258 |
259 | if name == 'int':
260 | return MiniSpecReturnValue(int(args[0]), False)
261 | elif name == 'float':
262 | return MiniSpecReturnValue(float(args[0]), False)
263 | elif name == 'str':
264 | return MiniSpecReturnValue(args[0], False)
265 | else:
266 | skill_instance = Statement.low_level_skillset.get_skill(name)
267 | if skill_instance is not None:
268 | print_debug(f'Executing low-level skill: {skill_instance.get_name()} {args}')
269 | return MiniSpecReturnValue.from_tuple(skill_instance.execute(args))
270 |
271 | skill_instance = Statement.high_level_skillset.get_skill(name)
272 | if skill_instance is not None:
273 | print_debug(f'Executing high-level skill: {skill_instance.get_name()}', args, skill_instance.execute(args))
274 | interpreter = MiniSpecProgram()
275 | interpreter.parse([skill_instance.execute(args)])
276 | interpreter.finished = True
277 | val = interpreter.eval()
278 | if val.value == 'rp':
279 | return MiniSpecReturnValue(f'High-level skill {skill_instance.get_name()} failed', True)
280 | return val
281 | raise Exception(f'Skill {name} is not defined')
282 |
283 | def eval_expr(self, var: str) -> MiniSpecReturnValue:
284 | print_t(f'Eval expr: {var}')
285 | var = var.strip()
286 | if var.startswith('->'):
287 | self.ret = True
288 | return MiniSpecReturnValue(self.eval_expr(var.lstrip('->')).value, True)
289 | if '=' in var:
290 |
291 | var, expr = var.split('=')
292 | print_t(f'Eval expr var assign: {var} {expr}')
293 | expr = expr.strip()
294 | ret_val = self.eval_expr(expr)
295 | # if not ret_val.replan:
296 | self.env[var] = ret_val.value
297 | return ret_val
298 | # deal with + - * / operators
299 | if '+' in var or '-' in var or '*' in var or '/' in var:
300 | if '+' in var:
301 | operands = var.split('+')
302 | val = 0
303 | for operand in operands:
304 | val += self.eval_expr(operand).value
305 | elif '-' in var:
306 | operands = var.split('-')
307 | val = self.eval_expr(operands[0]).value
308 | for operand in operands[1:]:
309 | val -= self.eval_expr(operand).value
310 | elif '*' in var:
311 | operands = var.split('*')
312 | val = 1
313 | for operand in operands:
314 | val *= self.eval_expr(operand).value
315 | elif '/' in var:
316 | operands = var.split('/')
317 | val = self.eval_expr(operands[0]).value
318 | for operand in operands[1:]:
319 | val /= self.eval_expr(operand).value
320 | return MiniSpecReturnValue(val, False)
321 |
322 | if len(var) == 0:
323 | raise Exception('Empty operand')
324 | if var.startswith('_'):
325 | return MiniSpecReturnValue(self.get_env_value(var), False)
326 | elif var == 'True' or var == 'False':
327 | return MiniSpecReturnValue(evaluate_value(var), False)
328 | elif var[0].isalpha():
329 | return self.eval_function(var)
330 | else:
331 | return MiniSpecReturnValue(evaluate_value(var), False)
332 |
333 | def eval_condition(self, condition: str) -> MiniSpecReturnValue:
334 | if '&' in condition:
335 | conditions = condition.split('&')
336 | cond = True
337 | for c in conditions:
338 | ret_val = self.eval_condition(c)
339 | if ret_val.replan:
340 | return ret_val
341 | cond = cond and ret_val.value
342 | return MiniSpecReturnValue(cond, False)
343 | if '|' in condition:
344 | conditions = condition.split('|')
345 | for c in conditions:
346 | ret_val = self.eval_condition(c)
347 | if ret_val.replan:
348 | return ret_val
349 | if ret_val.value == True:
350 | return MiniSpecReturnValue(True, False)
351 | return MiniSpecReturnValue(False, False)
352 |
353 | operand_1, comparator, operand_2 = re.split(r'(>|<|==|!=)', condition)
354 | operand_1 = self.eval_expr(operand_1)
355 | if operand_1.replan:
356 | return operand_1
357 | operand_2 = self.eval_expr(operand_2)
358 | if operand_2.replan:
359 | return operand_2
360 |
361 | print_debug(f'Condition ops: {operand_1.value} {comparator} {operand_2.value}')
362 | if type(operand_1.value) == int and type(operand_2.value) == float or \
363 | type(operand_1.value) == float and type(operand_2.value) == int:
364 | operand_1.value = float(operand_1.value)
365 | operand_2.value = float(operand_2.value)
366 |
367 | if type(operand_1.value) != type(operand_2.value):
368 | if comparator == '!=':
369 | return MiniSpecReturnValue(True, False)
370 | elif comparator == '==':
371 | return MiniSpecReturnValue(False, False)
372 | else:
373 | raise Exception(f'Invalid comparator: {operand_1.value}:{type(operand_1.value)} {operand_2.value}:{type(operand_2.value)}')
374 |
375 | if comparator == '>':
376 | cmp = operand_1.value > operand_2.value
377 | elif comparator == '<':
378 | cmp = operand_1.value < operand_2.value
379 | elif comparator == '==':
380 | cmp = operand_1.value == operand_2.value
381 | elif comparator == '!=':
382 | cmp = operand_1.value != operand_2.value
383 | else:
384 | raise Exception(f'Invalid comparator: {comparator}')
385 |
386 | return MiniSpecReturnValue(cmp, False)
387 |
388 | def __repr__(self) -> str:
389 | s = ''
390 | if self.action == 'if':
391 | s += f'if {self.condition}'
392 | elif self.action == 'loop':
393 | s += f'[{self.loop_count}]'
394 | else:
395 | s += f'{self.action}'
396 | if self.sub_statements is not None:
397 | s += ' {'
398 | for statement in self.sub_statements.statements:
399 | s += f'{statement}; '
400 | s += '}'
401 | return s
402 |
403 | class MiniSpecInterpreter:
404 | def __init__(self, message_queue: queue.Queue):
405 | self.env = {}
406 | self.ret = False
407 | self.code_buffer: str = ''
408 | self.execution_history = []
409 | if Statement.low_level_skillset is None or \
410 | Statement.high_level_skillset is None:
411 | raise Exception('Statement: Skillset is not initialized')
412 |
413 | Statement.execution_queue = Queue()
414 | self.execution_thread = Thread(target=self.executor)
415 | self.execution_thread.start()
416 |
417 | self.timestamp_get_plan = None
418 | self.timestamp_start_execution = None
419 | self.timestamp_end_execution = None
420 | self.program_count = 0
421 | self.ret_queue = Queue()
422 | self.message_queue = message_queue
423 |
424 | def execute(self, code: Stream[ChatCompletion.ChatCompletionChunk] | List[str]) -> MiniSpecReturnValue:
425 | print_t(f'>>> Get a stream')
426 | self.execution_history = []
427 | self.timestamp_get_plan = time.time()
428 | program = MiniSpecProgram(mq=self.message_queue)
429 | program.parse(code, True)
430 | self.program_count = len(program.statements)
431 | t2 = time.time()
432 | print_t(">>> Program: ", program, "Time: ", t2 - self.timestamp_get_plan)
433 |
434 | def executor(self):
435 | while True:
436 | if not Statement.execution_queue.empty():
437 | if self.timestamp_start_execution is None:
438 | self.timestamp_start_execution = time.time()
439 | print_t(">>> Start execution")
440 | statement = Statement.execution_queue.get()
441 | print_debug(f'Queue get statement: {statement}')
442 | ret_val = statement.eval()
443 | print_t(f'Queue statement done: {statement}')
444 | if statement.ret:
445 | while not Statement.execution_queue.empty():
446 | Statement.execution_queue.get()
447 | self.ret_queue.put(ret_val)
448 | return
449 | self.execution_history.append(statement)
450 | # if ret_val.replan:
451 | # print_t(f'Queue statement replan: {statement}')
452 | # self.ret_queue.put(ret_val)
453 | # return
454 | self.program_count -= 1
455 | if self.program_count == 0:
456 | self.timestamp_end_execution = time.time()
457 | print_t(f'>>> Execution time: {self.timestamp_end_execution - self.timestamp_start_execution}')
458 | self.timestamp_start_execution = None
459 | self.ret_queue.put(ret_val)
460 | return
461 | else:
462 | time.sleep(0.005)
463 |
--------------------------------------------------------------------------------
/controller/shared_frame.py:
--------------------------------------------------------------------------------
1 | from PIL import Image
2 | from typing import Optional
3 | from numpy.typing import NDArray
4 | import numpy as np
5 | import threading
6 | import time
7 |
8 | class Frame():
9 | def __init__(self, image: Image.Image | NDArray[np.uint8]=None, depth: Optional[NDArray[np.int16]]=None):
10 | if image is None:
11 | self._image_buffer = np.zeros((352, 640, 3), dtype=np.uint8)
12 | self._image = Image.fromarray(self._image_buffer)
13 | if isinstance(image, np.ndarray):
14 | self._image_buffer = image
15 | self._image = Image.fromarray(image)
16 | elif isinstance(image, Image.Image):
17 | self._image = image
18 | self._image_buffer = np.array(image)
19 | self._depth = depth
20 |
21 | @property
22 | def image(self) -> Image.Image:
23 | return self._image
24 |
25 | @property
26 | def depth(self) -> Optional[NDArray[np.int16]]:
27 | return self._depth
28 |
29 | @image.setter
30 | def image(self, image: Image.Image):
31 | self._image = image
32 | self._image_buffer = np.array(image)
33 |
34 | @depth.setter
35 | def depth(self, depth: Optional[NDArray[np.int16]]):
36 | self._depth = depth
37 |
38 | @property
39 | def image_buffer(self) -> NDArray[np.uint8]:
40 | return self._image_buffer
41 |
42 | @image_buffer.setter
43 | def image_buffer(self, image_buffer: NDArray[np.uint8]):
44 | self._image_buffer = image_buffer
45 | self._image = Image.fromarray(image_buffer)
46 |
47 | class SharedFrame():
48 | def __init__(self):
49 | self.timestamp = 0
50 | self.frame = Frame()
51 | self.yolo_result = {}
52 | self.lock = threading.Lock()
53 |
54 | def get_image(self) -> Optional[Image.Image]:
55 | with self.lock:
56 | return self.frame.image
57 |
58 | def get_yolo_result(self) -> dict:
59 | with self.lock:
60 | return self.yolo_result
61 |
62 | def get_depth(self) -> Optional[NDArray[np.int16]]:
63 | with self.lock:
64 | return self.frame.depth
65 |
66 | def set(self, frame: Frame, yolo_result: dict):
67 | with self.lock:
68 | self.frame = frame
69 | self.timestamp = time.time()
70 | self.yolo_result = yolo_result
--------------------------------------------------------------------------------
/controller/skillset.py:
--------------------------------------------------------------------------------
1 | import re
2 | from enum import Enum
3 | from typing import Optional, List, Union
4 | from .abs.skill_item import SkillItem, SkillArg
5 |
6 | class SkillSetLevel(Enum):
7 | LOW = "low"
8 | HIGH = "high"
9 |
10 | class SkillSet():
11 | def __init__(self, level = "low", lower_level_skillset: 'SkillSet' = None):
12 | self.skills = {}
13 | self.level = SkillSetLevel(level)
14 | self.lower_level_skillset = lower_level_skillset
15 |
16 | def get_skill(self, skill_name: str) -> Optional[SkillItem]:
17 | """Returns a SkillItem by its name or abbr."""
18 | skill = None
19 | if skill_name in self.skills:
20 | skill = self.skills[skill_name]
21 | elif skill_name in SkillItem.abbr_dict:
22 | skill = self.skills.get(SkillItem.abbr_dict[skill_name])
23 | return skill
24 |
25 | def add_skill(self, skill_item: SkillItem):
26 | """Adds a SkillItem to the set."""
27 | if skill_item.skill_name in self.skills:
28 | raise ValueError(f"A skill with the name '{skill_item.skill_name}' already exists.")
29 | # Set the low-level skillset for high-level skills
30 | if self.level == SkillSetLevel.HIGH and isinstance(skill_item, HighLevelSkillItem):
31 | if self.lower_level_skillset is not None:
32 | skill_item.set_skillset(self.lower_level_skillset, self)
33 | else:
34 | raise ValueError("Low-level skillset is not set.")
35 |
36 | self.skills[skill_item.skill_name] = skill_item
37 |
38 | def remove_skill(self, skill_name: str):
39 | """Removes a SkillItem from the set by its name."""
40 | if skill_name not in self.skills:
41 | raise ValueError(f"No skill found with the name '{skill_name}'.")
42 | # remove skill by value
43 | del self.skills[skill_name]
44 |
45 | def __repr__(self) -> str:
46 | string = ""
47 | for skill in self.skills.values():
48 | string += f"{skill}\n"
49 | return string
50 |
51 | class LowLevelSkillItem(SkillItem):
52 | def __init__(self, skill_name: str, skill_callable: callable,
53 | skill_description: str = "", args: List[SkillArg] = []):
54 | self.skill_name = skill_name
55 | self.abbr = self.generate_abbreviation(skill_name)
56 | self.abbr_dict[self.abbr] = skill_name
57 | self.skill_callable = skill_callable
58 | self.skill_description = skill_description
59 | self.args = args
60 |
61 | def get_name(self) -> str:
62 | return self.skill_name
63 |
64 | def get_skill_description(self) -> str:
65 | return self.skill_description
66 |
67 | def get_argument(self) -> List[SkillArg]:
68 | return self.args
69 |
70 | def execute(self, arg_list: List[Union[int, float, str]]):
71 | """Executes the skill with the provided arguments."""
72 | if callable(self.skill_callable):
73 | parsed_args = self.parse_args(arg_list)
74 | return self.skill_callable(*parsed_args)
75 | else:
76 | raise ValueError(f"'{self.skill_callable}' is not a callable function.")
77 |
78 | def __repr__(self) -> str:
79 | return (f"abbr:{self.abbr},"
80 | f"name:{self.skill_name},"
81 | f"args:{[arg for arg in self.args]},"
82 | f"description:{self.skill_description}")
83 |
84 | class HighLevelSkillItem(SkillItem):
85 | def __init__(self, skill_name: str, definition: str,
86 | skill_description: str = ""):
87 | self.skill_name = skill_name
88 | self.abbr = self.generate_abbreviation(skill_name)
89 | self.abbr_dict[self.abbr] = skill_name
90 | self.definition = definition
91 | self.skill_description = skill_description
92 | self.low_level_skillset = None
93 | self.args = []
94 |
95 | def load_from_dict(skill_dict: dict):
96 | return HighLevelSkillItem(skill_dict["skill_name"], skill_dict["definition"], skill_dict["skill_description"])
97 |
98 | def get_name(self) -> str:
99 | return self.skill_name
100 |
101 | def get_skill_description(self) -> str:
102 | return self.skill_description
103 |
104 | def get_argument(self) -> List[SkillArg]:
105 | return self.args
106 |
107 | def set_skillset(self, low_level_skillset: SkillSet, high_level_skillset: SkillSet):
108 | self.low_level_skillset = low_level_skillset
109 | self.high_level_skillset = high_level_skillset
110 | self.args = self.generate_argument_list()
111 |
112 | def generate_argument_list(self) -> List[SkillArg]:
113 | # Extract all skill calls with their arguments from the code
114 | skill_calls = re.findall(r'(\w+)\(([^)]*)\)', self.definition)
115 |
116 | arg_types = {}
117 |
118 | for skill_name, args in skill_calls:
119 | args = [a.strip() for a in args.split(',')]
120 | if skill_name == "int":
121 | function_args = [SkillArg("value", int)]
122 | elif skill_name == "float":
123 | function_args = [SkillArg("value", float)]
124 | elif skill_name == "str":
125 | function_args = [SkillArg("value", str)]
126 | else:
127 | skill = self.low_level_skillset.get_skill(skill_name)
128 | if skill is None:
129 | skill = self.high_level_skillset.get_skill(skill_name)
130 |
131 | if skill is None:
132 | raise ValueError(f"Skill '{skill_name}' not found in the low-level or high-level skillset.")
133 |
134 | function_args = skill.get_argument()
135 | for i, arg in enumerate(args):
136 | if arg.startswith('$') and arg not in arg_types:
137 | # Match the positional argument with its type from the function definition
138 | arg_types[arg] = function_args[i]
139 |
140 | # Convert the mapped arguments to a user-friendly list in order of $position
141 | arg_types = dict(sorted(arg_types.items()))
142 | arg_list = [arg for arg in arg_types.values()]
143 |
144 | return arg_list
145 |
146 | def execute(self, arg_list: List[Union[int, float, str]]):
147 | """Executes the skill with the provided arguments."""
148 | if self.low_level_skillset is None:
149 | raise ValueError("Low-level skillset is not set.")
150 | if len(arg_list) != len(self.args):
151 | raise ValueError(f"Expected {len(self.args)} arguments, but got {len(arg_list)}.")
152 | # replace all $1, $2, ... with segments
153 | definition = self.definition
154 | for i in range(0, len(arg_list)):
155 | definition = definition.replace(f"${i + 1}", arg_list[i])
156 | return definition
157 |
158 | def __repr__(self) -> str:
159 | return (f"abbr:{self.abbr},"
160 | f"name:{self.skill_name},"
161 | f"definition:{self.definition},"
162 | f"args:{[arg for arg in self.args]},"
163 | f"description:{self.skill_description}")
--------------------------------------------------------------------------------
/controller/tello_wrapper.py:
--------------------------------------------------------------------------------
1 | import time, cv2
2 | import numpy as np
3 | from typing import Tuple
4 | from djitellopy import Tello
5 |
6 | from .abs.robot_wrapper import RobotWrapper
7 |
8 | import logging
9 | Tello.LOGGER.setLevel(logging.WARNING)
10 |
11 | MOVEMENT_MIN = 20
12 | MOVEMENT_MAX = 300
13 |
14 | SCENE_CHANGE_DISTANCE = 120
15 | SCENE_CHANGE_ANGLE = 90
16 |
17 | def adjust_exposure(img, alpha=1.0, beta=0):
18 | """
19 | Adjust the exposure of an image.
20 |
21 | :param img: Input image
22 | :param alpha: Contrast control (1.0-3.0). Higher values increase exposure.
23 | :param beta: Brightness control (0-100). Higher values add brightness.
24 | :return: Exposure adjusted image
25 | """
26 | # Apply exposure adjustment using the formula: new_img = img * alpha + beta
27 | new_img = cv2.convertScaleAbs(img, alpha=alpha, beta=beta)
28 | return new_img
29 |
30 | def sharpen_image(img):
31 | """
32 | Apply a sharpening filter to an image.
33 |
34 | :param img: Input image
35 | :return: Sharpened image
36 | """
37 | # Define a sharpening kernel
38 | kernel = np.array([[0, -1, 0],
39 | [-1, 5, -1],
40 | [0, -1, 0]])
41 |
42 | # Apply the sharpening filter
43 | sharpened = cv2.filter2D(img, -1, kernel)
44 | return sharpened
45 |
46 | class FrameReader:
47 | def __init__(self, fr):
48 | # Initialize the video capture
49 | self.fr = fr
50 |
51 | @property
52 | def frame(self):
53 | # Read a frame from the video capture
54 | frame = self.fr.frame
55 | frame = adjust_exposure(frame, alpha=1.3, beta=-30)
56 | return sharpen_image(frame)
57 |
58 | def cap_distance(distance):
59 | if distance < MOVEMENT_MIN:
60 | return MOVEMENT_MIN
61 | elif distance > MOVEMENT_MAX:
62 | return MOVEMENT_MAX
63 | return distance
64 |
65 | class TelloWrapper(RobotWrapper):
66 | def __init__(self):
67 | self.drone = Tello()
68 | self.active_count = 0
69 | self.stream_on = False
70 |
71 | def keep_active(self):
72 | if self.active_count % 20 == 0:
73 | self.drone.send_control_command("command")
74 | self.active_count += 1
75 |
76 | def connect(self):
77 | self.drone.connect()
78 |
79 | def takeoff(self) -> bool:
80 | if not self.is_battery_good():
81 | return False
82 | else:
83 | self.drone.takeoff()
84 | return True
85 |
86 | def land(self):
87 | self.drone.land()
88 |
89 | def start_stream(self):
90 | self.stream_on = True
91 | self.drone.streamon()
92 |
93 | def stop_stream(self):
94 | self.stream_on = False
95 | self.drone.streamoff()
96 |
97 | def get_frame_reader(self):
98 | if not self.stream_on:
99 | return None
100 | return FrameReader(self.drone.get_frame_read())
101 |
102 | def move_forward(self, distance: int) -> Tuple[bool, bool]:
103 | self.drone.move_forward(cap_distance(distance))
104 | self.movement_x_accumulator += distance
105 | time.sleep(0.5)
106 | return True, distance > SCENE_CHANGE_DISTANCE
107 |
108 | def move_backward(self, distance: int) -> Tuple[bool, bool]:
109 | self.drone.move_back(cap_distance(distance))
110 | self.movement_x_accumulator -= distance
111 | time.sleep(0.5)
112 | return True, distance > SCENE_CHANGE_DISTANCE
113 |
114 | def move_left(self, distance: int) -> Tuple[bool, bool]:
115 | self.drone.move_left(cap_distance(distance))
116 | self.movement_y_accumulator += distance
117 | time.sleep(0.5)
118 | return True, distance > SCENE_CHANGE_DISTANCE
119 |
120 | def move_right(self, distance: int) -> Tuple[bool, bool]:
121 | self.drone.move_right(cap_distance(distance))
122 | self.movement_y_accumulator -= distance
123 | time.sleep(0.5)
124 | return True, distance > SCENE_CHANGE_DISTANCE
125 |
126 | def move_up(self, distance: int) -> Tuple[bool, bool]:
127 | self.drone.move_up(cap_distance(distance))
128 | time.sleep(0.5)
129 | return True, False
130 |
131 | def move_down(self, distance: int) -> Tuple[bool, bool]:
132 | self.drone.move_down(cap_distance(distance))
133 | time.sleep(0.5)
134 | return True, False
135 |
136 | def turn_ccw(self, degree: int) -> Tuple[bool, bool]:
137 | self.drone.rotate_counter_clockwise(degree)
138 | self.rotation_accumulator += degree
139 | time.sleep(1)
140 | # return True, degree > SCENE_CHANGE_ANGLE
141 | return True, False
142 |
143 | def turn_cw(self, degree: int) -> Tuple[bool, bool]:
144 | self.drone.rotate_clockwise(degree)
145 | self.rotation_accumulator -= degree
146 | time.sleep(1)
147 | # return True, degree > SCENE_CHANGE_ANGLE
148 | return True, False
149 |
150 | def is_battery_good(self) -> bool:
151 | self.battery = self.drone.query_battery()
152 | print(f"> Battery level: {self.battery}% ", end='')
153 | if self.battery < 20:
154 | print('is too low [WARNING]')
155 | else:
156 | print('[OK]')
157 | return True
158 | return False
--------------------------------------------------------------------------------
/controller/utils.py:
--------------------------------------------------------------------------------
1 | from typing import Union
2 | import datetime
3 |
4 | def print_t(*args, **kwargs):
5 | # Get the current timestamp
6 | current_time = datetime.datetime.now().strftime('%H:%M:%S.%f')[:-3]
7 |
8 | # Use built-in print to display the timestamp followed by the message
9 | print(f"[{current_time}]", *args, **kwargs)
10 |
11 | def input_t(literal):
12 | # Get the current timestamp
13 | current_time = datetime.datetime.now().strftime('%H:%M:%S.%f')[:-3]
14 |
15 | # Use built-in print to display the timestamp followed by the message
16 | return input(f"[{current_time}] {literal}")
17 |
18 | def split_args(arg_str: str) -> list[str]:
19 | args = []
20 | current_arg = ''
21 | parentheses_count = 0 # Keep track of open parentheses
22 |
23 | if arg_str.startswith('\'') and arg_str.endswith('\''):
24 | args.append(arg_str)
25 | return args
26 |
27 | for char in arg_str:
28 | if char == ',' and parentheses_count == 0:
29 | # If we encounter a comma and we're not inside parentheses, split here
30 | args.append(current_arg.strip())
31 | current_arg = ''
32 | else:
33 | # Otherwise, keep adding characters to the current argument
34 | if char == '(':
35 | parentheses_count += 1
36 | elif char == ')':
37 | parentheses_count -= 1
38 | current_arg += char
39 |
40 | # Don't forget to add the last argument after the loop finishes
41 | if current_arg:
42 | args.append(current_arg.strip())
43 |
44 | return args
--------------------------------------------------------------------------------
/controller/virtual_robot_wrapper.py:
--------------------------------------------------------------------------------
1 | import cv2, time
2 | from typing import Tuple
3 | from .abs.robot_wrapper import RobotWrapper
4 |
5 | class FrameReader:
6 | def __init__(self, cap):
7 | # Initialize the video capture
8 | self.cap = cap
9 | if not self.cap.isOpened():
10 | raise ValueError("Could not open video device")
11 |
12 | @property
13 | def frame(self):
14 | # Read a frame from the video capture
15 | ret, frame = self.cap.read()
16 | if not ret:
17 | raise ValueError("Could not read frame")
18 | return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
19 |
20 | class VirtualRobotWrapper(RobotWrapper):
21 | def __init__(self):
22 | self.stream_on = False
23 | pass
24 |
25 | def keep_active(self):
26 | pass
27 |
28 | def connect(self):
29 | pass
30 |
31 | def takeoff(self) -> bool:
32 | return True
33 |
34 | def land(self):
35 | pass
36 |
37 | def start_stream(self):
38 | self.cap = cv2.VideoCapture(0)
39 | self.stream_on = True
40 |
41 | def stop_stream(self):
42 | self.cap.release()
43 | self.stream_on = False
44 |
45 | def get_frame_reader(self):
46 | if not self.stream_on:
47 | return None
48 | return FrameReader(self.cap)
49 |
50 | def move_forward(self, distance: int) -> Tuple[bool, bool]:
51 | print(f"-> Moving forward {distance} cm")
52 | self.movement_x_accumulator += distance
53 | time.sleep(1)
54 | return True, False
55 |
56 | def move_backward(self, distance: int) -> Tuple[bool, bool]:
57 | print(f"-> Moving backward {distance} cm")
58 | self.movement_x_accumulator -= distance
59 | time.sleep(1)
60 | return True, False
61 |
62 | def move_left(self, distance: int) -> Tuple[bool, bool]:
63 | print(f"-> Moving left {distance} cm")
64 | self.movement_y_accumulator += distance
65 | time.sleep(1)
66 | return True, False
67 |
68 | def move_right(self, distance: int) -> Tuple[bool, bool]:
69 | print(f"-> Moving right {distance} cm")
70 | self.movement_y_accumulator -= distance
71 | time.sleep(1)
72 | return True, False
73 |
74 | def move_up(self, distance: int) -> Tuple[bool, bool]:
75 | print(f"-> Moving up {distance} cm")
76 | time.sleep(1)
77 | return True, False
78 |
79 | def move_down(self, distance: int) -> Tuple[bool, bool]:
80 | print(f"-> Moving down {distance} cm")
81 | time.sleep(1)
82 | return True, False
83 |
84 | def turn_ccw(self, degree: int) -> Tuple[bool, bool]:
85 | print(f"-> Turning CCW {degree} degrees")
86 | self.rotation_accumulator += degree
87 | if degree >= 90:
88 | print("-> Turning CCW over 90 degrees")
89 | return True, False
90 | time.sleep(1)
91 | return True, False
92 |
93 | def turn_cw(self, degree: int) -> Tuple[bool, bool]:
94 | print(f"-> Turning CW {degree} degrees")
95 | self.rotation_accumulator -= degree
96 | if degree >= 90:
97 | print("-> Turning CW over 90 degrees")
98 | return True, False
99 | time.sleep(1)
100 | return True, False
--------------------------------------------------------------------------------
/controller/vision_skill_wrapper.py:
--------------------------------------------------------------------------------
1 | from typing import Union, Tuple, Optional
2 | import numpy as np
3 | import time, math
4 | import cv2
5 | from filterpy.kalman import KalmanFilter
6 | from .shared_frame import SharedFrame
7 |
8 | def iou(boxA, boxB):
9 | # Calculate the intersection over union (IoU) of two bounding boxes
10 | xA = max(boxA['x1'], boxB['x1'])
11 | yA = max(boxA['y1'], boxB['y1'])
12 | xB = min(boxA['x2'], boxB['x2'])
13 | yB = min(boxA['y2'], boxB['y2'])
14 |
15 | # Compute the area of intersection rectangle
16 | interArea = max(0, xB - xA) * max(0, yB - yA)
17 |
18 | # Compute the area of both the prediction and ground-truth rectangles
19 | boxAArea = (boxA['x2'] - boxA['x1']) * (boxA['y2'] - boxA['y1'])
20 | boxBArea = (boxB['x2'] - boxB['x1']) * (boxB['y2'] - boxB['y1'])
21 |
22 | # Compute the intersection over union
23 | iou = interArea / float(boxAArea + boxBArea - interArea)
24 |
25 | return iou
26 |
27 | def euclidean_distance(boxA, boxB):
28 | centerA = ((boxA['x1'] + boxA['x2']) / 2, (boxA['y1'] + boxA['y2']) / 2)
29 | centerB = ((boxB['x1'] + boxB['x2']) / 2, (boxB['y1'] + boxB['y2']) / 2)
30 | return math.sqrt((centerA[0] - centerB[0])**2 + (centerA[1] - centerB[1])**2)
31 |
32 |
33 | class ObjectInfo:
34 | def __init__(self, name, x, y, w, h) -> None:
35 | self.name = name
36 | self.x = float(x)
37 | self.y = float(y)
38 | self.w = float(w)
39 | self.h = float(h)
40 |
41 | def __str__(self) -> str:
42 | return f"{self.name} x:{self.x:.2f} y:{self.y:.2f} width:{self.w:.2f} height:{self.h:.2f}"
43 |
44 | class ObjectTracker:
45 | def __init__(self, name, x, y, w, h) -> None:
46 | self.name = name
47 | self.kf_pos = self.init_filter()
48 | self.kf_siz = self.init_filter()
49 | self.timestamp = 0
50 | self.size = None
51 | self.update(x, y, w, h)
52 |
53 | def update(self, x, y, w, h):
54 | self.kf_pos.update((x, y))
55 | self.kf_siz.update((w, h))
56 | self.timestamp = time.time()
57 |
58 | def predict(self) -> Optional[ObjectInfo]:
59 | # if no update in 2 seconds, return None
60 | if time.time() - self.timestamp > 0.5:
61 | return None
62 | self.kf_pos.predict()
63 | self.kf_siz.predict()
64 | return ObjectInfo(self.name, self.kf_pos.x[0][0], self.kf_pos.x[1][0], self.kf_siz.x[0][0], self.kf_siz.x[1][0])
65 |
66 | def init_filter(self):
67 | kf = KalmanFilter(dim_x=4, dim_z=2) # 4 state dimensions (x, y, vx, vy), 2 measurement dimensions (x, y)
68 | kf.F = np.array([[1, 0, 1, 0], # State transition matrix
69 | [0, 1, 0, 1],
70 | [0, 0, 1, 0],
71 | [0, 0, 0, 1]])
72 | kf.H = np.array([[1, 0, 0, 0], # Measurement function
73 | [0, 1, 0, 0]])
74 | kf.R *= 1 # Measurement uncertainty
75 | kf.P *= 1000 # Initial uncertainty
76 | kf.Q *= 0.01 # Process uncertainty
77 | return kf
78 |
79 | class VisionSkillWrapper():
80 | def __init__(self, shared_frame: SharedFrame):
81 | self.shared_frame = shared_frame
82 | self.last_update = 0
83 | self.object_trackers: dict[str, ObjectTracker] = {}
84 | self.object_list = []
85 | self.aruco_detector = cv2.aruco.ArucoDetector(
86 | cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_4X4_250),
87 | cv2.aruco.DetectorParameters())
88 |
89 | def update(self):
90 | if self.shared_frame.timestamp == self.last_update:
91 | return
92 | self.last_update = self.shared_frame.timestamp
93 | self.object_list = []
94 | objs = self.shared_frame.get_yolo_result()['result']
95 | for obj in objs:
96 | name = obj['name']
97 | box = obj['box']
98 | x = (box['x1'] + box['x2']) / 2
99 | y = (box['y1'] + box['y2']) / 2
100 | w = box['x2'] - box['x1']
101 | h = box['y2'] - box['y1']
102 | self.object_list.append(ObjectInfo(name, x, y, w, h))
103 | def _update(self):
104 | if self.shared_frame.timestamp == self.last_update:
105 | return
106 | self.last_update = self.shared_frame.timestamp
107 |
108 | objs = self.shared_frame.get_yolo_result()['result']
109 |
110 | updated_trackers = {}
111 |
112 | for obj in objs:
113 | name = obj['name']
114 | box = obj['box']
115 | x = (box['x1'] + box['x2']) / 2
116 | y = (box['y1'] + box['y2']) / 2
117 | w = box['x2'] - box['x1']
118 | h = box['y2'] - box['y1']
119 |
120 | best_match_key = None
121 | best_match_distance = float('inf')
122 |
123 | # Find the best matching tracker
124 | for key, tracker in self.object_trackers.items():
125 | if tracker.name == name:
126 | existing_box = {
127 | 'x1': tracker.kf_pos.x[0][0] - tracker.kf_siz.x[0][0] / 2,
128 | 'y1': tracker.kf_pos.x[1][0] - tracker.kf_siz.x[1][0] / 2,
129 | 'x2': tracker.kf_pos.x[0][0] + tracker.kf_siz.x[0][0] / 2,
130 | 'y2': tracker.kf_pos.x[1][0] + tracker.kf_siz.x[1][0] / 2,
131 | }
132 | distance = euclidean_distance(existing_box, box)
133 | if distance < best_match_distance:
134 | best_match_distance = distance
135 | best_match_key = key
136 |
137 | # Update the best matching tracker or create a new one
138 | if best_match_key is not None and best_match_distance < 50: # Threshold can be adjusted
139 | self.object_trackers[best_match_key].update(x, y, w, h)
140 | updated_trackers[best_match_key] = self.object_trackers[best_match_key]
141 | else:
142 | new_key = f"{name}_{len(self.object_trackers)}" # Create a unique key
143 | updated_trackers[new_key] = ObjectTracker(name, x, y, w, h)
144 |
145 | # Replace the old trackers with the updated ones
146 | self.object_trackers = updated_trackers
147 |
148 | # Create the list of current objects
149 | self.object_list = []
150 | to_delete = []
151 | for key, tracker in self.object_trackers.items():
152 | obj = tracker.predict()
153 | if obj is not None:
154 | self.object_list.append(obj)
155 | else:
156 | to_delete.append(key)
157 |
158 | # Remove trackers that should be deleted
159 | for key in to_delete:
160 | del self.object_trackers[key]
161 | # def update(self):
162 | # if self.shared_frame.timestamp == self.last_update:
163 | # return
164 | # self.last_update = self.shared_frame.timestamp
165 | # objs = self.shared_frame.get_yolo_result()['result'] + self.shared_frame.get_yolo_result()['result_custom']
166 | # for obj in objs:
167 | # name = obj['name']
168 | # box = obj['box']
169 | # x = (box['x1'] + box['x2']) / 2
170 | # y = (box['y1'] + box['y2']) / 2
171 | # w = box['x2'] - box['x1']
172 | # h = box['y2'] - box['y1']
173 | # if name not in self.object_trackers:
174 | # self.object_trackers[name] = ObjectTracker(name, x, y, w, h)
175 | # else:
176 | # self.object_trackers[name].update(x, y, w, h)
177 |
178 | # self.object_list = []
179 | # to_delete = []
180 | # for name, tracker in self.object_trackers.items():
181 | # obj = tracker.predict()
182 | # if obj is not None:
183 | # self.object_list.append(obj)
184 | # else:
185 | # to_delete.append(name)
186 | # for name in to_delete:
187 | # del self.object_trackers[name]
188 |
189 | def get_obj_list(self) -> str:
190 | self.update()
191 | str_list = []
192 | for obj in self.object_list:
193 | str_list.append(str(obj))
194 | return str(str_list).replace("'", '')
195 |
196 | def get_obj_info(self, object_name: str) -> ObjectInfo:
197 | for _ in range(10):
198 | self.update()
199 | for obj in self.object_list:
200 | if obj.name.startswith(object_name):
201 | return obj
202 | time.sleep(0.2)
203 | return None
204 |
205 | def is_visible(self, object_name: str) -> Tuple[bool, bool]:
206 | return self.get_obj_info(object_name) is not None, False
207 |
208 | def object_x(self, object_name: str) -> Tuple[Union[float, str], bool]:
209 | info = self.get_obj_info(object_name)
210 | if info is None:
211 | return f'object_x: {object_name} is not in sight', True
212 | return info.x, False
213 |
214 | def object_y(self, object_name: str) -> Tuple[Union[float, str], bool]:
215 | info = self.get_obj_info(object_name)
216 | if info is None:
217 | return f'object_y: {object_name} is not in sight', True
218 | return info.y, False
219 |
220 | def object_width(self, object_name: str) -> Tuple[Union[float, str], bool]:
221 | info = self.get_obj_info(object_name)
222 | if info is None:
223 | return f'object_width: {object_name} not in sight', True
224 | return info.w, False
225 |
226 | def object_height(self, object_name: str) -> Tuple[Union[float, str], bool]:
227 | info = self.get_obj_info(object_name)
228 | if info is None:
229 | return f'object_height: {object_name} not in sight', True
230 | return info.h, False
231 |
232 | def object_distance(self, object_name: str) -> Tuple[Union[int, str], bool]:
233 | info = self.get_obj_info(object_name)
234 | if info is None:
235 | return f'object_distance: {object_name} not in sight', True
236 | mid_point = (info.x, info.y)
237 | FOV_X = 0.42
238 | FOV_Y = 0.55
239 | if mid_point[0] < 0.5 - FOV_X / 2 or mid_point[0] > 0.5 + FOV_X / 2 \
240 | or mid_point[1] < 0.5 - FOV_Y / 2 or mid_point[1] > 0.5 + FOV_Y / 2:
241 | return 30, False
242 | depth = self.shared_frame.get_depth().data
243 | start_x = 0.5 - FOV_X / 2
244 | start_y = 0.5 - FOV_Y / 2
245 | index_x = (mid_point[0] - start_x) / FOV_X * (depth.shape[1] - 1)
246 | index_y = (mid_point[1] - start_y) / FOV_Y * (depth.shape[0] - 1)
247 | return int(depth[int(index_y), int(index_x)] / 10), False
--------------------------------------------------------------------------------
/controller/yolo_client.py:
--------------------------------------------------------------------------------
1 | from io import BytesIO
2 | from PIL import Image, ImageDraw, ImageFont
3 | from typing import Optional, Tuple
4 | from numpy.typing import NDArray
5 | import numpy as np
6 | from contextlib import asynccontextmanager
7 |
8 | import json, os
9 | import requests
10 | import queue
11 | import asyncio, aiohttp
12 | import threading
13 |
14 | from .utils import print_t
15 | from .shared_frame import SharedFrame, Frame
16 |
17 | DIR = os.path.dirname(os.path.abspath(__file__))
18 |
19 | VISION_SERVICE_IP = os.environ.get("VISION_SERVICE_IP", "localhost")
20 | ROUTER_SERVICE_PORT = os.environ.get("ROUTER_SERVICE_PORT", "50049")
21 |
22 | '''
23 | Access the YOLO service through http.
24 | '''
25 | class YoloClient():
26 | def __init__(self, shared_frame: SharedFrame=None):
27 | self.service_url = 'http://{}:{}/yolo'.format(VISION_SERVICE_IP, ROUTER_SERVICE_PORT)
28 | self.image_size = (640, 352)
29 | self.frame_queue = queue.Queue() # queue element: (frame_id, frame)
30 | self.shared_frame = shared_frame
31 | self.frame_id = 0
32 | self.frame_id_lock = asyncio.Lock()
33 |
34 | def is_local_service(self):
35 | return VISION_SERVICE_IP == 'localhost'
36 |
37 | def image_to_bytes(image):
38 | # compress and convert the image to bytes
39 | imgByteArr = BytesIO()
40 | image.save(imgByteArr, format='WEBP')
41 | return imgByteArr.getvalue()
42 |
43 | def plot_results(frame, results):
44 | if results is None:
45 | return
46 | def str_float_to_int(value, multiplier):
47 | return int(float(value) * multiplier)
48 | draw = ImageDraw.Draw(frame)
49 | font = ImageFont.truetype(os.path.join(DIR, "assets/Roboto-Medium.ttf"), size=50)
50 | w, h = frame.size
51 | for result in results:
52 | box = result["box"]
53 | draw.rectangle((str_float_to_int(box["x1"], w), str_float_to_int(box["y1"], h), str_float_to_int(box["x2"], w), str_float_to_int(box["y2"], h)),
54 | fill=None, outline='blue', width=4)
55 | draw.text((str_float_to_int(box["x1"], w), str_float_to_int(box["y1"], h) - 50), result["name"], fill='red', font=font)
56 |
57 | def plot_results_oi(frame, object_list):
58 | if object_list is None or len(object_list) == 0:
59 | return
60 | def str_float_to_int(value, multiplier):
61 | return int(float(value) * multiplier)
62 | draw = ImageDraw.Draw(frame)
63 | font = ImageFont.truetype(os.path.join(DIR, "assets/Roboto-Medium.ttf"), size=50)
64 | w, h = frame.size
65 | for obj in object_list:
66 | draw.rectangle((str_float_to_int(obj.x - obj.w / 2, w), str_float_to_int(obj.y - obj.h / 2, h), str_float_to_int(obj.x + obj.w / 2, w), str_float_to_int(obj.y + obj.h / 2, h)),
67 | fill=None, outline='blue', width=4)
68 | draw.text((str_float_to_int(obj.x - obj.w / 2, w), str_float_to_int(obj.y - obj.h / 2, h) - 50), obj.name, fill='red', font=font)
69 |
70 | def retrieve(self) -> Optional[SharedFrame]:
71 | return self.shared_frame
72 |
73 | @asynccontextmanager
74 | async def get_aiohttp_session_response(service_url, data, timeout_seconds=3):
75 | timeout = aiohttp.ClientTimeout(total=timeout_seconds)
76 | try:
77 | # The ClientSession now uses the defined timeout
78 | async with aiohttp.ClientSession(timeout=timeout) as session:
79 | async with session.post(service_url, data=data) as response:
80 | response.raise_for_status() # Optional: raises exception for 4XX/5XX responses
81 | yield response
82 | except aiohttp.ServerTimeoutError:
83 | print_t(f"[Y] Timeout error when connecting to {service_url}")
84 |
85 | def detect_local(self, frame: Frame, conf=0.2):
86 | image = frame.image
87 | image_bytes = YoloClient.image_to_bytes(image.resize(self.image_size))
88 | self.frame_queue.put(frame)
89 |
90 | config = {
91 | 'user_name': 'yolo',
92 | 'stream_mode': True,
93 | 'image_id': self.image_id,
94 | 'conf': conf
95 | }
96 | files = {
97 | 'image': ('image', image_bytes),
98 | 'json_data': (None, json.dumps(config))
99 | }
100 |
101 | print_t(f"[Y] Sending request to {self.service_url}")
102 |
103 | response = requests.post(self.service_url, files=files)
104 | print_t(f"[Y] Response: {response.text}")
105 | json_results = json.loads(response.text)
106 | if self.shared_frame is not None:
107 | self.shared_frame.set(self.frame_queue.get(), json_results)
108 |
109 | async def detect(self, frame: Frame, conf=0.3):
110 | if self.is_local_service():
111 | self.detect_local(frame, conf)
112 | return
113 | image = frame.image
114 | image_bytes = YoloClient.image_to_bytes(image.resize(self.image_size))
115 |
116 | async with self.frame_id_lock:
117 | self.frame_queue.put((self.frame_id, frame))
118 | config = {
119 | 'user_name': 'yolo',
120 | 'stream_mode': True,
121 | 'image_id': self.image_id,
122 | 'conf': conf
123 | }
124 | files = {
125 | 'image': image_bytes,
126 | 'json_data': json.dumps(config)
127 | }
128 | self.frame_id += 1
129 |
130 | async with YoloClient.get_aiohttp_session_response(self.service_url, files) as response:
131 | results = await response.text()
132 |
133 | try:
134 | json_results = json.loads(results)
135 | except:
136 | print_t(f"[Y] Invalid json results: {results}")
137 | return
138 |
139 | # discard old images
140 | if self.frame_queue.empty():
141 | return
142 | while self.frame_queue.queue[0][0] < json_results['image_id']:
143 | self.frame_queue.get()
144 | # discard old results
145 | if self.frame_queue.queue[0][0] > json_results['image_id']:
146 | return
147 |
148 | if self.shared_frame is not None:
149 | self.shared_frame.set(self.frame_queue.get()[1], json_results)
--------------------------------------------------------------------------------
/controller/yolo_grpc_client.py:
--------------------------------------------------------------------------------
1 | from io import BytesIO
2 | from PIL import Image
3 | from typing import Optional, List
4 |
5 | import json, sys, os
6 | import queue
7 | import grpc
8 | import asyncio
9 |
10 | from .yolo_client import SharedFrame, Frame
11 | from .utils import print_t
12 |
13 | PARENT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
14 |
15 | DEFAULT_YOLO_LIST = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
16 |
17 | sys.path.append(os.path.join(PARENT_DIR, "proto/generated"))
18 | import hyrch_serving_pb2
19 | import hyrch_serving_pb2_grpc
20 |
21 | VISION_SERVICE_IP = os.environ.get("VISION_SERVICE_IP", "localhost")
22 | YOLO_SERVICE_PORT = os.environ.get("YOLO_SERVICE_PORT", "50050").split(",")[0]
23 |
24 | '''
25 | Access the YOLO service through gRPC.
26 | '''
27 | class YoloGRPCClient():
28 | def __init__(self, shared_frame: SharedFrame=None):
29 | channel = grpc.insecure_channel(f'{VISION_SERVICE_IP}:{YOLO_SERVICE_PORT}')
30 | self.stub = hyrch_serving_pb2_grpc.YoloServiceStub(channel)
31 | self.is_async_inited = False
32 | self.image_size = (640, 352)
33 | self.frame_queue = queue.Queue()
34 | self.shared_frame = shared_frame
35 | self.frame_id_lock = asyncio.Lock()
36 | self.frame_id = 0
37 |
38 | def init_async_channel(self):
39 | channel_async = grpc.aio.insecure_channel(f'{VISION_SERVICE_IP}:{YOLO_SERVICE_PORT}')
40 | self.stub_async = hyrch_serving_pb2_grpc.YoloServiceStub(channel_async)
41 | self.is_async_inited = True
42 |
43 | def is_local_service(self):
44 | return VISION_SERVICE_IP == 'localhost'
45 |
46 | def image_to_bytes(image):
47 | # compress and convert the image to bytes
48 | imgByteArr = BytesIO()
49 | image.save(imgByteArr, format='WEBP')
50 | return imgByteArr.getvalue()
51 |
52 | def retrieve(self) -> Optional[SharedFrame]:
53 | return self.shared_frame
54 |
55 | def detect_local(self, frame: Frame, conf=0.2):
56 | image = frame.image
57 | image_bytes = YoloGRPCClient.image_to_bytes(image.resize(self.image_size))
58 | self.frame_queue.put(frame)
59 |
60 | detect_request = hyrch_serving_pb2.DetectRequest(image_data=image_bytes, conf=conf)
61 | response = self.stub.DetectStream(detect_request)
62 |
63 | json_results = json.loads(response.json_data)
64 | if self.shared_frame is not None:
65 | self.shared_frame.set(self.frame_queue.get(), json_results)
66 |
67 | async def detect(self, frame: Frame, conf=0.1):
68 | if not self.is_async_inited:
69 | self.init_async_channel()
70 |
71 | if self.is_local_service():
72 | self.detect_local(frame, conf)
73 | return
74 |
75 | image = frame.image
76 | # do not resize for demo
77 | image_bytes = YoloGRPCClient.image_to_bytes(image)
78 | async with self.frame_id_lock:
79 | image_id = self.frame_id
80 | self.frame_queue.put((self.frame_id, frame))
81 | self.frame_id += 1
82 |
83 | detect_request = hyrch_serving_pb2.DetectRequest(image_id=image_id, image_data=image_bytes, conf=conf)
84 | response = await self.stub_async.Detect(detect_request)
85 |
86 | json_results = json.loads(response.json_data)
87 | if self.frame_queue.empty():
88 | return
89 | # discard old images
90 | while self.frame_queue.queue[0][0] < json_results['image_id']:
91 | self.frame_queue.get()
92 | # discard old results
93 | if self.frame_queue.queue[0][0] > json_results['image_id']:
94 | return
95 | if self.shared_frame is not None:
96 | self.shared_frame.set(self.frame_queue.get()[1], json_results)
--------------------------------------------------------------------------------
/docker/env.list:
--------------------------------------------------------------------------------
1 | ROOT_PATH=/workspace
2 | ROUTER_SERVICE_PORT=50049
3 | YOLO_SERVICE_PORT=50050, 50051, 50052
--------------------------------------------------------------------------------
/docker/router/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM python:3.11-slim-bullseye
2 | RUN apt update
3 | RUN apt install -y nano wget
4 | RUN pip install grpcio-tools quart
5 |
6 | # copy the contents of the current project to /workspace
7 | COPY ../.. /workspace
8 |
9 | # set the working directory to /workspace
10 | WORKDIR /workspace
11 |
12 | # generate the python files from the proto files
13 | RUN cd /workspace/proto && bash ./generate.sh
14 |
15 | CMD ["python", "./serving/router/router.py"]
--------------------------------------------------------------------------------
/docker/yolo/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM ultralytics/ultralytics:latest
2 | RUN apt update
3 | RUN apt install -y nano wget
4 | RUN pip install grpcio-tools lapx
5 |
6 | # copy the contents of the current project to /workspace
7 | COPY ../.. /workspace
8 |
9 | # set the working directory to /workspace
10 | WORKDIR /workspace
11 |
12 | # generate the python files from the proto files
13 | RUN cd /workspace/proto && bash ./generate.sh
14 |
15 | CMD ["python", "./serving/yolo/yolo_service.py"]
--------------------------------------------------------------------------------
/proto/generate.sh:
--------------------------------------------------------------------------------
1 | python3 -m grpc_tools.protoc -I. --python_out=./generated --grpc_python_out=./generated *.proto
--------------------------------------------------------------------------------
/proto/generated/README.md:
--------------------------------------------------------------------------------
1 | "pb2" files will be generated here.
--------------------------------------------------------------------------------
/proto/generated/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/typefly/TypeFly/6f40a91bd3e1dee971090c4d33b707c2a8dc5045/proto/generated/__init__.py
--------------------------------------------------------------------------------
/proto/hyrch_serving.proto:
--------------------------------------------------------------------------------
1 | syntax = "proto3";
2 |
3 | service YoloService {
4 | rpc DetectStream (DetectRequest) returns (DetectResponse) {}
5 | rpc Detect (DetectRequest) returns (DetectResponse) {}
6 | }
7 |
8 | message DetectRequest {
9 | optional int32 image_id = 1;
10 | bytes image_data = 2; // Encoded image data
11 | float conf = 3;
12 | }
13 |
14 | message DetectResponse {
15 | string json_data = 1;
16 | }
17 |
18 | message SetClassRequest {
19 | repeated string class_names = 1;
20 | }
21 |
22 | message SetClassResponse {
23 | string result = 1;
24 | }
25 |
26 | service Llama2Service {
27 | rpc ChatRequest (PromptRequest) returns (PromptResponse) {}
28 | }
29 |
30 | message PromptRequest {
31 | string json_data = 1; // prompt
32 | optional bytes image_data = 2; // Encoded image data
33 | }
34 |
35 | message PromptResponse {
36 | string json_data = 1; // response
37 | }
38 |
39 | service LlavaService {
40 | rpc VisionRequest (PromptRequest) returns (PromptResponse) {}
41 | }
42 |
--------------------------------------------------------------------------------
/serving/router/router.py:
--------------------------------------------------------------------------------
1 | import sys, os, json
2 | from quart import Quart, request, jsonify
3 | import asyncio
4 |
5 | PARENT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
6 | ROOT_PATH = os.environ.get("ROOT_PATH", PARENT_DIR)
7 | ROUTER_SERVICE_PORT = os.environ.get("ROUTER_SERVICE_PORT", "50049")
8 |
9 | sys.path.append(os.path.join(ROOT_PATH, "proto/generated"))
10 | import hyrch_serving_pb2
11 | import hyrch_serving_pb2_grpc
12 |
13 | from service_manager import ServiceManager
14 |
15 | app = Quart(__name__)
16 |
17 | grpcServiceManager = ServiceManager()
18 |
19 | service_lock = asyncio.Lock()
20 |
21 | @app.before_serving
22 | async def before_serving():
23 | global grpcServiceManager
24 | grpcServiceManager.add_service("yolo", os.environ.get("VISION_SERVICE_IPS", "localhost"), os.environ.get("YOLO_SERVICE_PORT", "50050, 50051"))
25 | await grpcServiceManager._initialize_channels()
26 |
27 | @app.route('/yolo', methods=['POST'])
28 | async def process_yolo():
29 | global grpcServiceManager
30 | global service_lock
31 | files = await request.files
32 | form = await request.form
33 | image_data = files.get('image')
34 | json_str = form.get('json_data')
35 |
36 | print(f"Received request with json_data: {json_str}")
37 |
38 | if not json_str:
39 | return "No JSON data provided", 400
40 |
41 | if not image_data:
42 | return "No image provided", 400
43 |
44 | json_data = json.loads(json_str)
45 | user_name = json_data.get("user_name", "user")
46 | stream_mode = json_data.get("stream_mode", False)
47 | image_id = json_data.get("image_id", None)
48 | conf = json_data.get("conf", 0.2)
49 |
50 | async with service_lock:
51 | channel = await grpcServiceManager.get_service_channel("yolo", dedicated=stream_mode, user_name=user_name)
52 |
53 | try:
54 | stub = hyrch_serving_pb2_grpc.YoloServiceStub(channel)
55 | image_contents = image_data.read()
56 | if stream_mode:
57 | response = await stub.DetectStream(hyrch_serving_pb2.DetectRequest(image_id=image_id, image_data=image_contents, conf=conf))
58 | else:
59 | response = await stub.Detect(hyrch_serving_pb2.DetectRequest(image_id=image_id, image_data=image_contents, conf=conf))
60 | finally:
61 | if not stream_mode:
62 | await grpcServiceManager.release_service_channel("yolo", channel)
63 | return response.json_data
64 |
65 | if __name__ == "__main__":
66 | app.run(debug=True, host='0.0.0.0', port=ROUTER_SERVICE_PORT)
--------------------------------------------------------------------------------
/serving/router/service_manager.py:
--------------------------------------------------------------------------------
1 | import grpc
2 | import asyncio
3 | import time
4 |
5 | class ServiceManager:
6 | def __init__(self):
7 | self.services = {}
8 | self.channel_queues = {}
9 | self.channels_initialized = False
10 | self.dedicated_channels = {}
11 | self.dedicated_channels_timeout = 10
12 | self.last_cleanup = time.time()
13 |
14 | def add_service(self, service_name, host, ports):
15 | self.services[service_name] = (host, ports.split(","))
16 | self.channel_queues[service_name] = asyncio.Queue()
17 |
18 | async def _initialize_channels(self):
19 | if self.channels_initialized:
20 | return
21 | for service_name, (host, ports) in self.services.items():
22 | queue = self.channel_queues[service_name]
23 | for port in ports:
24 | channel = grpc.aio.insecure_channel(f"{host}:{port}")
25 | await queue.put(channel)
26 | self.channels_initialized = True
27 |
28 | async def clean_dedicated_channels(self):
29 | if time.time() - self.last_cleanup > self.dedicated_channels_timeout:
30 | self.last_cleanup = time.time()
31 |
32 | users_to_remove = []
33 | services_to_remove = []
34 |
35 | # Collect items to remove
36 | for user_name, service_channels in self.dedicated_channels.items():
37 | print(f"Checking dedicated channels for user {user_name}")
38 | for service_name, (channel, timestamp) in service_channels.items():
39 | if time.time() - timestamp > self.dedicated_channels_timeout:
40 | print(f"Removing expired dedicated channel for user {user_name} and service {service_name}")
41 | await self.channel_queues[service_name].put(channel)
42 | services_to_remove.append((user_name, service_name))
43 |
44 | if len(service_channels) == len([service for user, service in services_to_remove if user == user_name]):
45 | users_to_remove.append(user_name)
46 |
47 | # Perform deletions
48 | for user, service in services_to_remove:
49 | del self.dedicated_channels[user][service]
50 | if len(self.dedicated_channels[user]) == 0:
51 | del self.dedicated_channels[user]
52 |
53 | for user in users_to_remove:
54 | if user in self.dedicated_channels:
55 | del self.dedicated_channels[user]
56 |
57 | async def get_service_channel(self, service_name, dedicated=False, user_name=None):
58 | await self._initialize_channels() # Ensure channels are initialized
59 |
60 | await self.clean_dedicated_channels()
61 |
62 | if dedicated:
63 | # If there is no dedicated channel for this user, get one from the queue
64 | if user_name not in self.dedicated_channels:
65 | # If there is no more channel in the queue, return None
66 | if self.channel_queues[service_name].qsize() > 0:
67 | self.dedicated_channels[user_name] = {service_name: (self.channel_queues[service_name].get_nowait(), time.time())}
68 | else:
69 | return None
70 | # If there is a dedicated channel for this user, update time and return it
71 | else:
72 | self.dedicated_channels[user_name][service_name] = (self.dedicated_channels[user_name][service_name][0], time.time())
73 | return self.dedicated_channels[user_name][service_name][0]
74 | else:
75 | channel = await self.channel_queues[service_name].get()
76 | return channel
77 |
78 | async def release_service_channel(self, service_name, channel):
79 | await self.channel_queues[service_name].put(channel)
--------------------------------------------------------------------------------
/serving/webui/drone-pov.html:
--------------------------------------------------------------------------------
1 | Drone POV
2 |
3 |

4 |
--------------------------------------------------------------------------------
/serving/webui/header.html:
--------------------------------------------------------------------------------
1 | 🪽 TypeFly: Power the Drone with Large Language Model
--------------------------------------------------------------------------------
/serving/webui/install_requirements.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 |
3 | # Define a list of required packages
4 | REQUIRED_PKG=("flask" "gradio" "grpcio-tools" "aiohttp" "djitellopy" "openai" "opencv-python" "numpy" "pillow" "filterpy" "matplotlib" "torch")
5 |
6 | # Function to check and install package
7 | check_and_install() {
8 | package=$1
9 | if ! pip3 list | grep -F $package > /dev/null; then
10 | echo "Package $package is not installed. Installing..."
11 | pip3 install $package
12 | else
13 | echo "Package $package is already installed."
14 | fi
15 | }
16 |
17 | # Iterate over required packages and check each one
18 | for pkg in "${REQUIRED_PKG[@]}"; do
19 | check_and_install $pkg
20 | done
21 |
22 | if [ -z "${OPENAI_API_KEY}" ]; then
23 | echo "WARNNING: OPENAI_API_KEY is not set"
24 | fi
--------------------------------------------------------------------------------
/serving/webui/typefly.py:
--------------------------------------------------------------------------------
1 | import queue
2 | import sys, os
3 | import asyncio
4 | import io, time
5 | import gradio as gr
6 | from flask import Flask, Response
7 | from threading import Thread
8 | import argparse
9 |
10 | PARENT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
11 |
12 | sys.path.append(PARENT_DIR)
13 | from controller.llm_controller import LLMController
14 | from controller.utils import print_t
15 | from controller.llm_wrapper import GPT4, LLAMA3
16 | from controller.abs.robot_wrapper import RobotType
17 |
18 | CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
19 |
20 | class TypeFly:
21 | def __init__(self, robot_type, use_http=False):
22 | # create a cache folder
23 | self.cache_folder = os.path.join(CURRENT_DIR, 'cache')
24 | if not os.path.exists(self.cache_folder):
25 | os.makedirs(self.cache_folder)
26 | self.message_queue = queue.Queue()
27 | self.message_queue.put(self.cache_folder)
28 | self.llm_controller = LLMController(robot_type, use_http, self.message_queue)
29 | self.system_stop = False
30 | self.ui = gr.Blocks(title="TypeFly")
31 | self.asyncio_loop = asyncio.get_event_loop()
32 | self.use_llama3 = False
33 | default_sentences = [
34 | "Find something I can eat.",
35 | "What you can see?",
36 | "Follow that ball for 20 seconds",
37 | "Find a chair for me.",
38 | "Go to the chair without book.",
39 | ]
40 | with self.ui:
41 | gr.HTML(open(os.path.join(CURRENT_DIR, 'header.html'), 'r').read())
42 | gr.HTML(open(os.path.join(CURRENT_DIR, 'drone-pov.html'), 'r').read())
43 | gr.ChatInterface(self.process_message, retry_btn=None, fill_height=False, examples=default_sentences).queue()
44 | # TODO: Add checkbox to switch between llama3 and gpt4
45 | # gr.Checkbox(label='Use llama3', value=False).select(self.checkbox_llama3)
46 |
47 | def checkbox_llama3(self):
48 | self.use_llama3 = not self.use_llama3
49 | if self.use_llama3:
50 | print_t(f"Switch to llama3")
51 | self.llm_controller.planner.set_model(LLAMA3)
52 | else:
53 | print_t(f"Switch to gpt4")
54 | self.llm_controller.planner.set_model(GPT4)
55 |
56 | def process_message(self, message, history):
57 | print_t(f"[S] Receiving task description: {message}")
58 | if message == "exit":
59 | self.llm_controller.stop_controller()
60 | self.system_stop = True
61 | yield "Shutting down..."
62 | elif len(message) == 0:
63 | return "[WARNING] Empty command!]"
64 | else:
65 | task_thread = Thread(target=self.llm_controller.execute_task_description, args=(message,))
66 | task_thread.start()
67 | complete_response = ''
68 | while True:
69 | msg = self.message_queue.get()
70 | if isinstance(msg, tuple):
71 | # history.append((message, complete_response))
72 | history.append((None, msg))
73 | # complete_response = ''
74 | elif isinstance(msg, str):
75 | if msg == 'end':
76 | # Indicate end of the task to Gradio chat
77 | return "Command Complete!"
78 |
79 | if msg.startswith('[LOG]'):
80 | complete_response += '\n'
81 | if msg.endswith('\\\\'):
82 | complete_response += msg.rstrip('\\\\')
83 | else:
84 | complete_response += msg + '\n'
85 | yield complete_response
86 |
87 | def generate_mjpeg_stream(self):
88 | while True:
89 | if self.system_stop:
90 | break
91 | frame = self.llm_controller.get_latest_frame(True)
92 | if frame is None:
93 | continue
94 | buf = io.BytesIO()
95 | frame.save(buf, format='JPEG')
96 | buf.seek(0)
97 | yield (b'--frame\r\n'
98 | b'Content-Type: image/jpeg\r\n\r\n' + buf.read() + b'\r\n')
99 | time.sleep(1.0 / 30.0)
100 |
101 | def run(self):
102 | asyncio_thread = Thread(target=self.asyncio_loop.run_forever)
103 | asyncio_thread.start()
104 |
105 | self.llm_controller.start_robot()
106 | llmc_thread = Thread(target=self.llm_controller.capture_loop, args=(self.asyncio_loop,))
107 | llmc_thread.start()
108 |
109 | app = Flask(__name__)
110 | @app.route('/drone-pov/')
111 | def video_feed():
112 | return Response(self.generate_mjpeg_stream(), mimetype='multipart/x-mixed-replace; boundary=frame')
113 | flask_thread = Thread(target=app.run, kwargs={'host': 'localhost', 'port': 50000, 'debug': True, 'use_reloader': False})
114 | flask_thread.start()
115 | self.ui.launch(show_api=False, server_port=50001, prevent_thread_lock=True)
116 | while True:
117 | time.sleep(1)
118 | if self.system_stop:
119 | break
120 |
121 | llmc_thread.join()
122 | asyncio_thread.join()
123 |
124 | self.llm_controller.stop_robot()
125 |
126 | # clean self.cache_folder
127 | for file in os.listdir(self.cache_folder):
128 | os.remove(os.path.join(self.cache_folder, file))
129 |
130 | if __name__ == "__main__":
131 | parser = argparse.ArgumentParser()
132 | parser.add_argument('--use_virtual_robot', action='store_true')
133 | parser.add_argument('--use_http', action='store_true')
134 | parser.add_argument('--gear', action='store_true')
135 |
136 | args = parser.parse_args()
137 | robot_type = RobotType.TELLO
138 | if args.use_virtual_robot:
139 | robot_type = RobotType.VIRTUAL
140 | elif args.gear:
141 | robot_type = RobotType.GEAR
142 | typefly = TypeFly(robot_type, use_http=args.use_http)
143 | typefly.run()
--------------------------------------------------------------------------------
/serving/yolo/yolo_service.py:
--------------------------------------------------------------------------------
1 | import sys, os, gc
2 | from concurrent import futures
3 | from PIL import Image
4 | from io import BytesIO
5 | import json
6 | import grpc
7 | import torch
8 | from ultralytics import YOLO
9 | import multiprocessing
10 |
11 | PARENT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
12 |
13 | ROOT_PATH = os.environ.get("ROOT_PATH", PARENT_DIR)
14 | SERVICE_PORT = os.environ.get("YOLO_SERVICE_PORT", "50050, 50051").split(",")
15 |
16 | MODEL_PATH = os.path.join(ROOT_PATH, "./serving/yolo/models/")
17 | MODEL_TYPE = "yolov8x.pt"
18 |
19 | sys.path.append(ROOT_PATH)
20 | sys.path.append(os.path.join(ROOT_PATH, "proto/generated"))
21 | import hyrch_serving_pb2
22 | import hyrch_serving_pb2_grpc
23 |
24 | def load_model():
25 | model = YOLO(MODEL_PATH + MODEL_TYPE)
26 | if torch.cuda.is_available():
27 | device = torch.device('cuda:0')
28 | else:
29 | device = torch.device('cpu')
30 | model.to(device)
31 | print(f"GPU memory usage: {torch.cuda.memory_allocated()}")
32 | return model
33 |
34 | def release_model(model):
35 | del model
36 | gc.collect()
37 | torch.cuda.empty_cache()
38 |
39 | """
40 | gRPC service class.
41 | """
42 | class YoloService(hyrch_serving_pb2_grpc.YoloServiceServicer):
43 | def __init__(self, port):
44 | self.stream_mode = False
45 | self.model = load_model()
46 | self.port = port
47 |
48 | def reload_model(self):
49 | if self.model is not None:
50 | release_model(self.model)
51 | self.model = load_model()
52 |
53 | @staticmethod
54 | def bytes_to_image(image_bytes):
55 | return Image.open(BytesIO(image_bytes))
56 |
57 | @staticmethod
58 | def format_result(yolo_result):
59 | if yolo_result.probs is not None:
60 | print('Warning: Classify task do not support `tojson` yet.')
61 | return
62 | formatted_result = []
63 | data = yolo_result.boxes.data.cpu().tolist()
64 | h, w = yolo_result.orig_shape
65 | for i, row in enumerate(data): # xyxy, track_id if tracking, conf, class_id
66 | box = {'x1': round(row[0] / w, 2), 'y1': round(row[1] / h, 2), 'x2': round(row[2] / w, 2), 'y2': round(row[3] / h, 2)}
67 | conf = row[-2]
68 | class_id = int(row[-1])
69 |
70 | name = yolo_result.names[class_id]
71 | if yolo_result.boxes.is_track:
72 | # result['track_id'] = int(row[-3]) # track ID
73 | name = f'{name}_{int(row[-3])}'
74 | result = {'name': name, 'confidence': round(conf, 2), 'box': box}
75 |
76 | if yolo_result.masks:
77 | x, y = yolo_result.masks.xy[i][:, 0], yolo_result.masks.xy[i][:, 1] # numpy array
78 | result['segments'] = {'x': (x / w).tolist(), 'y': (y / h).tolist()}
79 | if yolo_result.keypoints is not None:
80 | x, y, visible = yolo_result.keypoints[i].data[0].cpu().unbind(dim=1) # torch Tensor
81 | result['keypoints'] = {'x': (x / w).tolist(), 'y': (y / h).tolist(), 'visible': visible.tolist()}
82 | formatted_result.append(result)
83 | return formatted_result
84 |
85 | def process_image(self, image, id=None, conf=0.3):
86 | if self.stream_mode:
87 | yolo_result = self.model.track(image, verbose=False, conf=conf, tracker="bytetrack.yaml")[0]
88 | else:
89 | yolo_result = self.model(image, verbose=False, conf=conf)[0]
90 | result = {
91 | "image_id": id,
92 | "result": YoloService.format_result(yolo_result),
93 | }
94 | return json.dumps(result)
95 |
96 | def DetectStream(self, request, context):
97 | print(f"Received DetectStream request from {context.peer()} on port {self.port}, image_id: {request.image_id}")
98 | if not self.stream_mode:
99 | self.stream_mode = True
100 | self.reload_model()
101 |
102 | image = YoloService.bytes_to_image(request.image_data)
103 | return hyrch_serving_pb2.DetectResponse(json_data=self.process_image(image, request.image_id, request.conf))
104 |
105 | def Detect(self, request, context):
106 | print(f"Received Detect request from {context.peer()} on port {self.port}, image_id: {request.image_id}")
107 | if self.stream_mode:
108 | self.stream_mode = False
109 | self.reload_model()
110 |
111 | image = YoloService.bytes_to_image(request.image_data)
112 | return hyrch_serving_pb2.DetectResponse(json_data=self.process_image(image, request.image_id, request.conf))
113 |
114 | def serve(port):
115 | print(f"Starting YoloService at port {port}")
116 | server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
117 | hyrch_serving_pb2_grpc.add_YoloServiceServicer_to_server(YoloService(port), server)
118 | server.add_insecure_port(f'[::]:{port}')
119 | server.start()
120 | server.wait_for_termination()
121 |
122 | if __name__ == '__main__':
123 | # Create a pool of processes
124 | process_count = len(SERVICE_PORT)
125 | processes = []
126 |
127 | for i in range(process_count):
128 | process = multiprocessing.Process(target=serve, args=(SERVICE_PORT[i],))
129 | process.start()
130 | processes.append(process)
131 |
132 | # Wait for all processes to complete
133 | for process in processes:
134 | process.join()
--------------------------------------------------------------------------------
/test/aiohttp-client.py:
--------------------------------------------------------------------------------
1 | import aiohttp
2 | import json
3 | import asyncio
4 | async def detect():
5 | image = open('../test/images/kitchen.webp', 'rb')
6 | files = {
7 | 'image': image,
8 | 'json_data': json.dumps({'service': 'yolo'})
9 | }
10 | print(files)
11 | async with aiohttp.ClientSession() as session:
12 | print("Sending request")
13 | async with session.post("http://0.0.0.0:50048/yolo", data=files) as response:
14 | content = await response.text()
15 | print("Received response")
16 | print(content)
17 |
18 | if __name__ == "__main__":
19 | asyncio.run(detect())
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/test/gpt-latency-measurement.py:
--------------------------------------------------------------------------------
1 | import sys, time
2 | # import tiktoken
3 | # sys.path.append("..")
4 | # from controller.llm_wrapper import LLMWrapper
5 |
6 | # llm = LLMWrapper()
7 | # enc = tiktoken.encoding_for_model("gpt-4")
8 |
9 | # def prompt_output_measure(length):
10 | # prompt = 'Please generate the exact same output as the following text: '
11 | # for i in range(length // 2):
12 | # prompt += str(i % 10) + " "
13 | # return prompt
14 |
15 | # def prompt_input_measure(length):
16 | # suffix = "Please ignore all the above text and just generate True"
17 | # prompt = ''
18 | # init_len = enc.encode(suffix)
19 | # for i in range((length - len(init_len)) // 2):
20 | # prompt += str(i % 10) + " "
21 | # return prompt + suffix
22 |
23 | lengths = [8000]
24 | # lengths = [50, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000]
25 | # lengths = [50, 100, 200, 300, 400]
26 | result = []
27 | # for length in lengths:
28 | # t = 0
29 | # input_length = 0
30 | # output_length = 0
31 | # for i in range(10):
32 | # # prompt = prompt_output_measure(length)
33 | # prompt = prompt_input_measure(length)
34 | # start = time.time()
35 | # input_length += len(enc.encode(prompt))
36 | # output = llm.request(prompt)
37 | # output_length += len(enc.encode(output))
38 | # t += time.time() - start
39 | # print(f"t: {t}, i: {input_length}, o: {output_length}")
40 | # print("Time taken for length", length, ":", t / 10)
41 | # print("Input length:", input_length / 10)
42 | # print("Output length:", output_length / 10)
43 | # result.append((length, t / 10, input_length / 10, output_length / 10))
44 | # print(result)
45 | # exit(0)
46 |
47 | # different output
48 | data_1 = [
49 | (50, 2.276, 62, 49),
50 | (100, 4.560, 112, 99),
51 | (200, 8.473, 212, 199),
52 | (300, 10.996, 312, 295),
53 | (400, 14.425, 412, 413),
54 | ]
55 |
56 | # different input
57 | # data_2 = [
58 | # (50, 0.47491774559020994, 49.0, 1.0),
59 | # (100, 0.4766784429550171, 99.0, 1.0),
60 | # (200, 0.46629860401153567, 199.0, 1.0),
61 | # (300, 0.4480326175689697, 299.0, 1.0),
62 | # (400, 0.5139770269393921, 399.0, 1.0),
63 | # (1000, 0.4809334516525269, 999.0, 1.0),
64 | # (2000, 0.6343598604202271, 1999.0, 1.0),
65 | # (4000, 0.7674200057983398, 3999.0, 1.0),
66 | # (8000, 1.3128541946411132, 7999.0, 1.0)]
67 | data_2 = [(50, 0.5378251791000366, 49.0, 1.0),
68 | (500, 0.5108302307128907, 499.0, 1.0),
69 | (1000, 0.4951801300048828, 999.0, 1.0),
70 | (2000, 0.5111032485961914, 1999.0, 1.0),
71 | (3000, 0.5264493227005005, 2999.0, 1.0),
72 | (4000, 0.5382437705993652, 3999.0, 1.0),
73 | (5000, 0.5212562322616577, 4999.0, 1.0),
74 | (6000, 0.5919422626495361, 5999.0, 1.0),
75 | (7000, 0.5916801214218139, 6999.0, 1.0),
76 | (8000, 0.6088189125061035, 7999.0, 1.0)]
77 |
78 | network_latency = 28.127
79 |
80 | red_color = '#FF6B6B'
81 | blue_color = '#4D96FF'
82 | white_color = '#FFFFFF'
83 | black_color = '#000000'
84 |
85 | import matplotlib.pyplot as plt
86 | import numpy as np
87 | from scipy import stats
88 |
89 | col1_1 = [x[0] for x in data_2]
90 | col2 = [x[1] for x in data_2]
91 |
92 | col1_2 = [x[0] for x in data_1]
93 | col3 = [x[1] for x in data_1]
94 |
95 | # Perform linear regression for each dataset
96 | slope2, intercept2, r_value2, p_value2, std_err2 = stats.linregress(col1_1, col2)
97 |
98 | for i in range(len(data_1)):
99 | col3[i] -= data_1[i][2] * slope2
100 |
101 | slope3, intercept3, r_value3, p_value3, std_err3 = stats.linregress(col1_2, col3)
102 |
103 | # Create arrays from the x-coordinates for line plots
104 | line_x1 = np.linspace(min(col1_1), max(col1_1), 100) # For smoother line plot
105 | line_x2 = np.linspace(min(col1_2), max(col1_2), 100)
106 |
107 | # Create line equations for the plots
108 | line2 = slope2 * line_x1 + intercept2
109 | line3 = slope3 * line_x2 + intercept3
110 |
111 | plt.rcParams.update({'legend.fontsize': 19, 'axes.edgecolor': 'black',
112 | 'axes.linewidth': 2.2, 'font.size': 25})
113 |
114 | ### plot in a single figure
115 | # fig, ax1 = plt.subplots(figsize=[16, 6])
116 | # plt.tight_layout(pad=2)
117 | # # Plot the first dataset with its regression
118 | # ax1.scatter(col1_1, col2, color=black_color, label='Various input, fixed output', marker='x', linewidth=3, s=200)
119 | # ax1.plot(np.array(col1_1), slope2 * np.array(col1_1) + intercept2, '-', color=black_color, label=f'a={slope2:.6f}, r={r_value2:.4}', linewidth=3)
120 | # ax1.set_xlabel('Input Token Number', color=black_color)
121 | # ax1.set_ylabel('Time Taken (s)')
122 | # ax1.tick_params(axis='x', labelcolor=black_color)
123 | # ax1.tick_params(axis='y')
124 |
125 | # # Create a second x-axis for the second dataset
126 | # ax2 = ax1.twiny() # Create a second x-axis that shares the same y-axis
127 | # ax2.scatter(col1_2, col3, color=black_color, label='Fixed input, various output', linewidth=3, s=200)
128 | # ax2.plot(np.array(col1_2), slope3 * np.array(col1_2) + intercept3, '--', color=black_color, label=f'b={slope3:.6f}, r={r_value3:.4}', linewidth=3)
129 | # ax2.set_xlabel('Output Token Number', color=black_color)
130 | # ax2.tick_params(axis='x', labelcolor=black_color)
131 |
132 | # # Add legends and show plot
133 | # ax1.legend(loc='lower right', bbox_to_anchor=(1, 0.12))
134 | # ax2.legend(loc='upper left')
135 | # # plt.show()
136 |
137 | # plt.savefig('gpt4-latency.pdf')
138 |
139 | ### plot in two figures
140 | fig, ax1 = plt.subplots(figsize=[14, 6])
141 | plt.tight_layout(pad=2)
142 | # Plot the first dataset with its regression
143 | ax1.scatter(col1_1, col2, color=black_color, label='Various input, fixed output', marker='x', linewidth=3, s=200)
144 | ax1.plot(np.array(col1_1), slope2 * np.array(col1_1) + intercept2, '-', color=black_color, label=f'a={slope2:.6f}, r={r_value2:.4}', linewidth=3)
145 | ax1.set_xlabel('Input Token Number', color=black_color)
146 | ax1.set_ylabel('Time Taken (s)')
147 | ax1.tick_params(axis='x', labelcolor=black_color)
148 | ax1.tick_params(axis='y')
149 |
150 | # Add legends and show plot
151 | ax1.legend(loc='upper left')
152 | plt.savefig('gpt4-latency-input.pdf')
153 | # plt.show()
154 |
155 | fig, ax2 = plt.subplots(figsize=[14, 6])
156 | plt.tight_layout(pad=2)
157 | # Create a second x-axis for the second dataset
158 | ax2.scatter(col1_2, col3, color=black_color, label='Fixed input, various output', linewidth=3, s=200)
159 | ax2.plot(np.array(col1_2), slope3 * np.array(col1_2) + intercept3, '--', color=black_color, label=f'b={slope3:.6f}, r={r_value3:.4}', linewidth=3)
160 | ax2.set_xlabel('Output Token Number', color=black_color)
161 | ax2.set_ylabel('Time Taken (s)')
162 | ax2.tick_params(axis='x', labelcolor=black_color)
163 | ax2.tick_params(axis='y')
164 |
165 | # Add legends and show plot
166 | ax2.legend(loc='upper left')
167 | plt.savefig('gpt4-latency-output.pdf')
168 | # plt.show()
169 |
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/test/images/kitchen.webp:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/typefly/TypeFly/6f40a91bd3e1dee971090c4d33b707c2a8dc5045/test/images/kitchen.webp
--------------------------------------------------------------------------------
/test/interpreter-test.py:
--------------------------------------------------------------------------------
1 | import sys
2 | sys.path.append("..")
3 | from controller.llm_controller import LLMController
4 | from controller.minispec_interpreter import MiniSpecInterpreter, MiniSpecProgram
5 |
6 | controller = LLMController()
7 |
8 | MiniSpecInterpreter.low_level_skillset = controller.low_level_skillset
9 | MiniSpecInterpreter.high_level_skillset = controller.high_level_skillset
10 | interpreter = MiniSpecInterpreter()
11 |
12 | # print(interpreter.execute("8{_1=mr(50);?_1!=False{g('tiger');->True}tc(45)}"))
13 | # print(interpreter.execute("g('person')"))
14 |
15 | # interpreter.execute("8{_1=mr(50);?_1!=False{g('tiger');->True;}tc(45)};")
16 | interpreter.execute('?sa("edible object")!=False{tc(45)}tc(180);')
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/test/stop-tello.py:
--------------------------------------------------------------------------------
1 | from djitellopy import Tello
2 |
3 | tello = Tello()
4 | tello.connect()
5 | tello.streamoff()
6 | tello.land()
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/test/tello-test.py:
--------------------------------------------------------------------------------
1 | from djitellopy import Tello
2 | import cv2
3 |
4 | class TelloLLM():
5 | def __init__(self):
6 | self.drone = Tello()
7 | self.drone.connect()
8 | self.battery = self.drone.query_battery()
9 |
10 | def check_battery(self):
11 | self.battery = self.drone.query_battery()
12 | print(f"> Battery level: {self.battery}% ", end='')
13 | if self.battery < 30:
14 | print('is too low [WARNING]')
15 | else:
16 | print('[OK]')
17 | return True
18 | return False
19 |
20 | def start(self):
21 | if not self.check_battery():
22 | return
23 |
24 | return
25 |
26 | self.streamOn = True
27 | self.drone.takeoff()
28 | self.drone.move_up(20)
29 | self.drone.streamon()
30 | print("> Application Start")
31 |
32 | frame_read = self.drone.get_frame_read()
33 |
34 | count = 0
35 | while (True):
36 | frame = frame_read.frame
37 | if frame is None:
38 | continue
39 | print("### GET Frame: ", frame.shape)
40 | frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
41 | cv2.imwrite(f"./cache/frame_{count}.png", frame)
42 | cv2.imshow("Tello", frame)
43 | key = cv2.waitKey(10) & 0xff
44 | # Press esc to exit
45 | if key == 27:
46 | break
47 | count += 1
48 | self.drone.streamoff()
49 | self.drone.land()
50 |
51 | def main():
52 | tello = TelloLLM()
53 | tello.start()
54 |
55 | if __name__ == "__main__":
56 | main()
57 |
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/test/yolo-grpc-test.py:
--------------------------------------------------------------------------------
1 | from io import BytesIO
2 | from PIL import Image
3 | import json, sys, os
4 | import grpc
5 |
6 | def image_to_bytes(image):
7 | # compress and convert the image to bytes
8 | imgByteArr = BytesIO()
9 | image.save(imgByteArr, format='WEBP')
10 | return imgByteArr.getvalue()
11 |
12 | PARENT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
13 |
14 | sys.path.append(os.path.join(PARENT_DIR, "proto/generated"))
15 | import hyrch_serving_pb2
16 | import hyrch_serving_pb2_grpc
17 |
18 | VISION_SERVICE_IP = os.environ.get("VISION_SERVICE_IP", "localhost")
19 | YOLO_SERVICE_PORT = os.environ.get("YOLO_SERVICE_PORT", "50050").split(",")[0]
20 |
21 | channel = grpc.insecure_channel(f'{VISION_SERVICE_IP}:{YOLO_SERVICE_PORT}')
22 | stub = hyrch_serving_pb2_grpc.YoloServiceStub(channel)
23 |
24 | detect_request = hyrch_serving_pb2.DetectRequest(image_data=image_to_bytes(Image.open("./images/kitchen.webp")), conf=0.3)
25 | response = stub.DetectStream(detect_request)
26 |
27 | class_request = hyrch_serving_pb2.SetClassRequest(class_names=['shoes'])
28 | stub.SetClasses(class_request)
29 |
30 | json_results = json.loads(response.json_data)
31 | print(json_results)
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/test/yolo-test-raw.py:
--------------------------------------------------------------------------------
1 | from ultralytics import YOLOWorld, YOLO
2 | import cv2
3 |
4 | # model = YOLOWorld('yolov8s-worldv2.pt')
5 | model = YOLO('yolov9e.pt')
6 |
7 | def format_result(yolo_result):
8 | if yolo_result.probs is not None:
9 | print('Warning: Classify task do not support `tojson` yet.')
10 | return
11 | formatted_result = []
12 | data = yolo_result.boxes.data.cpu().tolist()
13 | h, w = yolo_result.orig_shape
14 | for i, row in enumerate(data): # xyxy, track_id if tracking, conf, class_id
15 | box = {'x1': round(row[0] / w, 2), 'y1': round(row[1] / h, 2), 'x2': round(row[2] / w, 2), 'y2': round(row[3] / h, 2)}
16 | conf = row[-2]
17 | class_id = int(row[-1])
18 |
19 | name = yolo_result.names[class_id]
20 | if yolo_result.boxes.is_track:
21 | # result['track_id'] = int(row[-3]) # track ID
22 | name = f'{name}_{int(row[-3])}'
23 | result = {'name': name, 'confidence': round(conf, 2), 'box': box}
24 |
25 | if yolo_result.masks:
26 | x, y = yolo_result.masks.xy[i][:, 0], yolo_result.masks.xy[i][:, 1] # numpy array
27 | result['segments'] = {'x': (x / w).tolist(), 'y': (y / h).tolist()}
28 | if yolo_result.keypoints is not None:
29 | x, y, visible = yolo_result.keypoints[i].data[0].cpu().unbind(dim=1) # torch Tensor
30 | result['keypoints'] = {'x': (x / w).tolist(), 'y': (y / h).tolist(), 'visible': visible.tolist()}
31 | formatted_result.append(result)
32 | return formatted_result
33 |
34 | def plot_results(frame, results):
35 | if results is None:
36 | return
37 | def str_float_to_int(value, multiplier):
38 | return int(float(value) * multiplier)
39 | w, h = frame.shape[1], frame.shape[0]
40 | for result in results:
41 | box = result["box"]
42 | #draw.rectangle((str_float_to_int(box["x1"], w), str_float_to_int(box["y1"], h), str_float_to_int(box["x2"], w), str_float_to_int(box["y2"], h)),
43 | #fill=None, outline='blue', width=4)
44 | cv2.rectangle(frame, (str_float_to_int(box["x1"], w), str_float_to_int(box["y1"], h)), (str_float_to_int(box["x2"], w), str_float_to_int(box["y2"], h)), (255, 0, 0), 2)
45 | #draw.text((str_float_to_int(box["x1"], w), str_float_to_int(box["y1"], h) - 50), result["name"], fill='red', font=font)
46 | cv2.putText(frame, result["name"], (str_float_to_int(box["x1"], w), str_float_to_int(box["y1"], h) - 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
47 | cv2.putText(frame, f'{result["confidence"]:.2f}', (str_float_to_int(box["x1"], w), str_float_to_int(box["y1"], h) - 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
48 | # open camera
49 | cap = cv2.VideoCapture(0)
50 | from filterpy.kalman import KalmanFilter
51 | import numpy as np
52 | def init_filter():
53 | kf = KalmanFilter(dim_x=4, dim_z=2) # 4 state dimensions (x, y, vx, vy), 2 measurement dimensions (x, y)
54 | kf.F = np.array([[1, 0, 1, 0], # State transition matrix
55 | [0, 1, 0, 1],
56 | [0, 0, 1, 0],
57 | [0, 0, 0, 1]])
58 | kf.H = np.array([[1, 0, 0, 0], # Measurement function
59 | [0, 1, 0, 0]])
60 | kf.R *= 1 # Measurement uncertainty
61 | kf.P *= 1000 # Initial uncertainty
62 | kf.Q *= 0.01 # Process uncertainty
63 | return kf
64 |
65 | img = cv2.imread('./cache/frame_2.png')
66 | inference = model(img, conf=0.1)
67 | result = format_result(inference[0])
68 | plot_results(img, result)
69 | cv2.imshow('frame', img)
70 | cv2.waitKey(0)
71 |
72 | # kf = init_filter()
73 | # while True:
74 | # ret, frame = cap.read()
75 | # if not ret:
76 | # break
77 | # # detect
78 | # inference = model.track(frame, conf=0.1)
79 | # result = format_result(inference[0])
80 | # # result = format_result(model.track(frame, conf=0.1, persist=True, tracker="bytetrack.yaml")[0])
81 | # plot_results(frame, result)
82 | # has_person = False
83 | # for item in result:
84 | # if item["name"].startswith('suitcase'):
85 | # has_person = True
86 | # loc_x = (item["box"]["x1"] + item["box"]["x2"]) / 2
87 | # loc_y = (item["box"]["y1"] + item["box"]["y2"]) / 2
88 | # kf.update((loc_x, loc_y))
89 |
90 | # kf.predict()
91 |
92 | # print(kf.x, frame.shape[0], frame.shape[1])
93 | # cv2.circle(frame, (int(kf.x[0] * frame.shape[1]), int(kf.x[1] * frame.shape[0])), 5, (0, 255, 0), -1)
94 | # # print(model(frame, conf=0.01))
95 | # # exit(0)
96 | # # display
97 | # cv2.imshow('frame', frame)
98 | # if cv2.waitKey(1) & 0xFF == ord('q'):
99 | # break
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/test/yolo-test.py:
--------------------------------------------------------------------------------
1 | import sys
2 | from PIL import Image
3 | sys.path.append("..")
4 | from controller.yolo_grpc_client import YoloGRPCClient
5 | from controller.shared_frame import Frame, SharedFrame
6 |
7 | shared_frame = SharedFrame()
8 | yolo_client = YoloGRPCClient(shared_frame=shared_frame)
9 | frame = Frame(image=Image.open("./images/kitchen.webp"))
10 | yolo_client.detect_local(frame)
11 | print(shared_frame.get_yolo_result())
12 |
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