Update docs (#68)

* added new tool docs

* added example tutorials

* fixed link

* fixed link

* fixed link

* spelling mistakes

README.md

@@ -78,6 +78,30 @@ the individual steps and tools to get the answer:
 {"visualize_output": "final_output.png"}]
 ```
 
+You can also provide reference data for the model to utilize. For example, if you want
+to utilize VisualPromptCounting:
+
+```python
+agent(
+ "How many apples are in this image?",
+ image="apples.jpg",
+ reference_data={"bbox": [0.1, 0.11, 0.24, 0.25]},
+)
+```
+Where `[0.1, 0.11, 0.24, 0.25]` is the normalized bounding box coordinates of an apple.
+Similarly for DINOv you can provide a reference image and mask:
+
+```python
+agent(
+ "Can you detect all of the objects similar to the mask I've provided?",
+ image="image.jpg",
+ reference_data={"mask": "reference_mask.png", "image": "reference_image.png"},
+)
+```
+Here, `reference_mask.png` and `reference_image.png` in `reference_data` could be any
+image with it's corresponding mask that is the object you want to detect in `image.jpg`.
+You can find a demo app to generate masks for DINOv [here](examples/mask_app/).
+
 ### Tools
 There are a variety of tools for the model or the user to use. Some are executed locally
 while others are hosted for you. You can also ask an LLM directly to build a tool for
@@ -100,25 +124,26 @@ you. For example:
 You can also add your own custom tools for your vision agent to use:
 
 ```python
->>> from vision_agent.tools import Tool, register_tool
->>> @register_tool
->>> class NumItems(Tool):
->>>  name = "num_items_"
->>>  description = "Returns the number of items in a list."
->>>  usage = {
->>>  "required_parameters": [{"name": "prompt", "type": "list"}],
->>>  "examples": [
->>>  {
->>>  "scenario": "How many items are in this list? ['a', 'b', 'c']",
->>>  "parameters": {"prompt": "['a', 'b', 'c']"},
->>>  }
->>>  ],
->>>  }
->>>  def __call__(self, prompt: list[str]) -> int:
->>>  return len(prompt)
+from vision_agent.tools import Tool, register_tool
+@register_tool
+class NumItems(Tool):
+ name = "num_items_"
+ description = "Returns the number of items in a list."
+ usage = {
+ "required_parameters": [{"name": "prompt", "type": "list"}],
+ "examples": [
+ {
+ "scenario": "How many items are in this list? ['a', 'b', 'c']",
+ "parameters": {"prompt": "['a', 'b', 'c']"},
+ }
+ ],
+ }
+ def __call__(self, prompt: list[str]) -> int:
+ return len(prompt)
 ```
 This will register it with the list of tools Vision Agent has access to. It will be able
-to pick it based on the tool description and use it based on the usage provided.
+to pick it based on the tool description and use it based on the usage provided. You can
+find an example that creates a custom tool for template matching [here](examples/custom_tools/).
 
 #### Tool List
 | Tool | Description |
@@ -137,8 +162,10 @@ to pick it based on the tool description and use it based on the usage provided.
 | BoxDistance | BoxDistance returns the minimum distance between two bounding boxes normalized to 2 decimal places. |
 | BboxContains | BboxContains returns the intersection of two boxes over the target box area. It is good for check if one box is contained within another box. |
 | ExtractFrames | ExtractFrames extracts frames with motion from a video. |
-| ZeroShotCounting | ZeroShotCounting returns the total number of objects belonging to a single class in a given image |
-| VisualPromptCounting | VisualPromptCounting returns the total number of objects belonging to a single class given an image and visual prompt |
+| ZeroShotCounting | ZeroShotCounting returns the total number of objects belonging to a single class in a given image. |
+| VisualPromptCounting | VisualPromptCounting returns the total number of objects belonging to a single class given an image and visual prompt. |
+| VisualQuestionAnswering | VisualQuestionAnswering is a tool that can explain the contents of an image and answer questions about the image. |
+| ImageQuestionAnswering | ImageQuestionAnswering is similar to VisualQuestionAnswering but does not rely on OpenAI and instead uses a dedicated model for the task. |
 | OCR | OCR returns the text detected in an image along with the location. |
 
 

examples/custom_tools/README.md

@@ -0,0 +1,29 @@
+# Template Matching Custom Tool
+
+This demo shows you how to create a custom tool for template matching that your Vision
+Agent can then use to help you answer questions. To get started, you can install the
+requirements by running:
+
+```bash
+pip install -r requirements.txt
+```
+
+You can then run the custom tool by running:
+
+```bash
+python run_custom_tool.py
+```
+
+Tool choice can be difficult for the agent to get, so sometimes it helps to explicitly
+call out which tool you want to use. For example:
+
+```python
+import vision_agent as va
+
+agent = va.agent.VisionAgent(verbose=True)
+agent(
+ "Can you use the 'template_match_' tool to find the location of pid_template.png in pid.png?",
+ image="pid.png",
+ reference_data={"image": "pid_template.png"},
+)
+```

examples/mask_app/README.md

@@ -0,0 +1,31 @@
+# Generate Masks for DINOv
+
+This application allows you to generate masks to use for the DINOv tool. To get started
+install the requirements by running:
+
+```bash
+pip install -r requirements.txt
+```
+
+Then you can run the streamlit app by running:
+
+```bash
+streamlit run app.py
+```
+
+From here you can upload an image, paint a mask over the image, and then save the mask.
+This can be used as input for the DINOv tool.
+
+```python
+import vision_agent as va
+
+data = {
+ "prompt": [{"mask": "baggage.png", "image": "baggage_mask.png"}],
+ "image": "baggage2.png",
+}
+tool = va.tools.DINOv()
+output = res(**data)
+image = va.image_utils.overlay_masks("baggage2.png", output)
+image = va.image_utils.overlay_bboxes(image, output)
+image.show()
+```

vision_agent/agent/vision_agent.py

@@ -585,7 +585,7 @@ def chat_with_workflow(
  self.task_model, question, self.tools, reflections
  )
 
- task_depend = {"Original Quesiton": question}
+ task_depend = {"Original Question": question}
  previous_log = ""
  answers = []
  for task in task_list: