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‎layers/rgbChannelExtraction.py‎

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import cv2
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import numpy as np
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def preprocess_frame(self, frame):
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"""
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Extracts the red channel from the frame and magnifies its values.
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"""
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original_frame = frame
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inverted_image = cv2.bitwise_not(original_frame)
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original_array = np.array(original_frame, dtype=np.int64)
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inv_original_array = np.array(inverted_image, dtype=np.int64)
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red_channel = original_array[:, :, 2]
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result_image = original_array.copy()
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# MIN MAX NORM
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result_image[:, :, 0] = (
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(result_image[:, :, 0] - np.min(result_image[:, :, 0]))
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/ (np.max(result_image[:, :, 0]) - np.min(result_image[:, :, 0]))
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* 255
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)
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red_channel = result_image[:, :, 2]
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result_image[:, :, 0] = red_channel * (
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red_channel + inv_original_array[:, :, 0]
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)
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result_image[:, :, 1] = 0
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result_image[:, :, 2] = 0
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# MIN MAX NORM
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result_image[:, :, 0] = (
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(result_image[:, :, 0] - np.min(result_image[:, :, 0]))
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/ (np.max(result_image[:, :, 0]) - np.min(result_image[:, :, 0]))
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* 255
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)
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result_image = np.clip(result_image, 0, 255)
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result_image = result_image.astype(np.uint8)
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self.image_pub_pre.publish(np.array(result_image))
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return result_image

‎ml/converter/ptToTflite.py‎

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import torch
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import onnx
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from onnx import helper
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import tensorflow as tf
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from torchvision import transforms
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import onnx_tf
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from ..yolov7.models.experimental import attempt_load
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from PIL import Image
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import os
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import shutil
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def convert_pt_to_tflite(weights_path, output_path, sample_image_path):
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"""
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Input:
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weights_path : string (e.g. path/to/weights.pt)
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output_path : string (e.g. path/to/tflite/weights.tflite)
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sample_image_path : string (e.g. path/to/sample/image.png)
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Output:
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A .tflite weights file that can be processed.
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"""
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# Load the PyTorch ResNet50 model
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pytorch_model = attempt_load(weights_path)
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pytorch_model.eval()
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# Export the PyTorch model to ONNX format
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image_path = sample_image_path
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input_data = Image.open(image_path).convert('RGB')
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input_data = input_data.resize((960, 608))
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img_transform = transforms.Compose([
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transforms.ToTensor()
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])
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dummy_input = img_transform(input_data).unsqueeze(0)
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# print(dummy_input)
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onnx_model_path = 'temp.onnx'
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torch.onnx.export(pytorch_model, dummy_input, onnx_model_path, verbose=False, opset_version=12)
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# Load the ONNX model
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onnx_model = onnx.load(onnx_model_path)
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# Define a mapping from old names to new names
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name_map = {"input.1": "input_1"}
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# Initialize a list to hold the new inputs
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new_inputs = []
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# Iterate over the inputs and change their names if needed
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for inp in onnx_model.graph.input:
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if inp.name in name_map:
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# Create a new ValueInfoProto with the new name
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new_inp = helper.make_tensor_value_info(name_map[inp.name],
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inp.type.tensor_type.elem_type,
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[dim.dim_value for dim in inp.type.tensor_type.shape.dim])
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new_inputs.append(new_inp)
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else:
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new_inputs.append(inp)
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# Clear the old inputs and add the new ones
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onnx_model.graph.ClearField("input")
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onnx_model.graph.input.extend(new_inputs)
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# Go through all nodes in the model and replace the old input name with the new one
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for node in onnx_model.graph.node:
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for i, input_name in enumerate(node.input):
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if input_name in name_map:
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node.input[i] = name_map[input_name]
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# Remove ONNX model file
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try:
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os.remove(onnx_model_path)
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except OSError as e:
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print(f"Could not delete {onnx_model_path}\nERROR:{e}")
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# Convert the ONNX model to TensorFlow format
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tf_model_path = 'temp.pb'
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tf_rep = onnx_tf.backend.prepare(onnx_model)
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tf_rep.export_graph(tf_model_path)
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# Convert the TensorFlow model to TensorFlow Lite format
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converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model(tf_model_path)
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tflite_model = converter.convert()
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try:
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shutil.rmtree(tf_model_path)
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except OSError as e:
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print(f"Could not delete {tf_model_path}\nERROR:{e}")
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# Save the TensorFlow Lite model to a file
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with open(output_path, 'wb') as f:
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f.write(tflite_model)

‎ml/weights/README.txt‎

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##Weights will be placed here

‎ml/yolov7‎

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Subproject commit a207844b1ce82d204ab36d87d496728d3d2348e7

‎ros/rosviz/CMakeLists.txt‎

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cmake_minimum_required(VERSION 3.0.2)
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project(rosviz)
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## Compile as C++11, supported in ROS Kinetic and newer
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# add_compile_options(-std=c++11)
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## Find catkin macros and libraries
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## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
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## is used, also find other catkin packages
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find_package(catkin REQUIRED COMPONENTS
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roscpp
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rospy
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)
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## System dependencies are found with CMake's conventions
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# find_package(Boost REQUIRED COMPONENTS system)
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## Uncomment this if the package has a setup.py. This macro ensures
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## modules and global scripts declared therein get installed
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## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html
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# catkin_python_setup()
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################################################
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## Declare ROS messages, services and actions ##
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################################################
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## To declare and build messages, services or actions from within this
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## package, follow these steps:
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## * Let MSG_DEP_SET be the set of packages whose message types you use in
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## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).
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## * In the file package.xml:
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## * add a build_depend tag for "message_generation"
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## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET
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## * If MSG_DEP_SET isn't empty the following dependency has been pulled in
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## but can be declared for certainty nonetheless:
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## * add a exec_depend tag for "message_runtime"
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## * In this file (CMakeLists.txt):
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## * add "message_generation" and every package in MSG_DEP_SET to
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## find_package(catkin REQUIRED COMPONENTS ...)
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## * add "message_runtime" and every package in MSG_DEP_SET to
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## catkin_package(CATKIN_DEPENDS ...)
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## * uncomment the add_*_files sections below as needed
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## and list every .msg/.srv/.action file to be processed
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## * uncomment the generate_messages entry below
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## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)
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## Generate messages in the 'msg' folder
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# add_message_files(
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# FILES
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# Message1.msg
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# Message2.msg
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# )
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## Generate services in the 'srv' folder
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# add_service_files(
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# FILES
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# Service1.srv
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# Service2.srv
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# )
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## Generate actions in the 'action' folder
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# add_action_files(
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# FILES
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# Action1.action
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# Action2.action
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# )
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## Generate added messages and services with any dependencies listed here
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# generate_messages(
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# DEPENDENCIES
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# std_msgs # Or other packages containing msgs
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# )
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################################################
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## Declare ROS dynamic reconfigure parameters ##
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################################################
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## To declare and build dynamic reconfigure parameters within this
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## package, follow these steps:
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## * In the file package.xml:
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## * add a build_depend and a exec_depend tag for "dynamic_reconfigure"
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## * In this file (CMakeLists.txt):
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## * add "dynamic_reconfigure" to
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## find_package(catkin REQUIRED COMPONENTS ...)
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## * uncomment the "generate_dynamic_reconfigure_options" section below
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## and list every .cfg file to be processed
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## Generate dynamic reconfigure parameters in the 'cfg' folder
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# generate_dynamic_reconfigure_options(
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# cfg/DynReconf1.cfg
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# cfg/DynReconf2.cfg
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# )
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###################################
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## catkin specific configuration ##
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###################################
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## The catkin_package macro generates cmake config files for your package
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## Declare things to be passed to dependent projects
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## INCLUDE_DIRS: uncomment this if your package contains header files
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## LIBRARIES: libraries you create in this project that dependent projects also need
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## CATKIN_DEPENDS: catkin_packages dependent projects also need
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## DEPENDS: system dependencies of this project that dependent projects also need
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catkin_package(
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# INCLUDE_DIRS include
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# LIBRARIES rosviz
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# CATKIN_DEPENDS roscpp rospy
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# DEPENDS system_lib
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)
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###########
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## Build ##
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###########
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## Specify additional locations of header files
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## Your package locations should be listed before other locations
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include_directories(
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# include
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${catkin_INCLUDE_DIRS}
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)
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## Declare a C++ library
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# add_library(${PROJECT_NAME}
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# src/${PROJECT_NAME}/rosviz.cpp
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# )
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## Add cmake target dependencies of the library
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## as an example, code may need to be generated before libraries
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## either from message generation or dynamic reconfigure
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# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
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## Declare a C++ executable
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## With catkin_make all packages are built within a single CMake context
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## The recommended prefix ensures that target names across packages don't collide
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# add_executable(${PROJECT_NAME}_node src/rosviz_node.cpp)
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## Rename C++ executable without prefix
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## The above recommended prefix causes long target names, the following renames the
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## target back to the shorter version for ease of user use
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## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node"
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# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "")
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## Add cmake target dependencies of the executable
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## same as for the library above
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# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
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## Specify libraries to link a library or executable target against
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# target_link_libraries(${PROJECT_NAME}_node
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# ${catkin_LIBRARIES}
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# )
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#############
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## Install ##
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#############
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# all install targets should use catkin DESTINATION variables
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# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html
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## Mark executable scripts (Python etc.) for installation
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## in contrast to setup.py, you can choose the destination
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# catkin_install_python(PROGRAMS
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# scripts/my_python_script
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# DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
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# )
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## Mark executables for installation
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## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_executables.html
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# install(TARGETS ${PROJECT_NAME}_node
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# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
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# )
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## Mark libraries for installation
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## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_libraries.html
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# install(TARGETS ${PROJECT_NAME}
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# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
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# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
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# RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION}
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# )
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## Mark cpp header files for installation
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# install(DIRECTORY include/${PROJECT_NAME}/
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# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
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# FILES_MATCHING PATTERN "*.h"
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# PATTERN ".svn" EXCLUDE
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# )
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## Mark other files for installation (e.g. launch and bag files, etc.)
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# install(FILES
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# # myfile1
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# # myfile2
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# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
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# )
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#############
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## Testing ##
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#############
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## Add gtest based cpp test target and link libraries
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# catkin_add_gtest(${PROJECT_NAME}-test test/test_rosviz.cpp)
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# if(TARGET ${PROJECT_NAME}-test)
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# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME})
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# endif()
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## Add folders to be run by python nosetests
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# catkin_add_nosetests(test)

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