first commit

layers/rgbChannelExtraction.py

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

ml/converter/ptToTflite.py

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

ml/weights/README.txt

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+##Weights will be placed here

ros/rosviz/CMakeLists.txt

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