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After a long hesitation about whether it would be fun to do or not, I finally did it. This UDF provides a way to use mediapipe in AutoIt The usage is similar to the python usage of mediapipe Prerequisites Download and extract autoit-mediapipe-0.10.35-opencv-4.13.0-com-v0.6.0.7z into a folder Download the opencv UDF from here Sources Here Documentation A generated documentation for functions is available here (v0.6.0) Examples More examples can be found here (v0.6.0) To run them, please follow these instructions (v0.6.0) Face Detection with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/face_detector/python/face_detector.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/face_detector/python/face_detector.ipynb ;~ Title: Face Detection with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" ; STEP 1: Import the necessary modules. Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Main() Func Main() Local $_IMAGE_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\brother-sister-girl-family-boy-977170.jpg" Local $_IMAGE_URL = "https://i.imgur.com/Vu2Nqwb.jpg" Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\blaze_face_short_range.tflite" Local $_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/face_detector/blaze_face_short_range/float16/1/blaze_face_short_range.tflite" Local $url, $file_path Local $sample_files[] = [ _ _Mediapipe_Tuple($_IMAGE_FILE, $_IMAGE_URL), _ _Mediapipe_Tuple($_MODEL_FILE, $_MODEL_URL) _ ] For $config In $sample_files $file_path = $config[0] $url = $config[1] If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next ; STEP 2: Create a FaceDetector object. Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $vision.FaceDetectorOptions(_Mediapipe_Params("base_options", $base_options)) Local $detector = $vision.FaceDetector.create_from_options($options) ; STEP 3: Load the input image. Local $image = $mp.Image.create_from_file($_IMAGE_FILE) ; Compute the scale to make drawn elements visible when the image is resized for display Local $scale = 1 / resize_and_show($image.mat_view(), Default, False) ; STEP 4: Detect faces in the input image. Local $detection_result = $detector.detect($image) ; STEP 5: Process the detection result. In this case, visualize it. Local $annotated_image = visualize($image.mat_view(), $detection_result, $scale) resize_and_show($annotated_image, "face_detector") $cv.waitKey() EndFunc ;==>Main Func isclose($a, $b) Return Abs($a - $b) <= 1E-6 EndFunc ;==>isclose ; Checks if the float value is between 0 and 1. Func is_valid_normalized_value($value) Return $value >= 0 And $value <= 1 Or isclose(0, $value) Or isclose(1, $value) EndFunc ;==>is_valid_normalized_value #cs Converts normalized value pair to pixel coordinates. #ce Func _normalized_to_pixel_coordinates($normalized_x, $normalized_y, $image_width, $image_height) If Not (is_valid_normalized_value($normalized_x) And is_valid_normalized_value($normalized_y)) Then ; TODO: Draw coordinates even if it's outside of the image bounds. Return Default EndIf Local $x_px = _Min(Floor($normalized_x * $image_width), $image_width - 1) Local $y_px = _Min(Floor($normalized_y * $image_height), $image_height - 1) Return _OpenCV_Point($x_px, $y_px) EndFunc ;==>_normalized_to_pixel_coordinates #cs Draws bounding boxes and keypoints on the input image and return it. Args: image: The input RGB image. detection_result: The list of all "Detection" entities to be visualize. Returns: Image with bounding boxes. #ce Func visualize($rgb_image, $detection_result, $scale = 1.0) Local $MARGIN = 10 * $scale ; pixels Local $ROW_SIZE = 10 ; pixels Local $FONT_SIZE = $scale Local $FONT_THICKNESS = 2 * $scale Local $TEXT_COLOR = _OpenCV_RGB(255, 0, 0) ; red Local $bbox_thickness = 3 * $scale Local $keypoint_color = _OpenCV_RGB(0, 255, 0) Local $keypoint_thickness = 2 * $scale Local $keypoint_radius = 2 * $scale Local $annotated_image = $cv.cvtColor($rgb_image, $CV_COLOR_RGB2BGR) Local $width = $rgb_image.width Local $height = $rgb_image.height Local $bbox, $start_point, $end_point, $keypoint_px Local $category, $category_name, $probability, $result_text, $text_location For $detection In $detection_result.detections ; Draw bounding_box $bbox = $detection.bounding_box $start_point = _OpenCV_Point($bbox.origin_x, $bbox.origin_y) $end_point = _OpenCV_Point($bbox.origin_x + $bbox.width, $bbox.origin_y + $bbox.height) $cv.rectangle($annotated_image, $start_point, $end_point, $TEXT_COLOR, $bbox_thickness) ; Draw keypoints For $keypoint In $detection.keypoints $keypoint_px = _normalized_to_pixel_coordinates($keypoint.x, $keypoint.y, $width, $height) $cv.circle($annotated_image, $keypoint_px, $keypoint_thickness, $keypoint_color, $keypoint_radius) Next ; Draw label and score $category = $detection.categories(0) $category_name = $category.category_name $probability = Round($category.score, 2) $result_text = $category_name & ' (' & $probability & ')' $text_location = _OpenCV_Point($MARGIN + $bbox.origin_x, $MARGIN + $ROW_SIZE + $bbox.origin_y) $cv.putText($annotated_image, $result_text, $text_location, $CV_FONT_HERSHEY_PLAIN, $FONT_SIZE, $TEXT_COLOR, $FONT_THICKNESS) Next Return $annotated_image EndFunc ;==>visualize Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Face Landmarks Detection with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/face_landmarker/python/%5BMediaPipe_Python_Tasks%5D_Face_Landmarker.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/face_landmarker/python/%5BMediaPipe_Python_Tasks%5D_Face_Landmarker.ipynb ;~ Title: Face Landmarks Detection with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" ; STEP 1: Import the necessary modules. Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Global $drawing_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision.drawing_utils") _AssertIsObj($drawing_utils, "Failed to load mediapipe.tasks.autoit.vision.drawing_utils") Global $drawing_styles = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision.drawing_styles") _AssertIsObj($drawing_styles, "Failed to load mediapipe.tasks.autoit.vision.drawing_styles") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Main() Func Main() Local $_IMAGE_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\business-person.png" Local $_IMAGE_URL = "https://storage.googleapis.com/mediapipe-assets/business-person.png" Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\face_landmarker.task" Local $_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task" Local $url, $file_path Local $sample_files[] = [ _ _Mediapipe_Tuple($_IMAGE_FILE, $_IMAGE_URL), _ _Mediapipe_Tuple($_MODEL_FILE, $_MODEL_URL) _ ] For $config In $sample_files $file_path = $config[0] $url = $config[1] If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next ; STEP 2: Create a FaceLandmarker object. Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $vision.FaceLandmarkerOptions(_Mediapipe_Params("base_options", $base_options, _ "output_face_blendshapes", True, _ "output_facial_transformation_matrixes", True, _ "num_faces", 1)) Local $detector = $vision.FaceLandmarker.create_from_options($options) ; STEP 3: Load the input image. Local $image = $mp.Image.create_from_file($_IMAGE_FILE) ; STEP 4: Detect hand landmarks from the input image. Local $detection_result = $detector.detect($image) ; STEP 5: Process the classification result. In this case, visualize it. Local $annotated_image = draw_landmarks_on_image($image.mat_view(), $detection_result) resize_and_show($annotated_image) $cv.waitKey() EndFunc ;==>Main Func draw_landmarks_on_image($image, $detection_result) ; Compute the scale to make drawn elements visible when the image is resized for display Local $scale = 1 / resize_and_show($image, Default, False) Local $face_landmarks_list = $detection_result.face_landmarks Local $annotated_image = $cv.cvtColor($image, $CV_COLOR_RGB2BGR) ; Loop through the detected faces to visualize. For $face_landmarks In $face_landmarks_list ; Draw the face landmarks. $drawing_utils.draw_landmarks(_Mediapipe_Params( _ "image", $annotated_image, _ "landmark_list", $face_landmarks, _ "connections", $vision.FaceLandmarksConnections.FACE_LANDMARKS_TESSELATION, _ "landmark_drawing_spec", Null, _ "connection_drawing_spec", $drawing_styles.get_default_face_mesh_tesselation_style($scale))) $drawing_utils.draw_landmarks(_Mediapipe_Params( _ "image", $annotated_image, _ "landmark_list", $face_landmarks, _ "connections", $vision.FaceLandmarksConnections.FACE_LANDMARKS_CONTOURS, _ "landmark_drawing_spec", Null, _ "connection_drawing_spec", $drawing_styles.get_default_face_mesh_contours_style(1, $scale))) $drawing_utils.draw_landmarks(_Mediapipe_Params( _ "image", $annotated_image, _ "landmark_list", $face_landmarks, _ "connections", $vision.FaceLandmarksConnections.FACE_LANDMARKS_LEFT_IRIS, _ "landmark_drawing_spec", Null, _ "connection_drawing_spec", $drawing_styles.get_default_face_mesh_iris_connections_style($scale))) $drawing_utils.draw_landmarks(_Mediapipe_Params( _ "image", $annotated_image, _ "landmark_list", $face_landmarks, _ "connections", $vision.FaceLandmarksConnections.FACE_LANDMARKS_RIGHT_IRIS, _ "landmark_drawing_spec", Null, _ "connection_drawing_spec", $drawing_styles.get_default_face_mesh_iris_connections_style($scale))) Next Return $annotated_image EndFunc ;==>draw_landmarks_on_image Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Gesture Recognizer with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/gesture_recognizer/python/gesture_recognizer.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/gesture_recognizer/python/gesture_recognizer.ipynb ;~ Title: Gesture Recognizer with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Global $mp_hands = $mp.tasks.vision.HandLandmarksConnections Global $mp_drawing = $mp.tasks.vision.drawing_utils Global $mp_drawing_styles = $mp.tasks.vision.drawing_styles Main() Func Main() Local $IMAGE_FILENAMES[] = ['thumbs_down.jpg', 'victory.jpg', 'thumbs_up.jpg', 'pointing_up.jpg'] Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\gesture_recognizer.task" Local $_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/gesture_recognizer/gesture_recognizer/float16/1/gesture_recognizer.task" Local $sample_files[UBound($IMAGE_FILENAMES) + 1] $sample_files[0] = _Mediapipe_Tuple($_MODEL_FILE, $_MODEL_URL) Local $url, $file_path, $name For $i = 0 To UBound($IMAGE_FILENAMES) - 1 $name = $IMAGE_FILENAMES[$i] $file_path = $MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $name $url = "https://storage.googleapis.com/mediapipe-tasks/gesture_recognizer/" & $name $sample_files[$i + 1] = _Mediapipe_Tuple($file_path, $url) Next For $config In $sample_files $file_path = $config[0] $url = $config[1] If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next ; STEP 2: Create a GestureRecognizer object. Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $vision.GestureRecognizerOptions(_Mediapipe_Params("base_options", $base_options)) Local $recognizer = $vision.GestureRecognizer.create_from_options($options) Local $image, $recognition_result, $top_gesture, $hands_landmarks For $image_file_name In $IMAGE_FILENAMES ; STEP 3: Load the input image. $image = $mp.Image.create_from_file($MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $image_file_name) ; STEP 4: Recognize gestures in the input image. $recognition_result = $recognizer.recognize($image) ; STEP 5: Process the result. In this case, visualize it. $top_gesture = $recognition_result.gestures(0) (0) $hands_landmarks = $recognition_result.hand_landmarks display_image_with_gestures_and_hand_landmarks($image, $top_gesture, $hands_landmarks) Next $cv.waitKey() EndFunc ;==>Main #cs Displays an image with the gesture category and its score along with the hand landmarks. #ce Func display_image_with_gestures_and_hand_landmarks($image, $gesture, $hands_landmarks) ; Display gestures and hand landmarks. Local $annotated_image = $cv.cvtColor($image.mat_view(), $CV_COLOR_RGB2BGR) Local $title = StringFormat("%s (%.2f)", $gesture.category_name, $gesture.score) ; Compute the scale to make drawn elements visible when the image is resized for display Local $scale = 1 / resize_and_show($annotated_image, Default, False) For $hand_landmarks In $hands_landmarks $mp_drawing.draw_landmarks( _ $annotated_image, _ $hand_landmarks, _ $mp_hands.HAND_CONNECTIONS, _ $mp_drawing_styles.get_default_hand_landmarks_style($scale), _ $mp_drawing_styles.get_default_hand_connections_style($scale)) Next resize_and_show($annotated_image, $title) EndFunc ;==>display_image_with_gestures_and_hand_landmarks Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Hand Landmarks Detection with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/hand_landmarker/python/hand_landmarker.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/hand_landmarker/python/hand_landmarker.ipynb ;~ Title: Hand Landmarks Detection with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Global $mp_hands = $mp.tasks.vision.HandLandmarksConnections Global $mp_drawing = $mp.tasks.vision.drawing_utils Global $mp_drawing_styles = $mp.tasks.vision.drawing_styles Main() Func Main() Local $_IMAGE_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\woman_hands.jpg" Local $_IMAGE_URL = "https://storage.googleapis.com/mediapipe-tasks/hand_landmarker/woman_hands.jpg" Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\hand_landmarker.task" Local $_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task" Local $url, $file_path Local $sample_files[] = [ _ _Mediapipe_Tuple($_IMAGE_FILE, $_IMAGE_URL), _ _Mediapipe_Tuple($_MODEL_FILE, $_MODEL_URL) _ ] For $config In $sample_files $file_path = $config[0] $url = $config[1] If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next ; STEP 2: Create an ImageClassifier object. Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $vision.HandLandmarkerOptions(_Mediapipe_Params("base_options", $base_options, _ "num_hands", 2)) Local $detector = $vision.HandLandmarker.create_from_options($options) ; STEP 3: Load the input image. Local $image = $mp.Image.create_from_file($_IMAGE_FILE) ; STEP 4: Detect hand landmarks from the input image. Local $detection_result = $detector.detect($image) ; STEP 5: Process the classification result. In this case, visualize it. Local $annotated_image = draw_landmarks_on_image($image.mat_view(), $detection_result) resize_and_show($annotated_image, "hand_landmarker") $cv.waitKey() EndFunc ;==>Main Func draw_landmarks_on_image($rgb_image, $detection_result) ; Compute the scale to make drawn elements visible when the image is resized for display Local $scale = 1 / resize_and_show($rgb_image, Default, False) Local $MARGIN = 10 * $scale ; pixels Local $FONT_SIZE = $scale Local $FONT_THICKNESS = 2 * $scale Local $HANDEDNESS_TEXT_COLOR = _OpenCV_Scalar(88, 205, 54) ; vibrant green Local $hand_landmarks_list = $detection_result.hand_landmarks Local $handedness_list = $detection_result.handedness Local $annotated_image = $cv.cvtColor($rgb_image, $CV_COLOR_RGB2BGR) Local $width = $annotated_image.width Local $height = $annotated_image.height Local $hand_landmarks, $handedness Local $min_x, $min_y, $text_x, $text_y ; Loop through the detected hands to visualize. For $idx = 0 To UBound($hand_landmarks_list) - 1 $hand_landmarks = $hand_landmarks_list[$idx] $handedness = $handedness_list[$idx] $min_x = 1 $min_y = 1 For $landmark In $hand_landmarks If $landmark.x < $min_x Then $min_x = $landmark.x If $landmark.y < $min_y Then $min_y = $landmark.y Next ; Draw the hand landmarks. $mp_drawing.draw_landmarks( _ $annotated_image, _ $hand_landmarks, _ $mp_hands.HAND_CONNECTIONS, _ $mp_drawing_styles.get_default_hand_landmarks_style($scale), _ $mp_drawing_styles.get_default_hand_connections_style($scale)) ; Get the top left corner of the detected hand's bounding box. $text_x = $min_x * $width $text_y = $min_y * $height - $MARGIN ; Draw handedness (left or right hand) on the image. $cv.putText($annotated_image, $handedness[0].category_name, _ _OpenCV_Point($text_x, $text_y), $CV_FONT_HERSHEY_DUPLEX, _ $FONT_SIZE, $HANDEDNESS_TEXT_COLOR, $FONT_THICKNESS, $CV_LINE_AA) Next Return $annotated_image EndFunc ;==>draw_landmarks_on_image Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "image" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Image Classifier with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/image_classification/python/image_classifier.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/image_classification/python/image_classifier.ipynb ;~ Title: Image Classifier with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Main() Func Main() Local $IMAGE_FILENAMES[] = ['burger.jpg', 'cat.jpg'] Local $url, $file_path For $name In $IMAGE_FILENAMES $file_path = $MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $name $url = "https://storage.googleapis.com/mediapipe-tasks/image_classifier/" & $name If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\efficientnet_lite0.tflite" If Not FileExists($_MODEL_FILE) Then $download_utils.download("https://storage.googleapis.com/mediapipe-models/image_classifier/efficientnet_lite0/float32/1/efficientnet_lite0.tflite", $_MODEL_FILE) EndIf ; STEP 2: Create an ImageClassifier object. Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $vision.ImageClassifierOptions(_Mediapipe_Params("base_options", $base_options, "max_results", 4)) Local $classifier = $vision.ImageClassifier.create_from_options($options) Local $image, $classification_result, $top_category, $title For $image_name In $IMAGE_FILENAMES ; STEP 3: Load the input image. $image = $mp.Image.create_from_file($MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $image_name) ; STEP 4: Classify the input image. $classification_result = $classifier.classify($image) ; STEP 5: Process the classification result. In this case, visualize it. $top_category = $classification_result.classifications(0).categories(0) $title = StringFormat("%s (%.2f)", $top_category.category_name, $top_category.score) resize_and_show($cv.cvtColor($image.mat_view(), $CV_COLOR_RGB2BGR), $title) Next $cv.waitKey() EndFunc ;==>Main Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Image Embedding with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/image_embedder/python/image_embedder.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/image_embedder/python/image_embedder.ipynb ;~ Title: Image Embedding with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Global $cosine_similarity = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.components.utils.cosine_similarity") _AssertIsObj($cosine_similarity, "Failed to load mediapipe.tasks.autoit.components.utils.cosine_similarity") Main() Func Main() Local $IMAGE_FILENAMES[] = ['burger.jpg', 'burger_crop.jpg'] Local $url, $file_path For $name In $IMAGE_FILENAMES $file_path = $MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $name $url = "https://storage.googleapis.com/mediapipe-assets/" & $name If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\mobilenet_v3_small.tflite" If Not FileExists($_MODEL_FILE) Then $download_utils.download("https://storage.googleapis.com/mediapipe-models/image_embedder/mobilenet_v3_small/float32/1/mobilenet_v3_small.tflite", $_MODEL_FILE) EndIf ; Create options for Image Embedder Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $l2_normalize = True ;@param {type:"boolean"} Local $quantize = True ;@param {type:"boolean"} Local $options = $vision.ImageEmbedderOptions(_Mediapipe_Params( _ "base_options", $base_options, _ "l2_normalize", $l2_normalize, _ "quantize", $quantize)) ; Create Image Embedder Local $embedder = $vision.ImageEmbedder.create_from_options($options) ; Format images for MediaPipe Local $first_image = $mp.Image.create_from_file($MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $IMAGE_FILENAMES[0]) Local $second_image = $mp.Image.create_from_file($MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $IMAGE_FILENAMES[1]) Local $first_embedding_result = $embedder.embed($first_image) Local $second_embedding_result = $embedder.embed($second_image) Local $similarity = $cosine_similarity.cosine_similarity($first_embedding_result.embeddings(0), $second_embedding_result.embeddings(0)) ConsoleWrite('@@ Debug(' & @ScriptLineNumber & ') : $similarity = ' & $similarity & @CRLF) ;### Debug Console resize_and_show($cv.cvtColor($first_image.mat_view(), $CV_COLOR_RGB2BGR), $IMAGE_FILENAMES[0]) resize_and_show($cv.cvtColor($second_image.mat_view(), $CV_COLOR_RGB2BGR), $IMAGE_FILENAMES[1]) $cv.waitKey() EndFunc ;==>Main Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Image Segmenter #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/image_segmentation/python/image_segmentation.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/image_segmentation/python/image_segmentation.ipynb ;~ Title: Image Segmenter #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Main() Func Main() Local $IMAGE_FILENAMES[] = ['segmentation_input_rotation0.jpg'] Local $url, $file_path For $name In $IMAGE_FILENAMES $file_path = $MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $name $url = "https://storage.googleapis.com/mediapipe-assets/" & $name If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\deeplab_v3.tflite" If Not FileExists($_MODEL_FILE) Then $download_utils.download("https://storage.googleapis.com/mediapipe-models/image_segmenter/deeplab_v3/float32/1/deeplab_v3.tflite", $_MODEL_FILE) EndIf Local $BG_COLOR = _OpenCV_Scalar(192, 192, 192) ; gray Local $FG_COLOR = _OpenCV_Scalar(255, 255, 255) ; white ; Create the options that will be used for ImageSegmenter Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $vision.ImageSegmenterOptions(_Mediapipe_Params("base_options", $base_options, _ "output_category_mask", True)) ; Create the image segmenter Local $segmenter = $vision.ImageSegmenter.create_from_options($options) Local $image, $segmentation_result, $category_mask, $image_data Local $fg_image, $bg_image, $fg_mask Local $output_image, $blurred_image ; Loop through demo image(s) For $image_file_name In $IMAGE_FILENAMES ; Create the MediaPipe image file that will be segmented $image = $mp.Image.create_from_file($MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $image_file_name) ; Retrieve the masks for the segmented image $segmentation_result = $segmenter.segment($image) $category_mask = $segmentation_result.category_mask ; mediapipe uses RGB images while opencv uses BGR images $image_data = $cv.cvtColor($image.mat_view(), $CV_COLOR_RGB2BGR) ; Generate solid color images for showing the output segmentation mask. $fg_image = $cv.Mat.create($image_data.size(), $CV_8UC3, $FG_COLOR) $bg_image = $cv.Mat.create($image_data.size(), $CV_8UC3, $BG_COLOR) ; The foreground mask corresponds to all 'i' pixels where category_mask[i] > 0.2 $fg_mask = $cv.compare($category_mask.mat_view(), 0.2, $CV_CMP_GT) ; Draw fg_image on bg_image only where fg_mask should apply $output_image = $bg_image.copy() $fg_image.copyTo($fg_mask, $output_image) resize_and_show($output_image, 'Segmentation mask of ' & $image_file_name) ; Blur the image only where fg_mask should not apply $blurred_image = $cv.GaussianBlur($image_data, _OpenCV_Size(55, 55), 0) $image_data.copyTo($fg_mask, $blurred_image) resize_and_show($blurred_image, 'Blurred background of ' & $image_file_name) Next $cv.waitKey() EndFunc ;==>Main Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Interactive Image Segmenter #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/interactive_segmentation/python/interactive_segmenter.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/interactive_segmentation/python/interactive_segmenter.ipynb ;~ Title: Interactive Image Segmenter #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Global $containers = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.components.containers") _AssertIsObj($containers, "Failed to load mediapipe.tasks.autoit.components.containers") Main() Func Main() Local $IMAGE_FILENAMES[] = ['cats_and_dogs.jpg'] Local $url, $file_path For $name In $IMAGE_FILENAMES $file_path = $MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $name $url = "https://storage.googleapis.com/mediapipe-assets/" & $name If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\magic_touch.tflite" If Not FileExists($_MODEL_FILE) Then $download_utils.download("https://storage.googleapis.com/mediapipe-models/interactive_segmenter/magic_touch/float32/1/magic_touch.tflite", $_MODEL_FILE) EndIf Local $x = 0.68 ;@param {type:"slider", min:0, max:1, step:0.01} Local $y = 0.68 ;@param {type:"slider", min:0, max:1, step:0.01} Local $BG_COLOR = _OpenCV_Scalar(192, 192, 192) ; gray Local $FG_COLOR = _OpenCV_Scalar(255, 255, 255) ; white Local $OVERLAY_COLOR = _OpenCV_Scalar(100, 100, 0) ; cyan Local $RegionOfInterest = $vision.InteractiveSegmenterRegionOfInterest Local $NormalizedKeypoint = $containers.keypoint.NormalizedKeypoint ; Create the options that will be used for InteractiveSegmenter Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $vision.InteractiveSegmenterOptions(_Mediapipe_Params("base_options", $base_options, _ "output_category_mask", True)) ; Create the interactive segmenter Local $segmenter = $vision.InteractiveSegmenter.create_from_options($options) Local $image, $roi, $segmentation_result, $category_mask, $image_data Local $fg_image, $bg_image, $fg_mask Local $output_image, $blurred_image, $overlayed_image Local $keypoint_px, $alpha Local $color = _OpenCV_Scalar(255, 255, 0) Local $thickness, $radius, $scale ; Loop through demo image(s) For $image_file_name In $IMAGE_FILENAMES ; Create the MediaPipe image file that will be segmented $image = $mp.Image.create_from_file($MEDIAPIPE_SAMPLES_DATA_PATH & "\" & $image_file_name) ; Retrieve the masks for the segmented image $roi = $RegionOfInterest(_Mediapipe_Params("format", $MEDIAPIPE_TASKS_VISION_INTERACTIVE_SEGMENTER_REGION_OF_INTEREST_FORMAT_KEYPOINT, _ "keypoint", $NormalizedKeypoint($x, $y))) $segmentation_result = $segmenter.segment($image, $roi) $category_mask = $segmentation_result.category_mask ; mediapipe uses RGB images while opencv uses BGR images $image_data = $cv.cvtColor($image.mat_view(), $CV_COLOR_RGB2BGR) ; Generate solid color images for showing the output segmentation mask. $fg_image = $cv.Mat.create($image_data.size(), $CV_8UC3, $FG_COLOR) $bg_image = $cv.Mat.create($image_data.size(), $CV_8UC3, $BG_COLOR) ; The foreground mask corresponds to all 'i' pixels where category_mask[i] > 0.2 $fg_mask = $cv.compare($category_mask.mat_view(), 0.1, $CV_CMP_GT) ; Draw fg_image on bg_image only where fg_mask should apply $output_image = $bg_image.copy() $fg_image.copyTo($fg_mask, $output_image) ; Compute the point of interest coordinates $keypoint_px = _normalized_to_pixel_coordinates($x, $y, $image.width, $image.height) ; Compute the scale to make drawn elements visible when the image is resized for display $scale = 1 / resize_and_show($image, Default, False) $thickness = 10 * $scale $radius = 2 * $scale ; Draw a circle to denote the point of interest $cv.circle($output_image, $keypoint_px, $thickness, $color, $radius) ; Display the segmented image resize_and_show($output_image, 'Segmentation mask of ' & $image_file_name) ; Blur the image only where fg_mask should not apply $blurred_image = $cv.GaussianBlur($image_data, _OpenCV_Size(55, 55), 0) $image_data.copyTo($fg_mask, $blurred_image) ; Draw a circle to denote the point of interest $cv.circle($blurred_image, $keypoint_px, $thickness, $color, $radius) ; Display the blurred image resize_and_show($blurred_image, 'Blurred background of ' & $image_file_name) ; Create an overlay image with the desired color (e.g., (255, 0, 0) for red) $overlayed_image = $cv.Mat.create($image_data.size(), $CV_8UC3, $OVERLAY_COLOR) ; Create an alpha channel based on the segmentation mask with the desired opacity (e.g., 0.7 for 70%) ; fg_mask values are 0 where the mask should not apply and 255 where it should ; multiplying by 0.7 / 255.0 gives values that are 0 where the mask should not apply and 0.7 where it should $alpha = $fg_mask.convertTo($CV_32F, Null, 0.7 / 255.0) ; repeat the alpha mask for each image channel color $alpha = $cv.merge(_OpenCV_Tuple($alpha, $alpha, $alpha)) ; Blend the original image and the overlay image based on the alpha channel $overlayed_image = $cv.add($cv.multiply($image_data, $cv.subtract(1.0, $alpha), Null, Default, $CV_32F), $cv.multiply($overlayed_image, $alpha, Null, Default, $CV_32F)) ; Draw a circle to denote the point of interest $cv.circle($overlayed_image, $keypoint_px, $thickness, $color, $radius) ; Display the overlayed image resize_and_show($overlayed_image, 'Overlayed foreground of ' & $image_file_name) Next $cv.waitKey() EndFunc ;==>Main Func isclose($a, $b) Return Abs($a - $b) <= 1E-6 EndFunc ;==>isclose ; Checks if the float value is between 0 and 1. Func is_valid_normalized_value($value) Return ($value > 0 Or isclose(0, $value)) And ($value < 1 Or isclose(1, $value)) EndFunc ;==>is_valid_normalized_value #cs Converts normalized value pair to pixel coordinates. #ce Func _normalized_to_pixel_coordinates($normalized_x, $normalized_y, $image_width, $image_height) If Not (is_valid_normalized_value($normalized_x) And is_valid_normalized_value($normalized_y)) Then ; TODO: Draw coordinates even if it's outside of the image bounds. Return Default EndIf Local $x_px = _Min(Floor($normalized_x * $image_width), $image_width - 1) Local $y_px = _Min(Floor($normalized_y * $image_height), $image_height - 1) Return _OpenCV_Point($x_px, $y_px) EndFunc ;==>_normalized_to_pixel_coordinates Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Language Detector with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/language_detector/python/%5BMediaPipe_Python_Tasks%5D_Language_Detector.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/language_detector/python/%5BMediaPipe_Python_Tasks%5D_Language_Detector.ipynb ;~ Title: Language Detector with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" ; STEP 1: Import the necessary modules. Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $text = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.text") _AssertIsObj($text, "Failed to load mediapipe.tasks.autoit.text") Main() Func Main() Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\language_detector.tflite" Local $_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/language_detector/language_detector/float32/latest/language_detector.tflite" Local $url, $file_path Local $sample_files[] = [ _ _Mediapipe_Tuple($_MODEL_FILE, $_MODEL_URL) _ ] For $config In $sample_files $file_path = $config[0] $url = $config[1] If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next ; Define the input text that you wants the model to classify. Local $INPUT_TEXT = "分久必合合久必分" ;@param {type:"string"} ; STEP 2: Create a LanguageDetector object. Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $text.LanguageDetectorOptions(_Mediapipe_Params("base_options", $base_options)) Local $detector = $text.LanguageDetector.create_from_options($options) ; STEP 3: Get the language detcetion result for the input text. Local $detection_result = $detector.detect($INPUT_TEXT) ; STEP 4: Process the detection result and print the languages detected and their scores. For $detection In $detection_result.detections ConsoleWrite(StringFormat("%s: (%.2f)", $detection.language_code, $detection.probability) & @CRLF) Next EndFunc ;==>Main Func _OnAutoItExit() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Object Detection with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/object_detection/python/object_detector.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/object_detection/python/object_detector.ipynb ;~ Title: Object Detection with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Main() Func Main() Local $_IMAGE_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\cat_and_dog.jpg" Local $_IMAGE_URL = "https://storage.googleapis.com/mediapipe-tasks/object_detector/cat_and_dog.jpg" Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\efficientdet_lite0.tflite" Local $_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/object_detector/efficientdet_lite0/int8/1/efficientdet_lite0.tflite" Local $url, $file_path Local $sample_files[] = [ _ _Mediapipe_Tuple($_IMAGE_FILE, $_IMAGE_URL), _ _Mediapipe_Tuple($_MODEL_FILE, $_MODEL_URL) _ ] For $config In $sample_files $file_path = $config[0] $url = $config[1] If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next ; STEP 2: Create an ObjectDetector object. Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $vision.ObjectDetectorOptions(_Mediapipe_Params("base_options", $base_options, _ "score_threshold", 0.5)) Local $detector = $vision.ObjectDetector.create_from_options($options) ; STEP 3: Load the input image. Local $image = $mp.Image.create_from_file($_IMAGE_FILE) ; Compute the scale to make drawn elements visible when the image is resized for display Local $scale = 1 / resize_and_show($image.mat_view(), Default, False) ; STEP 4: Detect objects in the input image. Local $detection_result = $detector.detect($image) ; STEP 5: Process the detection result. In this case, visualize it. Local $annotated_image = visualize($image.mat_view(), $detection_result, $scale) resize_and_show($annotated_image, "object_detection") $cv.waitKey() ; STEP 6: Closes the detector explicitly when the detector is not used ina context. $detector.close() EndFunc ;==>Main #cs Draws bounding boxes and keypoints on the input image and return it. Args: rgb_image: The input RGB image. detection_result: The list of all "Detection" entities to be visualize. scale: Scale to keep drawing visible after resize Returns: Image with bounding boxes. #ce Func visualize($rgb_image, $detection_result, $scale = 1.0) Local $MARGIN = 10 * $scale ; pixels Local $ROW_SIZE = 10 ; pixels Local $FONT_SIZE = $scale Local $FONT_THICKNESS = $scale Local $TEXT_COLOR = _OpenCV_RGB(255, 0, 0) ; red Local $annotated_image = $cv.cvtColor($rgb_image, $CV_COLOR_RGB2BGR) Local $bbox, $start_point, $end_point Local $category, $category_name, $probability, $result_text, $text_location For $detection In $detection_result.detections ; Draw bounding_box $bbox = $detection.bounding_box $start_point = _OpenCV_Point($bbox.origin_x, $bbox.origin_y) $end_point = _OpenCV_Point($bbox.origin_x + $bbox.width, $bbox.origin_y + $bbox.height) $cv.rectangle($annotated_image, $start_point, $end_point, $TEXT_COLOR, 3) ; Draw label and score $category = $detection.categories(0) $category_name = $category.category_name $probability = Round($category.score, 2) $result_text = $category_name & ' (' & $probability & ')' $text_location = _OpenCV_Point($MARGIN + $bbox.origin_x, $MARGIN + $ROW_SIZE + $bbox.origin_y) $cv.putText($annotated_image, $result_text, $text_location, $CV_FONT_HERSHEY_PLAIN, $FONT_SIZE, $TEXT_COLOR, $FONT_THICKNESS) Next Return $annotated_image EndFunc ;==>visualize Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Pose Landmarks Detection with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/pose_landmarker/python/%5BMediaPipe_Python_Tasks%5D_Pose_Landmarker.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/pose_landmarker/python/%5BMediaPipe_Python_Tasks%5D_Pose_Landmarker.ipynb ;~ Title: Pose Landmarks Detection with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") _OpenCV_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-opencv-com\autoit_opencv_com4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $cv = _OpenCV_get() _AssertIsObj($cv, "Failed to load opencv") Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $drawing_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision.drawing_utils") _AssertIsObj($drawing_utils, "Failed to load mediapipe.tasks.autoit.vision.drawing_utils") Global $drawing_styles = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision.drawing_styles") _AssertIsObj($drawing_styles, "Failed to load mediapipe.tasks.autoit.vision.drawing_styles") Global $vision = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.vision") _AssertIsObj($vision, "Failed to load mediapipe.tasks.autoit.vision") Main() Func Main() Local $_IMAGE_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\girl-4051811_960_720.jpg" Local $_IMAGE_URL = "https://cdn.pixabay.com/photo/2019/03/12/20/39/girl-4051811_960_720.jpg" Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\pose_landmarker_heavy.task" Local $_MODEL_URL = "https://storage.googleapis.com/mediapipe-models/pose_landmarker/pose_landmarker_heavy/float16/1/pose_landmarker_heavy.task" Local $url, $file_path Local $sample_files[] = [ _ _Mediapipe_Tuple($_IMAGE_FILE, $_IMAGE_URL), _ _Mediapipe_Tuple($_MODEL_FILE, $_MODEL_URL) _ ] For $config In $sample_files $file_path = $config[0] $url = $config[1] If Not FileExists($file_path) Then $download_utils.download($url, $file_path) EndIf Next ; STEP 2: Create a PoseLandmarker object. Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $vision.PoseLandmarkerOptions(_Mediapipe_Params( _ "base_options", $base_options, _ "output_segmentation_masks", True)) Local $detector = $vision.PoseLandmarker.create_from_options($options) ; STEP 3: Load the input image. Local $image = $mp.Image.create_from_file($_IMAGE_FILE) ; STEP 4: Detect pose landmarks from the input image. Local $detection_result = $detector.detect($image) ; STEP 5: Process the detection result. In this case, visualize it. Local $annotated_image = draw_landmarks_on_image($image.mat_view(), $detection_result) ; Display the image resize_and_show($annotated_image, "Pose Landmarks Detection with MediaPipe Tasks : Image") ; Visualize the pose segmentation mask. Local $segmentation_mask = $detection_result.segmentation_masks(0).mat_view() resize_and_show($segmentation_mask, "Pose Landmarks Detection with MediaPipe Tasks : Mask") $cv.waitKey() EndFunc ;==>Main Func draw_landmarks_on_image($rgb_image, $detection_result) ; Compute the scale to make drawn elements visible when the image is resized for display Local $scale = 1 / resize_and_show($rgb_image, Default, False) Local $pose_landmarks_list = $detection_result.pose_landmarks Local $annotated_image = $cv.cvtColor($rgb_image, $CV_COLOR_RGB2BGR) Local $pose_landmark_style = $drawing_styles.get_default_pose_landmarks_style($scale) Local $pose_connection_style = $drawing_utils.DrawingSpec(_Mediapipe_Params("color", _Mediapipe_Tuple(0, 255, 0), "thickness", 2)) ; Loop through the detected poses to visualize. For $pose_landmarks In $pose_landmarks_list ; Draw the pose landmarks. $drawing_utils.draw_landmarks(_Mediapipe_Params( _ "image", $annotated_image, _ "landmark_list", $pose_landmarks, _ "connections", $vision.PoseLandmarksConnections.POSE_LANDMARKS, _ "landmark_drawing_spec", $pose_landmark_style, _ "connection_drawing_spec", $pose_connection_style)) Next Return $annotated_image EndFunc ;==>draw_landmarks_on_image Func resize_and_show($image, $title = Default, $show = Default) If $title == Default Then $title = "" If $show == Default Then $show = True Local Const $DESIRED_HEIGHT = 480 Local Const $DESIRED_WIDTH = 480 Local $w = $image.width Local $h = $image.height If $h < $w Then $h = $h / ($w / $DESIRED_WIDTH) $w = $DESIRED_WIDTH Else $w = $w / ($h / $DESIRED_HEIGHT) $h = $DESIRED_HEIGHT EndIf Local $interpolation = ($DESIRED_WIDTH > $image.width Or $DESIRED_HEIGHT > $image.height) ? $CV_INTER_CUBIC : $CV_INTER_AREA If $show Then Local $img = $cv.resize($image, _OpenCV_Size($w, $h), _OpenCV_Params("interpolation", $interpolation)) $cv.imshow($title, $img.convertToShow()) EndIf Return $w / $image.width EndFunc ;==>resize_and_show Func _OnAutoItExit() _OpenCV_Close() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Text Classifier with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/text_classification/python/text_classifier.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/text_classification/python/text_classifier.ipynb ;~ Title: Text Classifier with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $text = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.text") _AssertIsObj($text, "Failed to load mediapipe.tasks.autoit.text") Main() Func Main() Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\bert_classifier.tflite" If Not FileExists($_MODEL_FILE) Then $download_utils.download("https://storage.googleapis.com/mediapipe-models/text_classifier/bert_classifier/float32/1/bert_classifier.tflite", $_MODEL_FILE) EndIf ; Define the input text that you want the model to classify. Local $INPUT_TEXT = "I'm looking forward to what will come next." ; STEP 2: Create a TextClassifier object. Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) Local $options = $text.TextClassifierOptions(_Mediapipe_Params("base_options", $base_options)) Local $classifier = $text.TextClassifier.create_from_options($options) ; STEP 3: Classify the input text. Local $classification_result = $classifier.classify($INPUT_TEXT) ; STEP 4: Process the classification result. In this case, print out the most likely category. Local $top_category = $classification_result.classifications(0).categories(0) ConsoleWrite('@@ Debug(' & @ScriptLineNumber & ') : ' & StringFormat('%s (%.2f)', $top_category.category_name, $top_category.score) & @CRLF) ;### Debug Console EndFunc ;==>Main Func _OnAutoItExit() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj Text Embedding with MediaPipe Tasks #Region ;**** Directives created by AutoIt3Wrapper_GUI **** #AutoIt3Wrapper_UseX64=y #AutoIt3Wrapper_Change2CUI=y #AutoIt3Wrapper_Au3Check_Parameters=-d -w 1 -w 2 -w 3 -w 4 -w 5 -w 6 #AutoIt3Wrapper_AU3Check_Stop_OnWarning=y #EndRegion ;**** Directives created by AutoIt3Wrapper_GUI **** ;~ Sources: ;~ https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/text_embedder/python/text_embedder.ipynb ;~ https://github.com/google-ai-edge/mediapipe-samples/blob/3d23f0e459907af064c3e7494dbb180851e1694c/examples/text_embedder/python/text_embedder.ipynb ;~ Title: Text Embedding with MediaPipe Tasks #include "autoit-mediapipe-com\udf\mediapipe_udf_utils.au3" #include "autoit-opencv-com\udf\opencv_udf_utils.au3" _Mediapipe_Open("opencv-4.13.0-windows\opencv\build\x64\vc16\bin\opencv_world4130.dll", "autoit-mediapipe-com\autoit_mediapipe_com-0.10.35-4130.dll") OnAutoItExitRegister("_OnAutoItExit") ; Where to download data files Global Const $MEDIAPIPE_SAMPLES_DATA_PATH = @ScriptDir & "\examples\data" Global $download_utils = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.core.download_utils") _AssertIsObj($download_utils, "Failed to load mediapipe.tasks.autoit.core.download_utils") ; STEP 1: Import the necessary modules. Global $mp = _Mediapipe_get() _AssertIsObj($mp, "Failed to load mediapipe") Global $autoit = _Mediapipe_ObjCreate("mediapipe.tasks.autoit") _AssertIsObj($autoit, "Failed to load mediapipe.tasks.autoit") Global $text = _Mediapipe_ObjCreate("mediapipe.tasks.autoit.text") _AssertIsObj($text, "Failed to load mediapipe.tasks.autoit.text") Main() Func Main() Local $_MODEL_FILE = $MEDIAPIPE_SAMPLES_DATA_PATH & "\bert_embedder.tflite" If Not FileExists($_MODEL_FILE) Then $download_utils.download("https://storage.googleapis.com/mediapipe-models/text_embedder/bert_embedder/float32/1/bert_embedder.tflite", $_MODEL_FILE) EndIf ; Create your base options with the model that was downloaded earlier Local $base_options = $autoit.BaseOptions(_Mediapipe_Params("model_asset_path", $_MODEL_FILE)) ; Set your values for using normalization and quantization Local $l2_normalize = True ;@param {type:"boolean"} Local $quantize = False ;@param {type:"boolean"} ; Create the final set of options for the Embedder Local $options = $text.TextEmbedderOptions(_Mediapipe_Params( _ "base_options", $base_options, "l2_normalize", $l2_normalize, "quantize", $quantize)) Local $embedder = $text.TextEmbedder.create_from_options($options) ; Retrieve the first and second sets of text that will be compared Local $first_text = "I'm feeling so good" ;@param {type:"string"} Local $second_text = "I'm okay I guess" ;@param {type:"string"} ; Convert both sets of text to embeddings Local $first_embedding_result = $embedder.embed($first_text) Local $second_embedding_result = $embedder.embed($second_text) ; Retrieve the cosine similarity value from both sets of text, then take the ; cosine of that value to receie a decimal similarity value. Local $similarity = $text.TextEmbedder.cosine_similarity($first_embedding_result.embeddings(0), _ $second_embedding_result.embeddings(0)) ConsoleWrite('@@ Debug(' & @ScriptLineNumber & ') : $similarity = ' & $similarity & @CRLF) ;### Debug Console EndFunc ;==>Main Func _OnAutoItExit() _Mediapipe_Close() EndFunc ;==>_OnAutoItExit Func _AssertIsObj($vVal, $sMsg) If Not IsObj($vVal) Then ConsoleWriteError($sMsg & @CRLF) Exit 0x7FFFFFFF EndIf EndFunc ;==>_AssertIsObj