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Jimmy Allen 1c24efd919 Add add gesture recognition 2024-01-02 18:00:02 +13:00
Jimmy Allen b964a4c7a7 Ignore venv and jpg task files 2024-01-02 17:59:24 +13:00
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.venv
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*.task

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# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Main scripts to run gesture recognition."""
import argparse
import sys
import time
import cv2
import mediapipe as mp
from picamera2 import Picamera2
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from mediapipe.framework.formats import landmark_pb2
mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
# Global variables to calculate FPS
COUNTER, FPS = 0, 0
START_TIME = time.time()
def run(model: str, num_hands: int,
min_hand_detection_confidence: float,
min_hand_presence_confidence: float, min_tracking_confidence: float,
camera_id: int, width: int, height: int) -> None:
"""Continuously run inference on images acquired from the camera.
Args:
model: Name of the gesture recognition model bundle.
num_hands: Max number of hands can be detected by the recognizer.
min_hand_detection_confidence: The minimum confidence score for hand
detection to be considered successful.
min_hand_presence_confidence: The minimum confidence score of hand
presence score in the hand landmark detection.
min_tracking_confidence: The minimum confidence score for the hand
tracking to be considered successful.
camera_id: The camera id to be passed to OpenCV.
width: The width of the frame captured from the camera.
height: The height of the frame captured from the camera.
"""
# Start capturing video input from the camera
picam2 = Picamera2()
picam2.configure(picam2.create_preview_configuration(
main={"format": 'XRGB8888', "size": (1280, 720)}))
picam2.start()
# Visualization parameters
row_size = 50 # pixels
left_margin = 24 # pixels
text_color = (0, 0, 0) # black
font_size = 1
font_thickness = 1
fps_avg_frame_count = 10
# Label box parameters
label_text_color = (255, 255, 255) # white
label_font_size = 1
label_thickness = 2
recognition_frame = None
recognition_result_list = []
def save_result(result: vision.GestureRecognizerResult,
unused_output_image: mp.Image, timestamp_ms: int):
global FPS, COUNTER, START_TIME
# Calculate the FPS
if COUNTER % fps_avg_frame_count == 0:
FPS = fps_avg_frame_count / (time.time() - START_TIME)
START_TIME = time.time()
recognition_result_list.append(result)
COUNTER += 1
# Initialize the gesture recognizer model
base_options = python.BaseOptions(model_asset_path=model)
options = vision.GestureRecognizerOptions(base_options=base_options,
running_mode=vision.RunningMode.LIVE_STREAM,
num_hands=num_hands,
min_hand_detection_confidence=min_hand_detection_confidence,
min_hand_presence_confidence=min_hand_presence_confidence,
min_tracking_confidence=min_tracking_confidence,
result_callback=save_result)
recognizer = vision.GestureRecognizer.create_from_options(options)
# Continuously capture images from the camera and run inference
while True:
image = picam2.capture_array()
image = cv2.flip(cv2.rotate(image, cv2.ROTATE_180),1)
# Convert the image from BGR to RGB as required by the TFLite model.
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image)
# Run gesture recognizer using the model.
recognizer.recognize_async(mp_image, time.time_ns() // 1_000_000)
# Show the FPS
fps_text = 'FPS = {:.1f}'.format(FPS)
text_location = (left_margin, row_size)
current_frame = image
cv2.putText(current_frame, fps_text, text_location, cv2.FONT_HERSHEY_DUPLEX,
font_size, text_color, font_thickness, cv2.LINE_AA)
if recognition_result_list:
# Draw landmarks and write the text for each hand.
for hand_index, hand_landmarks in enumerate(
recognition_result_list[0].hand_landmarks):
# Calculate the bounding box of the hand
x_min = min([landmark.x for landmark in hand_landmarks])
y_min = min([landmark.y for landmark in hand_landmarks])
y_max = max([landmark.y for landmark in hand_landmarks])
# Convert normalized coordinates to pixel values
frame_height, frame_width = current_frame.shape[:2]
x_min_px = int(x_min * frame_width)
y_min_px = int(y_min * frame_height)
y_max_px = int(y_max * frame_height)
# Get gesture classification results
if recognition_result_list[0].gestures:
gesture = recognition_result_list[0].gestures[hand_index]
category_name = gesture[0].category_name
score = round(gesture[0].score, 2)
result_text = f'{category_name} ({score})'
# Compute text size
text_size = \
cv2.getTextSize(result_text, cv2.FONT_HERSHEY_DUPLEX, label_font_size,
label_thickness)[0]
text_width, text_height = text_size
# Calculate text position (above the hand)
text_x = x_min_px
text_y = y_min_px - 10 # Adjust this value as needed
# Make sure the text is within the frame boundaries
if text_y < 0:
text_y = y_max_px + text_height
# Draw the text
cv2.putText(current_frame, result_text, (text_x, text_y),
cv2.FONT_HERSHEY_DUPLEX, label_font_size,
label_text_color, label_thickness, cv2.LINE_AA)
# Draw hand landmarks on the frame
hand_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
hand_landmarks_proto.landmark.extend([
landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y,
z=landmark.z) for landmark in
hand_landmarks
])
mp_drawing.draw_landmarks(
current_frame,
hand_landmarks_proto,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style())
recognition_frame = current_frame
recognition_result_list.clear()
if recognition_frame is not None:
recognition_frame = cv2.cvtColor(recognition_frame, cv2.COLOR_RGB2BGR)
cv2.imshow('gesture_recognition', recognition_frame)
# Stop the program if the ESC key is pressed.
if cv2.waitKey(1) == 27:
break
recognizer.close()
cap.release()
cv2.destroyAllWindows()
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--model',
help='Name of gesture recognition model.',
required=False,
default='gesture_recognizer.task')
parser.add_argument(
'--numHands',
help='Max number of hands that can be detected by the recognizer.',
required=False,
default=1)
parser.add_argument(
'--minHandDetectionConfidence',
help='The minimum confidence score for hand detection to be considered '
'successful.',
required=False,
default=0.5)
parser.add_argument(
'--minHandPresenceConfidence',
help='The minimum confidence score of hand presence score in the hand '
'landmark detection.',
required=False,
default=0.5)
parser.add_argument(
'--minTrackingConfidence',
help='The minimum confidence score for the hand tracking to be '
'considered successful.',
required=False,
default=0.5)
# Finding the camera ID can be very reliant on platform-dependent methods.
# One common approach is to use the fact that camera IDs are usually indexed sequentially by the OS, starting from 0.
# Here, we use OpenCV and create a VideoCapture object for each potential ID with 'cap = cv2.VideoCapture(i)'.
# If 'cap' is None or not 'cap.isOpened()', it indicates the camera ID is not available.
parser.add_argument(
'--cameraId', help='Id of camera.', required=False, default=0)
parser.add_argument(
'--frameWidth',
help='Width of frame to capture from camera.',
required=False,
default=640)
parser.add_argument(
'--frameHeight',
help='Height of frame to capture from camera.',
required=False,
default=480)
args = parser.parse_args()
run(args.model, int(args.numHands), args.minHandDetectionConfidence,
args.minHandPresenceConfidence, args.minTrackingConfidence,
int(args.cameraId), args.frameWidth, args.frameHeight)
if __name__ == '__main__':
main()