import cv2
import mediapipe as mp
mp_holistic = mp.solutions.holistic
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands
IMAGE_FILES = []
###################################################################################
# # motion_hands
# with mp_hands.Hands(
# static_image_mode=True,
# max_num_hands=2,
# min_detection_confidence=0.5) as hands:
#
# for idx, file in enumerate(IMAGE_FILES):
# # Read an image, flip it around y-axis for correct handedness output (see
# # above).
# image = cv2.flip(cv2.imread(file), 1)
# # Convert the BGR image to RGB before processing.
# #results1 = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
#
# # Print handedness and draw hand landmarks on the image.
# # print('Handedness:', results1.multi_handedness)
#
# if not results1.multi_hand_landmarks:
# continue
# #image_height, image_width, _ = image.shape
# #annotated_image = image.copy()
#
# for hand_landmarks in results1.multi_hand_landmarks:
# print('hand_landmarks:', hand_landmarks)
# print(
# f'Index finger tip coordinates: (',
# f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '
# f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height})'
# )
# mp_drawing.draw_landmarks(
# annotated_image,
# hand_landmarks,
# mp_hands.HAND_CONNECTIONS,
# mp_drawing_styles.get_default_hand_landmarks_style(),
# mp_drawing_styles.get_default_hand_connections_style())
#
# cv2.imwrite(
# '/tmp/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))
# # Draw hand world landmarks.
# if not results1.multi_hand_world_landmarks:
# continue
# for hand_world_landmarks in results1.multi_hand_world_landmarks:
# mp_drawing.plot_landmarks(
# hand_world_landmarks, mp_hands.HAND_CONNECTIONS, azimuth=5)
#############################################################################
# For static images:
with mp_holistic.Holistic(
static_image_mode=True,
model_complexity=2,
enable_segmentation=True,
refine_face_landmarks=True) as holistic:
hands = mp_hands.Hands(
model_complexity=0,
min_detection_confidence=0.5,
min_tracking_confidence=0.5)
for idx, file in enumerate(IMAGE_FILES):
image = cv2.imread(file)
image_height, image_width, _ = image.shape
# Convert the BGR image to RGB before processing.
results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
######################### hands image setting ###################################
results1 = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
print('Handedness:', results1.multi_handedness)
# if not results1.multi_hand_landmarks:
# continue
#
# for hand_landmarks in results1.multi_hand_landmarks:
# print('hand_landmarks:', hand_landmarks)
# print(
# f'Index finger tip coordinates: (',
# f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '
# f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height})'
# )
# mp_drawing.draw_landmarks(
# annotated_image,
# hand_landmarks,
# mp_hands.HAND_CONNECTIONS,
# mp_drawing_styles.get_default_hand_landmarks_style(),
# mp_drawing_styles.get_default_hand_connections_style())
#
# # Draw hand world landmarks.
# if not results1.multi_hand_world_landmarks:
# continue
# for hand_world_landmarks in results1.multi_hand_world_landmarks:
# mp_drawing.plot_landmarks(
# hand_world_landmarks, mp_hands.HAND_CONNECTIONS, azimuth=5)
######################### hands image setting ###################################
if results.pose_landmarks:
print(
f'Nose coordinates: ('
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_height})'
)
annotated_image = image.copy()
# Draw segmentation on the image.
# To improve segmentation around boundaries, consider applying a joint
# bilateral filter to "results.segmentation_mask" with "image".
condition = mp.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
bg_image =mp.zeros(image.shape, dtype=mp.uint8)
bg_image[:] = BG_COLOR
annotated_image = mp.where(condition, annotated_image, bg_image)
# Draw pose, left and right hands, and face landmarks on the image.
mp_drawing.draw_landmarks(
annotated_image,
results.face_landmarks,
mp_holistic.FACEMESH_TESSELATION,
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles
.get_default_face_mesh_tesselation_style())
mp_drawing.draw_landmarks(
annotated_image,
results.pose_landmarks,
mp_holistic.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing_styles.
get_default_pose_landmarks_style())
cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
# Plot pose world landmarks.
mp_drawing.plot_landmarks(
results.pose_world_landmarks, mp_holistic.POSE_CONNECTIONS)
# For webcam input:
cap = cv2.VideoCapture(0)
#webcam window size
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 3200)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1800)
with mp_holistic.Holistic(
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as holistic:
hands = mp_hands.Hands(
model_complexity=0,
min_detection_confidence=0.5,
min_tracking_confidence=0.5)
while cap.isOpened():
success, image = cap.read()
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
imageRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = holistic.process(imageRGB)
results1 = hands.process(imageRGB)
print(results.pose_landmarks)
# Draw the hand annotations on the image.
image.flags.writeable = True
imageRGB = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
### hands start ######
# draw hands
if results1.multi_hand_landmarks:
for hand_landmarks in results1.multi_hand_landmarks:
mp_drawing.draw_landmarks(
image,
hand_landmarks,
mp_hands.HAND_CONNECTIONS,
mp_drawing_styles.get_default_hand_landmarks_style(),
mp_drawing_styles.get_default_hand_connections_style()
)
#### hands end ####
mp_drawing.draw_landmarks(
image,
results.face_landmarks,
mp_holistic.FACEMESH_CONTOURS,
landmark_drawing_spec=None,
connection_drawing_spec=mp_drawing_styles
.get_default_face_mesh_contours_style())
mp_drawing.draw_landmarks(
image,
results.pose_landmarks,
mp_holistic.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing_styles
.get_default_pose_landmarks_style())
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1))
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()>> pose_estimation.py
# This is a _very simple_ example of a web service that recognizes faces in uploaded images.
# Upload an image file and it will check if the image contains a picture of Barack Obama.
# The result is returned as json. For example:
#
# $ curl -XPOST -F "file=@obama2.jpg" http://127.0.0.1:5001
#
# Returns:
#
# {
# "face_found_in_image": true,
# "is_picture_of_obama": true
# }
#
# This example is based on the Flask file upload example: http://flask.pocoo.org/docs/0.12/patterns/fileuploads/
# NOTE: This example requires flask to be installed! You can install it with pip:
# $ pip3 install flask
import face_recognition
from flask import Flask, jsonify, request, redirect
# You can change this to any folder on your system
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
app = Flask(__name__)
def return_func():
return '''
<!doctype html>
<title>Is this a picture of Obama?</title>
<h1>Upload a picture and see if it's a picture of Obama!</h1>
<form method="POST" enctype="multipart/form-data" action="/">
<input type="file" name="file">
<input type="submit" value="Upload">
<br>
<hr>
<br>
</form>
<input type="button" value="모션 디렉터" onclick="location.href='/?item=cam'">
'''
# motion tracking module run
def motion_webcam():
print ("motion_webcam is active")
return_func()
exec(open("pose_estimation.py").read())
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
@app.route('/', methods=['GET', 'POST'])
def upload_image():
print( request.args.get('item') == 'cam')
# motion director request filter
if request.args.get('item') == 'cam':
print('cam connection ! : ')
motion_webcam()
# Check if a valid image file was uploaded
if request.method == 'POST':
if 'file' not in request.files:
return redirect(request.url)
file = request.files['file']
if file.filename == '':
return redirect(request.url)
if file and allowed_file(file.filename):
# The image file seems valid! Detect faces and return the result.
return detect_faces_in_image(file)
# If no valid image file was uploaded, show the file upload form:
return return_func()
def detect_faces_in_image(file_stream):
# Pre-calculated face encoding of Obama generated with face_recognition.face_encodings(img)
known_face_encoding = [-0.09634063, 0.12095481, -0.00436332, -0.07643753, 0.0080383,
0.01902981, -0.07184699, -0.09383309, 0.18518871, -0.09588896,
0.23951106, 0.0986533 , -0.22114635, -0.1363683 , 0.04405268,
0.11574756, -0.19899382, -0.09597053, -0.11969153, -0.12277931,
0.03416885, -0.00267565, 0.09203379, 0.04713435, -0.12731361,
-0.35371891, -0.0503444 , -0.17841317, -0.00310897, -0.09844551,
-0.06910533, -0.00503746, -0.18466514, -0.09851682, 0.02903969,
-0.02174894, 0.02261871, 0.0032102 , 0.20312519, 0.02999607,
-0.11646006, 0.09432904, 0.02774341, 0.22102901, 0.26725179,
0.06896867, -0.00490024, -0.09441824, 0.11115381, -0.22592428,
0.06230862, 0.16559327, 0.06232892, 0.03458837, 0.09459756,
-0.18777156, 0.00654241, 0.08582542, -0.13578284, 0.0150229 ,
0.00670836, -0.08195844, -0.04346499, 0.03347827, 0.20310158,
0.09987706, -0.12370517, -0.06683611, 0.12704916, -0.02160804,
0.00984683, 0.00766284, -0.18980607, -0.19641446, -0.22800779,
0.09010898, 0.39178532, 0.18818057, -0.20875394, 0.03097027,
-0.21300618, 0.02532415, 0.07938635, 0.01000703, -0.07719778,
-0.12651891, -0.04318593, 0.06219772, 0.09163868, 0.05039065,
-0.04922386, 0.21839413, -0.02394437, 0.06173781, 0.0292527 ,
0.06160797, -0.15553983, -0.02440624, -0.17509389, -0.0630486 ,
0.01428208, -0.03637431, 0.03971229, 0.13983178, -0.23006812,
0.04999552, 0.0108454 , -0.03970895, 0.02501768, 0.08157793,
-0.03224047, -0.04502571, 0.0556995 , -0.24374914, 0.25514284,
0.24795187, 0.04060191, 0.17597422, 0.07966681, 0.01920104,
-0.01194376, -0.02300822, -0.17204897, -0.0596558 , 0.05307484,
0.07417042, 0.07126575, 0.00209804]
# Load the uploaded image file
img = face_recognition.load_image_file(file_stream)
# Get face encodings for any faces in the uploaded image
unknown_face_encodings = face_recognition.face_encodings(img)
face_found = False
is_obama = False
if len(unknown_face_encodings) > 0:
face_found = True
# See if the first face in the uploaded image matches the known face of Obama
match_results = face_recognition.compare_faces([known_face_encoding], unknown_face_encodings[0])
if match_results[0]:
is_obama = True
# Return the result as json
result = {
"face_found_in_image": face_found,
"is_picture_of_obama": is_obama
}
return jsonify(result)
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5001, debug=True)
>> web_service_example.py
위 두 소스를 추가 .
해당 경로 web_service_example.py 실행 .
============================================================================
OS환경 :
Windows 10 Pro /
Intel(R) Core(TM) i7-10750H CPU @ 2.60GHz 2.59 GHz /
RAM 16.0GB /
GeForce GTX 1650 Ti
개발환경 :
tensorflow 2.7.0/
opencv 4.5.5 /
python 3.7 /
cuda 10.1 /
cudnn 7.6.5 /
anaconda 4.11.0(파이썬 가상환경)/
visual studio c++ 2017/ CMAKE
API >>
face_recognition / dlib
mediapipe / picamera / flask / pickle
[환경변수]
1. anaconad
D:\ssss\company work\projects\dev_env\anaconda\Library\bin
D:\ssss\company work\projects\dev_env\anaconda\Scripts
D:\ssss\company work\projects\dev_env\anaconda
2. python
D:\ssss\company work\projects\dev_env\pyhon3.10.2
D:\ssss\company work\projects\dev_env\pyhon3.10.2\Scripts
D:\ssss\company work\projects\dev_env\anaconda\Library\mingw-w64\bin
3. CUDA
D:\ssss\company work\projects\dev_env\anaconda\Lib\site-packages\cmake\data\share\cmake-3.22\Modules\FindCUDA
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib
4. CUDNN
C:\Users\NSJ\Downloads\cudnn-10.0-windows10-x64-v7.6.5.32\cuda
C:\Users\NSJ\Downloads\cudnn-10.0-windows10-x64-v7.6.5.32\cuda\bin
C:\Users\NSJ\Downloads\cudnn-10.0-windows10-x64-v7.6.5.32\cuda\include
5. CMAKE
C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake
C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin
ref )
https://github.com/ageitgey/face_recognition --> import
https://google.github.io/mediapipe/solutions/hands#python-solution-api
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