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RHD.py
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executable file
·59 lines (48 loc) · 1.78 KB
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#!/usr/bin/python3
import os
import sys
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
# For webcam input:
#pathToFiles='/media/ammar/CVRLDatasets/vae-hands-3d/RHD/RHD_published_v2/evaluation/color'
pathToFiles='/home/ammar/Documents/Datasets/RHD/images'
files=os.listdir(pathToFiles)
files.sort()
#Check everything ok
#print(files)
#sys.exit(0)
with mp_hands.Hands( min_detection_confidence=0.5, min_tracking_confidence=0.5, max_num_hands=1) as hands:
#for filename in files:
for i in range(0,2727):
filename='im%u.png'%i
fullPathToImageFile="%s/%s"%(pathToFiles,filename)
image = cv2.imread(fullPathToImageFile)
# Flip the image horizontally for a later selfie-view display, and convert
# the BGR image to RGB.
image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
# To improve performance, optionally mark the image as not writeable to
# pass by reference.
image.flags.writeable = False
results = hands.process(image)
# Draw the hand annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
mp_drawing.draw_landmarks(image,hand_landmarks,mp_hands.HAND_CONNECTIONS)
cv2.imshow('MediaPipe Hands', image)
if (results.multi_handedness!=None):
handedness = [ handedness.classification[0].label for handedness in results.multi_handedness ]
print(filename,",",handedness[0])
else:
#print(filename,",?")
key = cv2.waitKey(0)
#print(key)
if key==49 :
print(filename,",Left")
if key==48 :
print(filename,",Right")
if cv2.waitKey(1) & 0xFF == 27:
break