Hello, i tried a code to detect hand and fingers in realtime with webcam; it work fine in Python but with Hops Grasshopper receive only the first data.
Is that possible with Hops?
from flask import Flask
import ghhops_server as hs
import rhino3dm
import cv2
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
# register hops app as middleware
app = Flask(__name__)
hops = hs.Hops(app)
@hops.component(
"/hand",
name="hand_motion",
description="detect hand motion",
#icon="",
inputs=[
hs.HopsString("Image", "Image", "image",hs.HopsParamAccess.LIST),
hs.HopsNumber("Zscale","Zscale")
],
outputs=[
hs.HopsPoint("Points", "Points"),
hs.HopsNumber("width","Width"),
hs.HopsNumber("height","Height"),
hs.HopsString("Index","Index"),
#hs.HopsString("Score","s"),
hs.HopsString("Label","Label")
]
)
def hand(img,n):
file_list = img
with mp_hands.Hands(
static_image_mode=True,
max_num_hands=2,
min_detection_confidence=0.5) as hands:
for idx, file in enumerate(file_list):
# 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.
results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
ind = []
lab = []
for classifications in results.multi_handedness:
index = [classification.index for classification in classifications.classification]
#score = [classification.score for classification in classifications.classification]
label = [classification.label for classification in classifications.classification]
ind.append(index)
#scr.append(score)
lab.append(label)
if not results.multi_hand_landmarks:
continue
image_height, image_width, _ = image.shape
w = image_width
h = image_height
point = []
for hand_landmarks in results.multi_hand_landmarks:
pt = [rhino3dm.Point3d(-landmark.x*w, -landmark.y*h, -landmark.z*n) for landmark in hand_landmarks.landmark]
point.append(pt)
if len(point) == 2:
points = point[0]+point[1]
indexes = ind[0]+ind[1]
labels = lab[0]+lab[1]
elif len(point) == 1:
points = point[0]
indexes = ind[0]
labels = lab[0]
return points, w, h ,indexes,labels
@hops.component(
"/hands",
name="hands_motion",
description="hand motion video",
#icon="",
inputs=[
hs.HopsNumber("Zscale","Zscale")
],
outputs=[
hs.HopsPoint("Points", "Points"),
hs.HopsNumber("width","Width"),
hs.HopsNumber("height","Height"),
]
)
def hands(n):
# For webcam input:
cap = cv2.VideoCapture(0)
with mp_hands.Hands(
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as hands:
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
# 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)
image_height, image_width, _ = image.shape
w = image_width
h = image_height
point = []
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
pt = [rhino3dm.Point3d(-landmark.x * w, -landmark.y * h, -landmark.z * n) for landmark in
hand_landmarks.landmark]
point.append(pt)
if len(point) == 2:
points = point[0] + point[1]
elif len(point) == 1:
points = point[0]
return points, w, h
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()
if __name__ == "__main__":
app.run()
