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handpose_simon_backend.py
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handpose_simon_backend.py
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'''
Adapted from the MonoHand3D codebase for the MonocularRGB_3D_Handpose project (github release)
This script uses the 2D hand joint estimation network by Simon et al. (Available through the Openpose project)
@author: Paschalis Panteleris ([email protected])
'''
import sys
sys.path.append("lib")
import time
import os
import cv2
import numpy as np
import PyCeresIK as IK
from common import image
from common.opencv_grabbers import OpenCVGrabber
from common.calibrate import OpenCVCalib2CameraMeta, LoadOpenCVCalib
from common import factory
from common import pipeline
import PyMBVCore as Core
import PyJointTools as jt
import PyOpenPose as OP
from common import mva19
def mono_hand_loop(acq, outSize, config, track=False, paused=False, with_renderer=False):
print("Initialize WACV18 3D Pose estimator (IK)...")
pose_estimator = factory.HandPoseEstimator(config)
if with_renderer:
print("Initialize Hand Visualizer...")
hand_visualizer = pipeline.HandVisualizer(factory.mmanager, outSize)
print("Initialize openpose. Net output size",outSize,"...")
op = OP.OpenPose((160, 160), config["handnet_dims"], tuple(outSize), "COCO", config["OPENPOSE_ROOT"] + os.sep + "models" + os.sep, 0,
False, OP.OpenPose.ScaleMode.ZeroToOne, False, True)
left_hand_model = config["model_left"]
started = False
delay = {True: 0, False: 1}
ik_ms = est_ms = 0
p2d = bbox = None
count = 0
mbv_viz = opviz = None
smoothing = config.get("smoothing", 0)
boxsize = config["handnet_dims"][0]
stride = 8
peaks_thre = config["peaks_thre"]
print("Entering main Loop.")
while True:
try:
imgs, clbs = acq.grab()
if imgs is None or len(imgs)==0:
break
except Exception as e:
print("Failed to grab", e)
break
st = time.time()
bgr = imgs[0]
clb = clbs[0]
# compute kp using model initial pose
points2d = pose_estimator.ba.decodeAndProject(pose_estimator.model.init_pose, clb)
oldKp = np.array(points2d).reshape(-1, 2)
if bbox is None:
bbox = config["default_bbox"]
score = -1
result_pose = None
crop_viz = None
# STEP2 detect 2D joints for the detected hand.
if started and bbox is not None:
x,y,w,h = bbox
# print("BBOX: ",bbox)
crop = bgr[y:y+h,x:x+w]
img, pad = mva19.preprocess(crop, boxsize, stride)
t = time.time()
if left_hand_model:
op.detectHands(bgr, np.array([bbox+[0,0,0,0]],dtype=np.int32))
else:
op.detectHands(bgr, np.array([[0,0,0,0]+bbox],dtype=np.int32))
op_ms = (time.time() - t) * 1000.0
opviz = op.render(np.copy(bgr))
cv2.imshow("OPVIZ", opviz)
leftHands, rightHands = op.getKeypoints(op.KeypointType.HAND)
hands = leftHands if left_hand_model else rightHands
if hands is not None:
keypoints = hands[0][:,:3]
mask = keypoints[:, 2] < peaks_thre
keypoints[mask] = [0, 0, 1.0]
if track:
keypoints[mask, :2] = oldKp[mask]
keypoints[:, 2] = keypoints[:, 2]**3
rgbKp = IK.Observations(IK.ObservationType.COLOR, clb, keypoints)
obsVec = IK.ObservationsVector([rgbKp, ])
t = time.time()
score, res = pose_estimator.estimate(obsVec)
ik_ms = (time.time() - t)
# print(count,)
pose_estimator.print_report()
if track:
result_pose = list(smoothing * np.array(pose_estimator.model.init_pose) + (1.0 - smoothing) * np.array(res))
else:
result_pose = list(res)
# score is the residual, the lower the better, 0 is best
# -1 is failed optimization.
if track:
if -1 < score:# < 20000:
pose_estimator.model.init_pose = Core.ParamVector(result_pose)
else:
print("\n===>Reseting init position for IK<===\n")
pose_estimator.model.reset_pose()
if score > -1: # compute result points
p2d = np.array(pose_estimator.ba.decodeAndProject(Core.ParamVector(result_pose), clb)).reshape(-1, 2)
# scale = w/config.boxsize
bbox = mva19.update_bbox(p2d,bgr.shape[1::-1])
viz = np.copy(bgr)
viz2d = np.zeros_like(bgr)
if started and result_pose is not None:
viz2d = mva19.visualize_2dhand_skeleton(viz2d, keypoints, thre=peaks_thre)
cv2.imshow("2D CNN estimation",viz2d)
header = "FPS OPT+VIZ %03d, OPT %03fms (CNN %03fms, 3D %03fms)"%(1/(time.time()-st),(est_ms+ik_ms),est_ms, ik_ms)
if with_renderer:
hand_visualizer.render(pose_estimator.model, Core.ParamVector(result_pose), clb)
mbv_viz = hand_visualizer.getDepth()
cv2.imshow("MBV VIZ", mbv_viz)
mask = mbv_viz != [0, 0, 0]
viz[mask] = mbv_viz[mask]
else:
viz = mva19.visualize_3dhand_skeleton(viz, p2d)
pipeline.draw_rect(viz, "Hand", bbox, box_color=(0, 255, 0), text_color=(200, 200, 0))
else:
header = "Press 's' to start, 'r' to reset pose, 'p' to pause frame."
cv2.putText(viz, header, (20, 20), 0, 0.7, (50, 20, 20), 1, cv2.LINE_AA)
cv2.imshow("3D Hand Model reprojection", viz)
key = cv2.waitKey(delay[paused])
if key & 255 == ord('p'):
paused = not paused
if key & 255 == ord('q'):
break
if key & 255 == ord('r'):
print("\n===>Reseting init position for IK<===\n")
pose_estimator.model.reset_pose()
bbox = config['default_bbox']
print("RESETING BBOX",bbox)
if key & 255 == ord('s'):
started = not started
count += 1
if __name__ == '__main__':
config = {
# Right hand model config
"model": "models/hand_skinned.xml", "model_left": False,
# Left hand model config
# "model": "models/hand_left_skinned.xml", "model_left": True,
"model_init_pose": [-109.80840809323652, 95.70022984677065, 584.613931114289, 292.3322807284121, -1547.742897973965, -61.60146881490577, 435.33025195547793, 1.5707458637241434, 0.21444030289465843, 0.11033385117688158, 0.021952050059337137, 0.5716581133215294, 0.02969734913698679, 0.03414155945643072, 0.0, 1.1504613679382742, -0.5235922979328, 0.15626331136368257, 0.03656410417088128, 8.59579088582312e-07, 0.35789633949684985, 0.00012514308785717494, 0.005923001258945023, 0.24864102398139007, 0.2518954858979162, 0.0, 3.814694400000002e-13],
"model_map": IK.ModelAwareBundleAdjuster.HAND_SKINNED_TO_OP_RIGHT_HAND,
"OPENPOSE_ROOT": os.environ["OPENPOSE_ROOT"],
"handnet_dims": (304, 304),
"ba_iter": 200,
"padding": 0.3,
"minDim": 170,
"peaks_thre": 0.1,
"smoothing": 0.2,
# default bbox for the hand location
"default_bbox": [170,80,300,300],
}
# NOTE: You can replace the camera id with a video filename.
acq = OpenCVGrabber(0, calib_file="res/calib_webcam_mshd_vga.json")
acq.initialize()
mono_hand_loop(acq, (640,480), config, track=True, with_renderer=True)