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webcam3d1.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Feb 20 11:47:50 2018
@author: KEEL
"""
import argparse
import cv2
import logging
import time
import ast
import common
import numpy as np
from estimator import TfPoseEstimator
from networks import get_graph_path, model_wh
import copy
from lifting.prob_model import Prob3dPose
import socket
import time
TCP_IP = '192.168.1.212'
TCP_PORT = 5007
logger = logging.getLogger('TfPoseEstimator')
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s')
ch.setFormatter(formatter)
logger.addHandler(ch)
# def Estimate_3Ddata(image,e,scales):
# humans = e.inference(image, scales=scales)
# if (len(humans) == 0):
# human_appear = False
# return 0, image
# image = TfPoseEstimator.draw_humans(image, humans)
# logger.info('3d lifting initialization.')
# poseLifting = Prob3dPose('lifting/models/prob_model_params.mat')
# pose_2d_mpiis = []
# visibilities = []
# for human in humans:
# pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
# pose_2d_mpiis.append([(int(x * 432 + 0.5), int(y * 368 + 0.5)) for x, y in pose_2d_mpii])
# visibilities.append(visibility)
# pose_2d_mpiis = np.array(pose_2d_mpiis)
# visibilities = np.array(visibilities)
# if(pose_2d_mpiis.ndim != 3):
# return 0
# transformed_pose2d, weights = poseLifting.transform_joints(pose_2d_mpiis, visibilities)
# pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
# return pose_3d, image
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='tf-pose-estimation run')
parser.add_argument('--movie', type=str, default='../demo2.mp4')
parser.add_argument('--dataname',type=str,default='')
parser.add_argument('--datas', type=str, default='data/')
args = parser.parse_args()
#movie = cv2.VideoCapture(args.movie)
#movie = cv2.VideoCapture("rtsp://admin:[email protected]:30012")
movie = cv2.VideoCapture(0, cv2.CAP_DSHOW)
e = TfPoseEstimator(get_graph_path('mobilenet_thin'), target_size=(432,368))
ast_l = ast.literal_eval('[None]')
array = []
previous_frame = 0
previous_frame2 = 0
# _, frame = movie.read()
# data, frame = Estimate_3Ddata(frame,e,ast_l)
# # if(data == 0):
# # previous_frame[0] = 0
# previous_frame[0] = data
# _, frame = movie.read()
# data, frame = Estimate_3Ddata(frame,e,ast_l)
# # if(data == 0):
# # previous_frame[1] = 0
# previous_frame[1] = data
#frame_count = int(movie.get(7))
#for i in range(frame_count):
while(True):
_, frame = movie.read()
#***************************************************************
#data, image = Estimate_3Ddata(frame,e,ast_l)
humans = e.inference(frame, scales=ast_l)
if(len(humans)==0):
array = []
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((TCP_IP, TCP_PORT))
image = cv2.resize(frame, (432,368))
cv2.imshow('tf-pose-estimation result', image)
array = [-7.6688656386986205, 13.011893068270316, -76.44649495830629, -96.04607485096246, -92.81828865032355, -66.12264213078763, -71.17861721812547, -141.7884342909674, -513.2769495552633, -93.24026034432053, -49.908593729408864, -859.9538382379666, 93.9853006386473, 118.84215202672682, -65.02325740984881, 109.97191521670692, 49.68661022821461, -500.2815729013937, 43.949669089576304, 113.42515121422467, -826.8247952726662, -30.178936716325808, 32.71730632233089, 199.99237952146646, 1.3300306085416633, 4.264303940397121, 518.4445448043695, 68.23552287179022, -59.627185316857094, 579.1492439958412, 16.227613839804388, -26.9316905346605, 762.9746341871173, 120.34132128722644, 125.661445590322, 493.24154801020865, 142.98984394558525, 237.95051301631239, 192.7825133883671, 194.12002009602188, 169.61851447575899, -20.96114030070511, -98.459444321648, -96.3385622492707, 492.55167674182735, -193.9636006266926, -160.35763166809497, 189.06132786524188, -170.02771737499376, -227.0821354762086, -72.1787662704936, -7.6688656386986205, 13.011893068270316, -76.44649495830629, -96.04607485096246, -92.81828865032355, -66.12264213078763, -71.17861721812547, -141.7884342909674, -513.2769495552633, -93.24026034432053, -49.908593729408864, -859.9538382379666, 93.9853006386473, 118.84215202672682, -65.02325740984881, 109.97191521670692, 49.68661022821461, -500.2815729013937, 43.949669089576304, 113.42515121422467, -826.8247952726662, -30.178936716325808, 32.71730632233089, 199.99237952146646, 1.3300306085416633, 4.264303940397121, 518.4445448043695, 68.23552287179022, -59.627185316857094, 579.1492439958412, 16.227613839804388, -26.9316905346605, 762.9746341871173, 120.34132128722644, 125.661445590322, 493.24154801020865, 142.98984394558525, 237.95051301631239, 192.7825133883671, 194.12002009602188, 169.61851447575899, -20.96114030070511, -98.459444321648, -96.3385622492707, 492.55167674182735, -193.9636006266926, -160.35763166809497, 189.06132786524188, -170.02771737499376, -227.0821354762086, -72.1787662704936]
array = " ".join(str(x) for x in array)
s.sendall(bytes(array,encoding = 'utf-8'))
s.close()
#print(array)
print("No human appear...")
if cv2.waitKey(1) == 27:
break
continue
elif len(humans) > 0:
array = []
image = cv2.resize(frame, (432,368))
cv2.imshow('tf-pose-estimation result', image)
if cv2.waitKey(1) == 27:
break
pop_array = []
for j in range(len(humans)):
#for i in range(17):
#print(humans[j].body_parts[i].x)
if len(humans[j].body_parts)<13:
# array = []
# s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# s.connect((TCP_IP, TCP_PORT))
pop_array.append(j)
# print("no complete person appears")
# array = [-7.6688656386986205, 13.011893068270316, -76.44649495830629, -96.04607485096246, -92.81828865032355, -66.12264213078763, -71.17861721812547, -141.7884342909674, -513.2769495552633, -93.24026034432053, -49.908593729408864, -859.9538382379666, 93.9853006386473, 118.84215202672682, -65.02325740984881, 109.97191521670692, 49.68661022821461, -500.2815729013937, 43.949669089576304, 113.42515121422467, -826.8247952726662, -30.178936716325808, 32.71730632233089, 199.99237952146646, 1.3300306085416633, 4.264303940397121, 518.4445448043695, 68.23552287179022, -59.627185316857094, 579.1492439958412, 16.227613839804388, -26.9316905346605, 762.9746341871173, 120.34132128722644, 125.661445590322, 493.24154801020865, 142.98984394558525, 237.95051301631239, 192.7825133883671, 194.12002009602188, 169.61851447575899, -20.96114030070511, -98.459444321648, -96.3385622492707, 492.55167674182735, -193.9636006266926, -160.35763166809497, 189.06132786524188, -170.02771737499376, -227.0821354762086, -72.1787662704936, -7.6688656386986205, 13.011893068270316, -76.44649495830629, -96.04607485096246, -92.81828865032355, -66.12264213078763, -71.17861721812547, -141.7884342909674, -513.2769495552633, -93.24026034432053, -49.908593729408864, -859.9538382379666, 93.9853006386473, 118.84215202672682, -65.02325740984881, 109.97191521670692, 49.68661022821461, -500.2815729013937, 43.949669089576304, 113.42515121422467, -826.8247952726662, -30.178936716325808, 32.71730632233089, 199.99237952146646, 1.3300306085416633, 4.264303940397121, 518.4445448043695, 68.23552287179022, -59.627185316857094, 579.1492439958412, 16.227613839804388, -26.9316905346605, 762.9746341871173, 120.34132128722644, 125.661445590322, 493.24154801020865, 142.98984394558525, 237.95051301631239, 192.7825133883671, 194.12002009602188, 169.61851447575899, -20.96114030070511, -98.459444321648, -96.3385622492707, 492.55167674182735, -193.9636006266926, -160.35763166809497, 189.06132786524188, -170.02771737499376, -227.0821354762086, -72.1787662704936]
# array = " ".join(str(x) for x in array)
# s.sendall(bytes(array,encoding = 'utf-8'))
# s.close()
# print(array)
#humans.pop(pop_array)
humans = [e for e in humans if humans.index(e) not in pop_array]
if len(humans) == 0:
array = []
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((TCP_IP, TCP_PORT))
print("no complete person appears")
array = [-7.6688656386986205, 13.011893068270316, -76.44649495830629, -96.04607485096246, -92.81828865032355, -66.12264213078763, -71.17861721812547, -141.7884342909674, -513.2769495552633, -93.24026034432053, -49.908593729408864, -859.9538382379666, 93.9853006386473, 118.84215202672682, -65.02325740984881, 109.97191521670692, 49.68661022821461, -500.2815729013937, 43.949669089576304, 113.42515121422467, -826.8247952726662, -30.178936716325808, 32.71730632233089, 199.99237952146646, 1.3300306085416633, 4.264303940397121, 518.4445448043695, 68.23552287179022, -59.627185316857094, 579.1492439958412, 16.227613839804388, -26.9316905346605, 762.9746341871173, 120.34132128722644, 125.661445590322, 493.24154801020865, 142.98984394558525, 237.95051301631239, 192.7825133883671, 194.12002009602188, 169.61851447575899, -20.96114030070511, -98.459444321648, -96.3385622492707, 492.55167674182735, -193.9636006266926, -160.35763166809497, 189.06132786524188, -170.02771737499376, -227.0821354762086, -72.1787662704936, -7.6688656386986205, 13.011893068270316, -76.44649495830629, -96.04607485096246, -92.81828865032355, -66.12264213078763, -71.17861721812547, -141.7884342909674, -513.2769495552633, -93.24026034432053, -49.908593729408864, -859.9538382379666, 93.9853006386473, 118.84215202672682, -65.02325740984881, 109.97191521670692, 49.68661022821461, -500.2815729013937, 43.949669089576304, 113.42515121422467, -826.8247952726662, -30.178936716325808, 32.71730632233089, 199.99237952146646, 1.3300306085416633, 4.264303940397121, 518.4445448043695, 68.23552287179022, -59.627185316857094, 579.1492439958412, 16.227613839804388, -26.9316905346605, 762.9746341871173, 120.34132128722644, 125.661445590322, 493.24154801020865, 142.98984394558525, 237.95051301631239, 192.7825133883671, 194.12002009602188, 169.61851447575899, -20.96114030070511, -98.459444321648, -96.3385622492707, 492.55167674182735, -193.9636006266926, -160.35763166809497, 189.06132786524188, -170.02771737499376, -227.0821354762086, -72.1787662704936]
array = " ".join(str(x) for x in array)
s.sendall(bytes(array,encoding = 'utf-8'))
s.close()
#print(array)
if len(humans) >= 1:
# s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# s.connect((TCP_IP, TCP_PORT))
human_min = min(len(humans), 2)
humans = humans[:human_min]
print("complete human appears")
frame = TfPoseEstimator.draw_humans(frame, humans)
#logger.info('3d lifting initialization.')
poseLifting = Prob3dPose('lifting/models/prob_model_params.mat')
#***************************************************************
if len(humans) == 1:
pose_2d_mpiis = []
visibilities = []
human = humans[0]
if (not 9 in human.body_parts) and (not 12 in human.body_parts) and (8 in human.body_parts) and (11 in human.body_parts):
human.body_parts[9] = copy.copy(human.body_parts[8])
human.body_parts[12] = copy.copy(human.body_parts[11])
human.body_parts[9].y = human.body_parts[8].y + 0.8*(human.body_parts[8].y-human.body_parts[1].y)
human.body_parts[12].y = human.body_parts[11].y + 0.8*(human.body_parts[11].y-human.body_parts[1].y)
human.body_parts[10] = copy.copy(human.body_parts[8])
human.body_parts[13] = copy.copy(human.body_parts[11])
human.body_parts[10].y = human.body_parts[8].y + 1.6*(human.body_parts[8].y-human.body_parts[1].y)
human.body_parts[13].y = human.body_parts[11].y + 1.6*(human.body_parts[11].y-human.body_parts[1].y)
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * 432 + 0.5), int(y * 368 + 0.5)) for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
# if(pose_2d_mpiis.ndim != 3):
# return 0
transformed_pose2d, weights = poseLifting.transform_joints(pose_2d_mpiis, visibilities)
data = poseLifting.compute_3d(transformed_pose2d, weights)
print(data)
array = []
data_mean = (np.array(np.array(data[0]) + previous_frame))/2
previous_frame = data[0]
x = data_mean[0]
y = data_mean[1]
z = data_mean[2]
for j in range(17):
array.extend([x[j], y[j], z[j]])
array.extend([-7.6688656386986205, 13.011893068270316, -76.44649495830629, -96.04607485096246, -92.81828865032355, -66.12264213078763, -71.17861721812547, -141.7884342909674, -513.2769495552633, -93.24026034432053, -49.908593729408864, -859.9538382379666, 93.9853006386473, 118.84215202672682, -65.02325740984881, 109.97191521670692, 49.68661022821461, -500.2815729013937, 43.949669089576304, 113.42515121422467, -826.8247952726662, -30.178936716325808, 32.71730632233089, 199.99237952146646, 1.3300306085416633, 4.264303940397121, 518.4445448043695, 68.23552287179022, -59.627185316857094, 579.1492439958412, 16.227613839804388, -26.9316905346605, 762.9746341871173, 120.34132128722644, 125.661445590322, 493.24154801020865, 142.98984394558525, 237.95051301631239, 192.7825133883671, 194.12002009602188, 169.61851447575899, -20.96114030070511, -98.459444321648, -96.3385622492707, 492.55167674182735, -193.9636006266926, -160.35763166809497, 189.06132786524188, -170.02771737499376, -227.0821354762086, -72.1787662704936])
array = " ".join(str(x) for x in array)
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((TCP_IP, TCP_PORT))
image = cv2.resize(frame, (432,368))
cv2.imshow('tf-pose-estimation result', image)
if cv2.waitKey(1) == 27:
break
#print(array)
s.sendall(bytes(array,encoding = 'utf-8'))
s.close()
if len(humans) == 2:
array = []
data = []
pose_2d_mpiis = []
visibilities = []
# for k in range(len(humans)):
human1_hip = humans[0].body_parts[1].x
human2_hip = humans[1].body_parts[1].x
if(human1_hip > human2_hip):
humans[0], humans[1] = humans[1], humans[0]
for human in humans:
if (not 9 in human.body_parts) and (not 12 in human.body_parts) and (8 in human.body_parts) and (11 in human.body_parts):
human.body_parts[9] = copy.copy(human.body_parts[8])
human.body_parts[12] = copy.copy(human.body_parts[11])
human.body_parts[9].y = human.body_parts[8].y + 0.8*(human.body_parts[8].y-human.body_parts[1].y)
human.body_parts[12].y = human.body_parts[11].y + 0.8*(human.body_parts[11].y-human.body_parts[1].y)
human.body_parts[10] = copy.copy(human.body_parts[8])
human.body_parts[13] = copy.copy(human.body_parts[11])
human.body_parts[10].y = human.body_parts[8].y + 1.6*(human.body_parts[8].y-human.body_parts[1].y)
human.body_parts[13].y = human.body_parts[11].y + 1.6*(human.body_parts[11].y-human.body_parts[1].y)
pose_2d_mpii, visibility = common.MPIIPart.from_coco(humans[0])
pose_2d_mpiis.append([(int(x * 432 + 0.5), int(y * 368 + 0.5)) for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(pose_2d_mpiis, visibilities)
data.append(poseLifting.compute_3d(transformed_pose2d, weights))
pose_2d_mpiis = []
visibilities = []
pose_2d_mpii, visibility = common.MPIIPart.from_coco(humans[1])
pose_2d_mpiis.append([(int(x * 432 + 0.5), int(y * 368 + 0.5)) for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(pose_2d_mpiis, visibilities)
data.append(poseLifting.compute_3d(transformed_pose2d, weights))
#print(data)
# data_mean2 = (np.array(np.array(data[0]) + previous_frame2[0] + previous_frame2[1]))/3
# previous_frame2[0] = previous_frame2[1]
# previous_frame2[1] = np.array(data[0])
# x = data_mean2[0]
# y = data_mean2[1]
# z = data_mean2[2]
x = data[0][0][0]
y = data[0][0][1]
z = data[0][0][2]
for j in range(17):
array.extend([x[j], y[j], z[j]])
# data_mean3 = (np.array(np.array(data[1]) + previous_frame3[0] + previous_frame3[1]))/3
# previous_frame3[0] = previous_frame3[1]
# previous_frame3[1] = np.array(data[1])
# x2 = data_mean3[0]
# y2 = data_mean3[1]
# z2 = data_mean3[2]
x2 = data[1][0][0]
y2 = data[1][0][1]
z2 = data[1][0][2]
for j in range(17):
array.extend([x2[j], y2[j], z2[j]])
array = " ".join(str(x) for x in array)
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((TCP_IP, TCP_PORT))
image = cv2.resize(frame, (432,368))
cv2.imshow('tf-pose-estimation result', image)
if cv2.waitKey(1) == 27:
break
print(array)
s.sendall(bytes(array,encoding = 'utf-8'))
s.close()
#array = []
# pose_2d_mpiis = np.array(pose_2d_mpiis)
# visibilities = np.array(visibilities)
# # if(pose_2d_mpiis.ndim != 3):
# # return 0
# transformed_pose2d, weights = poseLifting.transform_joints(pose_2d_mpiis, visibilities)
# data = poseLifting.compute_3d(transformed_pose2d, weights)
# #***************************************************************
# data_mean = (np.array(data + previous_frame[0] + previous_frame[1]))/3
# previous_frame[0] = previous_frame[1]
# previous_frame[1] = data
# x = data_mean[0][0]
# y = data_mean[0][1]
# z = data_mean[0][2]
# for j in range(17):
# array.extend([x[j], y[j], z[j]])
# array = " ".join(str(x) for x in array)
# image = cv2.resize(frame, (432,368))
# cv2.imshow('tf-pose-estimation result', image)
# if cv2.waitKey(1) == 27:
# break
# s.sendall(bytes(array,encoding = 'utf-8'))
# s.close()