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openvino-usbcamera-cpu-ncs2-sync.py
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import sys
import os
import numpy as np
import cv2
from os import system
import io
import time
from os.path import isfile
from os.path import join
import re
import argparse
import platform
try:
from armv7l.openvino.inference_engine import IENetwork, IEPlugin
except:
from openvino.inference_engine import IENetwork, IEPlugin
def getKeypoints(probMap, threshold=0.1):
mapSmooth = cv2.GaussianBlur(probMap, (3, 3), 0, 0)
mapMask = np.uint8(mapSmooth>threshold)
keypoints = []
contours = None
try:
#OpenCV4.x
contours, _ = cv2.findContours(mapMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
except:
#OpenCV3.x
_, contours, _ = cv2.findContours(mapMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
blobMask = np.zeros(mapMask.shape)
blobMask = cv2.fillConvexPoly(blobMask, cnt, 1)
maskedProbMap = mapSmooth * blobMask
_, maxVal, _, maxLoc = cv2.minMaxLoc(maskedProbMap)
keypoints.append(maxLoc + (probMap[maxLoc[1], maxLoc[0]],))
return keypoints
def getValidPairs(outputs, w, h):
valid_pairs = []
invalid_pairs = []
n_interp_samples = 10
paf_score_th = 0.1
conf_th = 0.7
for k in range(len(mapIdx)):
pafA = outputs[0, mapIdx[k][0], :, :]
pafB = outputs[0, mapIdx[k][1], :, :]
pafA = cv2.resize(pafA, (w, h))
pafB = cv2.resize(pafB, (w, h))
candA = detected_keypoints[POSE_PAIRS[k][0]]
candB = detected_keypoints[POSE_PAIRS[k][1]]
nA = len(candA)
nB = len(candB)
if( nA != 0 and nB != 0):
valid_pair = np.zeros((0,3))
for i in range(nA):
max_j=-1
maxScore = -1
found = 0
for j in range(nB):
d_ij = np.subtract(candB[j][:2], candA[i][:2])
norm = np.linalg.norm(d_ij)
if norm:
d_ij = d_ij / norm
else:
continue
interp_coord = list(zip(np.linspace(candA[i][0], candB[j][0], num=n_interp_samples),
np.linspace(candA[i][1], candB[j][1], num=n_interp_samples)))
paf_interp = []
for k in range(len(interp_coord)):
paf_interp.append([pafA[int(round(interp_coord[k][1])), int(round(interp_coord[k][0]))],
pafB[int(round(interp_coord[k][1])), int(round(interp_coord[k][0]))] ])
paf_scores = np.dot(paf_interp, d_ij)
avg_paf_score = sum(paf_scores)/len(paf_scores)
if ( len(np.where(paf_scores > paf_score_th)[0]) / n_interp_samples ) > conf_th :
if avg_paf_score > maxScore:
max_j = j
maxScore = avg_paf_score
found = 1
if found:
valid_pair = np.append(valid_pair, [[candA[i][3], candB[max_j][3], maxScore]], axis=0)
valid_pairs.append(valid_pair)
else:
invalid_pairs.append(k)
valid_pairs.append([])
return valid_pairs, invalid_pairs
def getPersonwiseKeypoints(valid_pairs, invalid_pairs):
personwiseKeypoints = -1 * np.ones((0, 19))
for k in range(len(mapIdx)):
if k not in invalid_pairs:
partAs = valid_pairs[k][:,0]
partBs = valid_pairs[k][:,1]
indexA, indexB = np.array(POSE_PAIRS[k])
for i in range(len(valid_pairs[k])):
found = 0
person_idx = -1
for j in range(len(personwiseKeypoints)):
if personwiseKeypoints[j][indexA] == partAs[i]:
person_idx = j
found = 1
break
if found:
personwiseKeypoints[person_idx][indexB] = partBs[i]
personwiseKeypoints[person_idx][-1] += keypoints_list[partBs[i].astype(int), 2] + valid_pairs[k][i][2]
elif not found and k < 17:
row = -1 * np.ones(19)
row[indexA] = partAs[i]
row[indexB] = partBs[i]
row[-1] = sum(keypoints_list[valid_pairs[k][i,:2].astype(int), 2]) + valid_pairs[k][i][2]
personwiseKeypoints = np.vstack([personwiseKeypoints, row])
return personwiseKeypoints
fps = ""
detectfps = ""
framecount = 0
time1 = 0
camera_width = 320
camera_height = 240
# camera_width = 40 #80 #160 #320
# camera_height = 30 #60 #120 #240
keypointsMapping = ['Nose', 'Neck', 'R-Sho', 'R-Elb', 'R-Wr', 'L-Sho', 'L-Elb', 'L-Wr', 'R-Hip', 'R-Knee', 'R-Ank', 'L-Hip', 'L-Knee', 'L-Ank', 'R-Eye', 'L-Eye', 'R-Ear', 'L-Ear']
POSE_PAIRS = [[1,2], [1,5], [2,3], [3,4], [5,6], [6,7], [1,8], [8,9], [9,10], [1,11], [11,12], [12,13], [1,0], [0,14], [14,16], [0,15], [15,17], [2,17], [5,16]]
mapIdx = [[31,32], [39,40], [33,34], [35,36], [41,42], [43,44], [19,20], [21,22], [23,24], [25,26], [27,28], [29,30], [47,48], [49,50], [53,54], [51,52], [55,56], [37,38], [45,46]]
colors = [[0,100,255], [0,100,255], [0,255,255], [0,100,255], [0,255,255], [0,100,255], [0,255,0], [255,200,100], [255,0,255], [0,255,0], [255,200,100], [255,0,255], [0,0,255], [255,0,0], [200,200,0], [255,0,0], [200,200,0], [0,0,0]]
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FPS, 30)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--device", help="Specify the target device to infer on; CPU, GPU, MYRIAD is acceptable. (Default=CPU)", default="CPU", type=str)
parser.add_argument("-b", "--boost", help="Setting it to True will make it run faster instead of sacrificing accuracy. (Default=False)", default=False, type=bool)
args = parser.parse_args()
plugin = IEPlugin(device=args.device)
if "CPU" == args.device:
if platform.processor() == "x86_64":
plugin.add_cpu_extension("lib/libcpu_extension.so")
if args.boost == False:
model_xml = "models/train/test/openvino/mobilenet_v2_1.4_224/FP32/frozen-model.xml"
else:
model_xml = "models/train/test/openvino/mobilenet_v2_0.5_224/FP32/frozen-model.xml"
elif "GPU" == args.device or "MYRIAD" == args.device:
if args.boost == False:
model_xml = "models/train/test/openvino/mobilenet_v2_1.4_224/FP16/frozen-model.xml"
else:
model_xml = "models/train/test/openvino/mobilenet_v2_0.5_224/FP16/frozen-model.xml"
else:
print("Specify the target device to infer on; CPU, GPU, MYRIAD is acceptable.")
sys.exit(0)
model_bin = os.path.splitext(model_xml)[0] + ".bin"
net = IENetwork(model=model_xml, weights=model_bin)
input_blob = next(iter(net.inputs))
exec_net = plugin.load(network=net)
inputs = net.inputs["image"]
h = inputs.shape[2] #368
w = inputs.shape[3] #432
threshold = 0.1
nPoints = 18
try:
while True:
t1 = time.perf_counter()
ret, color_image = cap.read()
if not ret:
break
colw = color_image.shape[1]
colh = color_image.shape[0]
new_w = int(colw * min(w/colw, h/colh))
new_h = int(colh * min(w/colw, h/colh))
# new_w = 184
#new_h = 216
resized_image = cv2.resize(color_image, (new_w, new_h), interpolation = cv2.INTER_CUBIC)
canvas = np.full((h, w, 3), 128)
canvas[(h - new_h)//2:(h - new_h)//2 + new_h,(w - new_w)//2:(w - new_w)//2 + new_w, :] = resized_image
prepimg = canvas
prepimg = prepimg[np.newaxis, :, :, :] # Batch size axis add
prepimg = prepimg.transpose((0, 3, 1, 2)) # NHWC to NCHW, (1, 3, 368, 432)
outputs = exec_net.infer(inputs={input_blob: prepimg})["Openpose/concat_stage7"]
detected_keypoints = []
keypoints_list = np.zeros((0, 3))
keypoint_id = 0
for part in range(nPoints):
probMap = outputs[0, part, :, :]
probMap = cv2.resize(probMap, (canvas.shape[1], canvas.shape[0])) # (432, 368)
keypoints = getKeypoints(probMap, threshold)
keypoints_with_id = []
for i in range(len(keypoints)):
keypoints_with_id.append(keypoints[i] + (keypoint_id,))
keypoints_list = np.vstack([keypoints_list, keypoints[i]])
keypoint_id += 1
detected_keypoints.append(keypoints_with_id)
print(detected_keypoints)
frameClone = np.uint8(canvas.copy())
for i in range(nPoints):
for j in range(len(detected_keypoints[i])):
cv2.circle(frameClone, detected_keypoints[i][j][0:2], 5, colors[i], -1, cv2.LINE_AA)
valid_pairs, invalid_pairs = getValidPairs(outputs, w, h)
personwiseKeypoints = getPersonwiseKeypoints(valid_pairs, invalid_pairs)
for i in range(17):
for n in range(len(personwiseKeypoints)):
index = personwiseKeypoints[n][np.array(POSE_PAIRS[i])]
if -1 in index:
continue
B = np.int32(keypoints_list[index.astype(int), 0])
A = np.int32(keypoints_list[index.astype(int), 1])
cv2.line(frameClone, (B[0], A[0]), (B[1], A[1]), colors[i], 3, cv2.LINE_AA)
cv2.putText(frameClone, fps, (w-170,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38,0,255), 1, cv2.LINE_AA)
cv2.namedWindow("USB Camera", cv2.WINDOW_AUTOSIZE)
cv2.imshow("USB Camera" , frameClone)
if cv2.waitKey(1)&0xFF == ord('q'):
break
# FPS calculation
framecount += 1
if framecount >= 15:
fps = "(Playback) {:.1f} FPS".format(time1/15)
framecount = 0
time1 = 0
t2 = time.perf_counter()
elapsedTime = t2-t1
time1 += 1/elapsedTime
except:
import traceback
traceback.print_exc()
finally:
print("\n\nFinished\n\n")