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iewrap_heatmap.py
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import iewrap
import numpy as np
import cv2
#----------------------------------------------------------------------------------------
# Heatmap class
class heatmap:
colorTable = ( ( 0, ( 0, 0, 0)),
( 1, (128, 0, 0)),
( 32, (255, 0, 0)),
( 64, (255, 0,255)),
( 96, ( 0,255, 0)),
(128, ( 0,255, 0)),
(160, ( 0,255,255)),
(192, ( 0,255,255)),
(224, ( 0, 0,255)),
(256, ( 0, 0,255)) )
def __init__(self, nx, ny, nn):
self.num_x = nx
self.num_y = ny
self.num_n = nn
self.current = 0 # current level
self.heatmap = np.zeros((nx, ny, nn), dtype=np.uint8) # heatmap (has multiple levels)
self.frame = np.zeros((ny, nx, 3), dtype=np.uint8) # frame image genarated from the heatmap
self.colorLUT= [ self.colorInterpolate(v) for v in range(256) ]
def colorInterpolate(self, col):
if col< 0: col= 0
if col>255: col=255
prevCtbl=(0,(0,0,0))
for i, ctbl in enumerate(self.colorTable):
if col<ctbl[0]:
v1, col1 = prevCtbl
v2, col2 = ctbl
p = 0 if col==v1 else (col-v1)/(v2-v1)
b = int((col2[0]-col1[0])*p+col1[0])
r = int((col2[1]-col1[1])*p+col1[1])
g = int((col2[2]-col1[2])*p+col1[2])
prevCtbl = ctbl
return (b,r,g)
return (0,0,0)
def clearHeatmapLevel(self, level):
if level<0 or level>=self.num_n:
return
for x in range(self.num_x):
for y in range(self.num_y):
self.heatmap[x, y, level] = 0
def incrementTime(self):
self.current = (self.current+1) % self.num_n
self.clearHeatmapLevel(self.current) # clear the new level
def generateFrame(self):
valMap = np.sum(self.heatmap, axis=2)
for y in range(self.num_y):
for x in range(self.num_x):
val = valMap[x, y]
val = 0 if val< 0 else 255 if val>255 else val
col = self.colorLUT[val]
self.frame[y, x, 0] = col[0]
self.frame[y, x, 1] = col[1]
self.frame[y, x, 2] = col[2]
def addValue(self, x, y, val):
self.heatmap[x, y, self.current] += val
if self.heatmap[x, y, self.current] > 255:
self.heatmap[x, y, self.current] = 255
#----------------------------------------------------------------------------------------
# Person detection & re-identification
model_det = 'intel/pedestrian-detection-adas-0002/FP16/pedestrian-detection-adas-0002.xml'
# Face detection & re-identification
#model_det = 'intel/face-detection-adas-0001/FP16/face-detection-adas-0001.xml'
def main():
ie_detect = iewrap.ieWrapper(model_det, 'CPU')
# Open USB webcams
cam = cv2.VideoCapture(0)
#cam = cv2.VideoCapture(input_movie)
cam.set(cv2.CAP_PROP_FRAME_WIDTH , 640)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
hm = heatmap(10, 10, 30)
n = 0
while(cv2.waitKey(1)!=27):
ret, img = cam.read()
if ret==False:
break
img_out = img.copy()
det = ie_detect.blockInfer(img).reshape((200,7)) # Detect objects
img_out = img_out>>1
for obj in det: # obj = [ image_id, label, conf, xmin, ymin, xmax, ymax ]
if obj[2] > 0.75: # Confidence > 75%
xmin = abs(int(obj[3] * img_out.shape[1]))
ymin = abs(int(obj[4] * img_out.shape[0]))
xmax = abs(int(obj[5] * img_out.shape[1]))
ymax = abs(int(obj[6] * img_out.shape[0]))
cv2.rectangle(img_out, (xmin, ymin), (xmax, ymax), ( 0,255,255), 2)
x = int((obj[3]+obj[5])/2*hm.num_x)
y = int((obj[4]+obj[6])/2*hm.num_y)
hm.addValue(x, y, 4)
hm.generateFrame()
n = (n+1) % 30
if n==0:
hm.incrementTime()
frame = cv2.resize(hm.frame, dsize=(img_out.shape[1], img_out.shape[0]),
interpolation = cv2.INTER_CUBIC) # INTER_AREA, INTER_LINEAR, INTER_CUBIC
frame = frame | img_out
cv2.imshow('output', frame)
if __name__ == '__main__':
main()