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import cv2
import math
import numpy as np
kBACKGROUND_NUM = 10
kSIZE = 128
kEXPONENTAL_VALUE = 0.7
gSENSOR_FOV = 60.0 / 180.0 * math.pi
def exponential(img, value):
tmp = cv2.pow(img.astype(np.double), value)*(255.0/(255.0**value))
return tmp.astype(np.uint8)
def mergeFrames(imgs, SIZE, overlap):
tmp = np.zeros((SIZE, SIZE*2-overlap), dtype=np.uint16)
tmp[:, :SIZE] = imgs[0]
tmp[:, -SIZE:] += imgs[1]
tmp[:, (SIZE-overlap): SIZE] = tmp[:, (SIZE-overlap): SIZE]/2
tmp2 = np.zeros((SIZE, SIZE*2-overlap), dtype=np.uint16)
tmp2[:, :SIZE] = imgs[2]
tmp2[:, -SIZE:] += imgs[3]
tmp2[:, (SIZE-overlap): SIZE] = tmp2[:, (SIZE-overlap): SIZE]/2
merge = np.zeros((SIZE*2-overlap, SIZE*2-overlap), dtype=np.uint16)
merge[:SIZE, :] = tmp
merge[-SIZE:, :] += tmp2
merge[(SIZE-overlap):SIZE, :] = merge[(SIZE-overlap):SIZE, :]/2
#merge = exponential(merge, kEXPONENTAL_VALUE)
return merge.astype(np.uint8)
class DataFuser(object):
def __init__(self, sensor_dist):
self.background_cnt = 0
self.background_frame = np.zeros((4, 64))
self.sensor_dist = sensor_dist
def mergeFrame(self, frame, dist = None):
if self.background_cnt < kBACKGROUND_NUM:
self.background_frame += frame / kBACKGROUND_NUM
self.background_cnt += 1
return False
frame = exponential(cv2.subtract(exponential(frame, kEXPONENTAL_VALUE),
exponential(self.background_frame, kEXPONENTAL_VALUE)),
0.3)
print (([max(x) for x in frame]))
imgs = [np.reshape(img, (8, 8)) for img in frame]
imgs = [cv2.resize(img.astype(np.uint8), (kSIZE, kSIZE),
interpolation = cv2.INTER_LINEAR) for img in imgs]
try:
overlap = int(kSIZE -
kSIZE * (self.sensor_dist / (2 * dist * math.tan(gSENSOR_FOV / 2))))
except:
overlap = 0
if overlap < 0:
overlap = 0
overlap = 0
return mergeFrames(imgs, kSIZE, overlap)
if not dist:
pass