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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
-
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