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