def Speed(old_frame, pts, frame, frame_anterior, rate, fps):
frame = np.asarray(frame)
mask = np.zeros(shape=frame.shape[0:2])
for dic in old_frame:
mask[dic['topleft']['y']:dic['bottomright']['y'],
dic['topleft']['x']:dic['bottomright']['x']] = dic['id']
mask_h, M = perspective.four_point_transform(mask, pts)
frame_h, M = perspective.four_point_transform(frame, pts)
frame_ah, M = perspective.four_point_transform(frame_anterior, pts)
for i in range(len(old_frame)):
binary = (mask_h == old_frame[i]['id'])
cv2.imwrite('old.png', frame_ah)
cv2.imwrite('new.png', frame_h)
os.system('./deepflow2 old.png new.png sintel.flo')
file = 'sintel.flo'
flow = read(file)
mod = np.linalg.norm(flow, axis=2)
plt.imsave('opticalflow/' + str(framen - 1) + '.png', mod)
velocidad = float(np.mean(mod[binary == True]) * fps / rate * 3.6)
if (velocidad > (np.mean(old_frame[i]['speed']) / 2)) | (
not old_frame[i]['speed']):
old_frame[i]['speed'].append(velocidad)
else:
old_frame[i]['speed'].append(np.mean(old_frame[i]['speed']))
return old_frame, M
def main(flowfileFolder, outputPath):
list = os.listdir(flowfileFolder) # dir is your directory path
number_files = len(list)
print(number_files)
video = cv2.VideoWriter(outputPath,
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 30,
(800, 410))
for i in range(number_files):
index = (i + 1) * 3
flowPath = os.path.join(flowfileFolder, 'flow%d.flo' % index)
flow = readFlowFile.read(flowPath)
img = computeImg(flow)
video.write(img)
# cv2.imshow('flow', img)
# cv2.waitKey()
video.release()
rad = np.sqrt(np.multiply(u, u) + np.multiply(v, v))
maxrad = max([maxrad, np.amax(rad)])
print('max flow: %.4f flow range: u = %.3f .. %.3f; v = %.3f .. %.3f\n' %
(maxrad, minu, maxu, minv, maxv))
u = u / (maxrad + eps)
v = v / (maxrad + eps)
img = computeColor(u, v)
return img
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--flowfile',
type=str,
default='colorTest.flo',
help='Flow file')
parser.add_argument('--write',
type=bool,
default=False,
help='write flow as png')
file = parser.parse_args().flowfile
flow = readFlowFile.read(file)
flow /= 6
print(flow.shape)
img = computeImg(flow)
#cv2.imshow(file, img)
k = cv2.waitKey()
if parser.parse_args().write:
cv2.imwrite(file[:-4] + '.png', img)
file.close()
dirName = ""
totalMagField = []
temp = [] # temporal : List of frame magnitude, spatial : Field of pixel magnitude
pixel = 0
maxMag = np.inf
aveMag = np.inf
for i in range(0, 400):
fileName = "C:/Project/COG/ABMCTS-OF/flow_" + str(i) + "_" + str(i+1) + ".flo"
if os.path.isfile(fileName):
#### Main Calculation Starts
flowField = readFlowFile.read(fileName)
# Get magnitude of each vector and apply filter
magField = GetMagField(flowField)
if (temp == []):
temp = magField
else:
# Sum up all the magnitude fields into one field.
temp = temp + magField
# os.remove(fileName)
# if (os.path.isfile(str(i+1)+".png")):
# os.remove(str(i+1)+".png")
# Getting total displacement by adding all the magnitudes.
# print (temp)
totalDisplacement = float(sum(sum(temp)))
temp = np.array(temp)
u = x * range_f / s2 - range_f
v = y * range_f / s2 - range_f
img = computeColor.computeColor(u / truerange, v / truerange)
img[s2, :, :] = 0
img[:, s2, :] = 0
cv2.imshow('test color pattern', img)
k = cv2.waitKey()
F = np.stack((u, v), axis=2)
writeFlowFile.write(F, 'colorTest.flo')
flow = readFlowFile.read('colorTest.flo')
u = flow[:, :, 0]
v = flow[:, :, 1]
img = computeColor.computeColor(u / truerange, v / truerange)
img[s2, :, :] = 0
img[:, s2, :] = 0
cv2.imshow('saved and reloaded test color pattern', img)
k = cv2.waitKey()
# color encoding scheme for optical flow
img = computeColor.computeColor(u / range_f / math.sqrt(2),
v / range_f / math.sqrt(2))
u = x * range_f / s2 - range_f
v = y * range_f / s2 - range_f
img = computeColor.computeColor(u / truerange, v / truerange)
img[s2, :, :] = 0
img[:, s2, :] = 0
cv2.imshow("Test color pattern", img)
k = cv2.waitKey()
F = np.stack((u, v), axis=2)
writeFlowFile.write(F, Path("colorTest.flo"))
flow = readFlowFile.read(Path("colorTest.flo"))
u = flow[:, :, 0]
v = flow[:, :, 1]
img = computeColor.computeColor(u / truerange, v / truerange)
img[s2, :, :] = 0
img[:, s2, :] = 0
cv2.imshow("Saved and reloaded test color pattern", img)
k = cv2.waitKey()
# Color encoding scheme for optical flow
img = computeColor.computeColor(u / range_f / math.sqrt(2),
v / range_f / math.sqrt(2))
rad = np.sqrt(np.multiply(u, u) + np.multiply(v, v))
maxrad = max([maxrad, np.amax(rad)])
print(f"max flow:")
print(f" {maxrad:.4f}")
print(f"flow range:")
print(f" u = {minu:.3f} .. {maxu:.3f}")
print(f" v = {minv:.3f} .. {maxv:.3f}")
u = u / (maxrad + eps)
v = v / (maxrad + eps)
img = computeColor(u, v)
return img
# ==================================================================================================
if (__name__ == "__main__"):
import readFlowFile
parser = argparse.ArgumentParser()
parser.add_argument("flow_file", type=str, default="colorTest.flo", help="Flow file")
parser.add_argument("--write", action="store_true", help="Write flow as PNG")
args = parser.parse_args()
args.flow_file = Path(args.flow_file)
flow = readFlowFile.read(args.flow_file)
img = computeImg(flow)
cv2.imshow(str(args.flow_file), img)
k = cv2.waitKey()
if parser.parse_args().write:
cv2.imwrite(str(args.flow_file.with_suffix(".png")), img)
