def main():
f = "colours"
length = 151
r = 90
c = 120
nbins = 10
colors = np.zeros((length, r, c, 3), dtype=np.uint8)
grays = np.zeros((length, r, c), dtype=np.uint8)
# Read the images and store them in color and grayscale formats
for i in range(length):
im = Image.open(f+"/dwc"+str(i+1).zfill(3)+".png").convert('RGB')
colors[i] = np.asarray(im, dtype=np.uint8)
grays[i] = np.asarray(im.convert('L'))
# Save the color and grayscale images
for i in range(length):
out_col = Image.fromarray(colors[i])
out_gray = Image.fromarray(grays[i])
out_col.save("out/col_im/dwc_col"+str(i+1).zfill(3)+".png")
out_gray.save("out/gray_im/dwc_gray"+str(i+1).zfill(3)+".png")
# Draw histograms
colorhs, grayhs = histograms(length, nbins, colors, grays)
# Kmeans
clustering(length, nbins, colorhs, grayhs, colors, grays)
# Other Methods
other(length, nbins, grayhs, grays)
import histograms histograms.histograms(["skewness_east.txt", "skewness_north.txt"], "Skewness") histograms.histograms(["aratio_east.txt", "aratio_north.txt"], "Aspect Ratio") histograms.histograms(["skewness_east_observation.txt", "skewness_north_observation.txt"], "Skewness observation")
effectEnd = 0
high = 0
index = 0
video = cv.VideoCapture("julia.avi")
video.open("julia.avi")
while (True):
index += 1
ret, frame = video.read()
cv.imshow('frame', frame)
if cv.waitKey(1) & 0xFF == ord('q'):
break
if isFirstIteration:
prevHistograms = hst.histograms(frame)
prevEdges, prevDilatedEdges = sbl.sobelConvolution(frame)
prevMaxp = 0
isFirstIteration = False
else:
histograms = hst.histograms(frame)
edges, dilatedEdges = sbl.sobelConvolution(frame)
if hst.distance(histograms, prevHistograms) <= 1.9 and not isFading:
print("Histo -> Cut at frame : " + str(index))
elif hst.distance(histograms, prevHistograms) <= 2.6:
if not isFading:
isFading = True
fadeStart = index
else:
if isFading:
