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: