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62 lines
2 KiB
Python
62 lines
2 KiB
Python
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import numpy as np
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import cv2
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import itertools as it
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from scene import *
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class SceneFromVideo(Scene):
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def construct(self, file_name,
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freeze_last_frame = True,
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time_range = None):
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cap = cv2.VideoCapture(file_name)
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self.shape = (
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int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)),
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int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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)
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fps = cap.get(cv2.cv.CV_CAP_PROP_FPS)
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self.frame_duration = 1.0/fps
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frame_count = int(cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
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if time_range is None:
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start_frame = 0
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end_frame = frame_count
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else:
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start_frame, end_frame = map(lambda t : fps*t, time_range)
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frame_count = end_frame - start_frame
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print "Reading in " + file_name + "..."
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progress_bar = progressbar.ProgressBar(maxval=frame_count)
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progress_bar.start()
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for count in it.count():
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returned, frame = cap.read()
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if count < start_frame:
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continue
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if not returned or count > end_frame:
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break
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# b, g, r = cv2.split(frame)
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# self.frames.append(cv2.merge([r, g, b]))
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self.frames.append(frame)
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progress_bar.update(min(len(self.frames), frame_count))
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cap.release()
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progress_bar.finish()
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if freeze_last_frame and len(self.frames) > 0:
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self.original_background = self.background = self.frames[-1]
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def apply_gaussian_blur(self, ksize = (5, 5), sigmaX = 5):
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self.frames = [
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cv2.GaussianBlur(frame, ksize, sigmaX)
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for frame in self.frames
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]
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def apply_edge_detection(self, threshold1 = 50, threshold2 = 100):
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edged_frames = [
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cv2.Canny(frame, threshold1, threshold2)
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for frame in self.frames
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]
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for index in range(len(self.frames)):
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for i in range(3):
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self.frames[index][:,:,i] = edged_frames[index]
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