#!/usr/bin/env python ''' Video capture sample. Sample shows how VideoCapture class can be used to acquire video frames from a camera of a movie file. Also the sample provides an example of procedural video generation by an object, mimicking the VideoCapture interface (see Chess class). 'create_capture' is a convinience function for capture creation, falling back to procedural video in case of error. Usage: video.py [--shotdir ] [source0] [source1] ...' sourceN is an - integer number for camera capture - name of video file - synth: for procedural video Synth examples: synth:bg=../data/lena.jpg:noise=0.1 synth:class=chess:bg=../data/lena.jpg:noise=0.1:size=640x480 Keys: ESC - exit SPACE - save current frame to directory ''' # Python 2/3 compatibility from __future__ import print_function import numpy as np from numpy import pi, sin, cos import cv2 # built-in modules from time import clock # local modules from tst_scene_render import TestSceneRender import common class VideoSynthBase(object): def __init__(self, size=None, noise=0.0, bg = None, **params): self.bg = None self.frame_size = (640, 480) if bg is not None: self.bg = cv2.imread(bg, 1) h, w = self.bg.shape[:2] self.frame_size = (w, h) if size is not None: w, h = map(int, size.split('x')) self.frame_size = (w, h) self.bg = cv2.resize(self.bg, self.frame_size) self.noise = float(noise) def render(self, dst): pass def read(self, dst=None): w, h = self.frame_size if self.bg is None: buf = np.zeros((h, w, 3), np.uint8) else: buf = self.bg.copy() self.render(buf) if self.noise > 0.0: noise = np.zeros((h, w, 3), np.int8) cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise) buf = cv2.add(buf, noise, dtype=cv2.CV_8UC3) return True, buf def isOpened(self): return True class Book(VideoSynthBase): def __init__(self, **kw): super(Book, self).__init__(**kw) backGr = cv2.imread('../data/graf1.png') fgr = cv2.imread('../data/box.png') self.render = TestSceneRender(backGr, fgr, speed = 1) def read(self, dst=None): noise = np.zeros(self.render.sceneBg.shape, np.int8) cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise) return True, cv2.add(self.render.getNextFrame(), noise, dtype=cv2.CV_8UC3) class Cube(VideoSynthBase): def __init__(self, **kw): super(Cube, self).__init__(**kw) self.render = TestSceneRender(cv2.imread('../data/pca_test1.jpg'), deformation = True, speed = 1) def read(self, dst=None): noise = np.zeros(self.render.sceneBg.shape, np.int8) cv2.randn(noise, np.zeros(3), np.ones(3)*255*self.noise) return True, cv2.add(self.render.getNextFrame(), noise, dtype=cv2.CV_8UC3) class Chess(VideoSynthBase): def __init__(self, **kw): super(Chess, self).__init__(**kw) w, h = self.frame_size self.grid_size = sx, sy = 10, 7 white_quads = [] black_quads = [] for i, j in np.ndindex(sy, sx): q = [[j, i, 0], [j+1, i, 0], [j+1, i+1, 0], [j, i+1, 0]] [white_quads, black_quads][(i + j) % 2].append(q) self.white_quads = np.float32(white_quads) self.black_quads = np.float32(black_quads) fx = 0.9 self.K = np.float64([[fx*w, 0, 0.5*(w-1)], [0, fx*w, 0.5*(h-1)], [0.0,0.0, 1.0]]) self.dist_coef = np.float64([-0.2, 0.1, 0, 0]) self.t = 0 def draw_quads(self, img, quads, color = (0, 255, 0)): img_quads = cv2.projectPoints(quads.reshape(-1, 3), self.rvec, self.tvec, self.K, self.dist_coef) [0] img_quads.shape = quads.shape[:2] + (2,) for q in img_quads: cv2.fillConvexPoly(img, np.int32(q*4), color, cv2.LINE_AA, shift=2) def render(self, dst): t = self.t self.t += 1.0/30.0 sx, sy = self.grid_size center = np.array([0.5*sx, 0.5*sy, 0.0]) phi = pi/3 + sin(t*3)*pi/8 c, s = cos(phi), sin(phi) ofs = np.array([sin(1.2*t), cos(1.8*t), 0]) * sx * 0.2 eye_pos = center + np.array([cos(t)*c, sin(t)*c, s]) * 15.0 + ofs target_pos = center + ofs R, self.tvec = common.lookat(eye_pos, target_pos) self.rvec = common.mtx2rvec(R) self.draw_quads(dst, self.white_quads, (245, 245, 245)) self.draw_quads(dst, self.black_quads, (10, 10, 10)) classes = dict(chess=Chess, book=Book, cube=Cube) presets = dict( empty = 'synth:', lena = 'synth:bg=../data/lena.jpg:noise=0.1', chess = 'synth:class=chess:bg=../data/lena.jpg:noise=0.1:size=640x480', book = 'synth:class=book:bg=../data/graf1.png:noise=0.1:size=640x480', cube = 'synth:class=cube:bg=../data/pca_test1.jpg:noise=0.0:size=640x480' ) def create_capture(source = 0, fallback = presets['chess']): '''source: or '||synth [:= [:...]]' ''' source = str(source).strip() chunks = source.split(':') # handle drive letter ('c:', ...) if len(chunks) > 1 and len(chunks[0]) == 1 and chunks[0].isalpha(): chunks[1] = chunks[0] + ':' + chunks[1] del chunks[0] source = chunks[0] try: source = int(source) except ValueError: pass params = dict( s.split('=') for s in chunks[1:] ) cap = None if source == 'synth': Class = classes.get(params.get('class', None), VideoSynthBase) try: cap = Class(**params) except: pass else: cap = cv2.VideoCapture(source) if 'size' in params: w, h = map(int, params['size'].split('x')) cap.set(cv2.CAP_PROP_FRAME_WIDTH, w) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, h) if cap is None or not cap.isOpened(): print('Warning: unable to open video source: ', source) if fallback is not None: return create_capture(fallback, None) return cap if __name__ == '__main__': import sys import getopt print(__doc__) args, sources = getopt.getopt(sys.argv[1:], '', 'shotdir=') args = dict(args) shotdir = args.get('--shotdir', '.') if len(sources) == 0: sources = [ 0 ] caps = list(map(create_capture, sources)) shot_idx = 0 while True: imgs = [] for i, cap in enumerate(caps): ret, img = cap.read() imgs.append(img) cv2.imshow('capture %d' % i, img) ch = 0xFF & cv2.waitKey(1) if ch == 27: break if ch == ord(' '): for i, img in enumerate(imgs): fn = '%s/shot_%d_%03d.bmp' % (shotdir, i, shot_idx) cv2.imwrite(fn, img) print(fn, 'saved') shot_idx += 1 cv2.destroyAllWindows()