専門ユニット2/山内研セミナー(2021/11/10)

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サンプルプログラム hist.ipynb

hist.ipynb
''' This is a sample for histogram plotting for RGB images and grayscale images for better understanding of colour distribution
  
Benefit : Learn how to draw histogram of images
          Get familier with cv.calcHist, cv.equalizeHist,cv.normalize and some drawing functions
  
Level : Beginner or Intermediate
  
Functions : 1) hist_curve : returns histogram of an image drawn as curves
            2) hist_lines : return histogram of an image drawn as bins ( only for grayscale images )
  
Usage : python hist.py <image_file>
  
Abid Rahman 3/14/12 debug Gary Bradski
'''
  
import numpy as np
import cv2
  
bins = np.arange(256).reshape(256,1)
  
def hist_curve(im):
    h = np.zeros((300,256,3))
    if len(im.shape) == 2:
        color = [(255,255,255)]
    elif im.shape[2] == 3:
        color = [ (255,0,0),(0,255,0),(0,0,255) ]
    for ch, col in enumerate(color):
        hist_item = cv2.calcHist([im],[ch],None,[256],[0,256])
        cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
        hist=np.int32(np.around(hist_item))
        pts = np.int32(np.column_stack((bins,hist)))
        cv2.polylines(h,[pts],False,col)
    y=np.flipud(h)
    return y
  
def hist_lines(im):
    h = np.zeros((300,256,3))
    if len(im.shape)!=2:
        print("hist_lines applicable only for grayscale images")
        #print("so converting image to grayscale for representation"
        im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
    hist_item = cv2.calcHist([im],[0],None,[256],[0,256])
    cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
    hist=np.int32(np.around(hist_item))
    for x,y in enumerate(hist):
        cv2.line(h,(x,0),(x,y[0]),(255,255,255))
    y = np.flipud(h)
    return y
  
def main():
    import sys
  
    fname = 'lena.jpg'
    im = cv2.imread(fname)
    
    if im is None:
        print('Failed to load image file:', fname)
        sys.exit(1)
  
    gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
  
    print(''' Histogram plotting \n
    Keymap :\n
    a - show histogram for color image in curve mode \n
    b - show histogram in bin mode \n
    c - show equalized histogram (always in bin mode) \n
    d - show histogram for gray image in curve mode \n
    e - show histogram for a normalized image in curve mode \n
    Esc - exit \n
    ''')
  
    cv2.imshow('image',im)
    while True:
        k = cv2.waitKey(0)
        if k == ord('a'):
            curve = hist_curve(im)
            cv2.imshow('histogram',curve)
            cv2.imshow('image',im)
            print('a')
        elif k == ord('b'):
            print('b')
            lines = hist_lines(im)
            cv2.imshow('histogram',lines)
            cv2.imshow('image',gray)
        elif k == ord('c'):
            print('c')
            equ = cv2.equalizeHist(gray)
            lines = hist_lines(equ)
            cv2.imshow('histogram',lines)
            cv2.imshow('image',equ)
        elif k == ord('d'):
            print('d')
            curve = hist_curve(gray)
            cv2.imshow('histogram',curve)
            cv2.imshow('image',gray)
        elif k == ord('e'):
            print('e')
            norm = cv2.normalize(gray, gray, alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX)
            lines = hist_lines(norm)
            cv2.imshow('histogram',lines)
            cv2.imshow('image',norm)
        elif k == 27:
            print('ESC')
            cv2.destroyAllWindows()
            break
  
    print('Done')
  
main()
cv2.destroyAllWindows()
    

サンプルプログラム opt_flow.ipynb

opt_flow.ipynb
import numpy as np
import cv2
  
import video
  
def draw_flow(img, flow, step=16):
    h, w = img.shape[:2]
    y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int)
    fx, fy = flow[y,x].T
    lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)
    lines = np.int32(lines + 0.5)
    vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
    cv2.polylines(vis, lines, 0, (0, 255, 0))
    for (x1, y1), (_x2, _y2) in lines:
        cv2.circle(vis, (x1, y1), 1, (0, 255, 0), -1)
    return vis
  
def draw_hsv(flow):
    h, w = flow.shape[:2]
    fx, fy = flow[:,:,0], flow[:,:,1]
    ang = np.arctan2(fy, fx) + np.pi
    v = np.sqrt(fx*fx+fy*fy)
    hsv = np.zeros((h, w, 3), np.uint8)
    hsv[...,0] = ang*(180/np.pi/2)
    hsv[...,1] = 255
    hsv[...,2] = np.minimum(v*4, 255)
    bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
    return bgr
  
def warp_flow(img, flow):
    h, w = flow.shape[:2]
    flow = -flow
    flow[:,:,0] += np.arange(w)
    flow[:,:,1] += np.arange(h)[:,np.newaxis]
    res = cv2.remap(img, flow, None, cv2.INTER_LINEAR)
    return res
  
def main():
    fn = 0
    cam = video.create_capture(fn)
    _ret, prev = cam.read()
    prevgray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)
    show_hsv = False
    show_glitch = False
    cur_glitch = prev.copy()
  
    while True:
        _ret, img = cam.read()
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        flow = cv2.calcOpticalFlowFarneback(prevgray, gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
        prevgray = gray
  
        cv2.imshow('flow', draw_flow(gray, flow))
        if show_hsv:
            cv2.imshow('flow HSV', draw_hsv(flow))
        if show_glitch:
            cur_glitch = warp_flow(cur_glitch, flow)
            cv2.imshow('glitch', cur_glitch)
  
        ch = cv2.waitKey(5)
        if ch == 27:
            break
        if ch == ord('1'):
            show_hsv = not show_hsv
            print('HSV flow visualization is', ['off', 'on'][show_hsv])
        if ch == ord('2'):
            show_glitch = not show_glitch
            if show_glitch:
                cur_glitch = img.copy()
            print('glitch is', ['off', 'on'][show_glitch])

    print('Done')
  
main()
cv2.destroyAllWindows()