我们从Python开源项目中，提取了以下16个代码示例，用于说明如何使用PIL.Image.AFFINE。
def ScaleRotateTranslate(self, image, angle, center=None, new_center=None, scale=None, expand=False): ''' experimental - not used yet ''' if center is None: return image.rotate(angle, expand) angle = -angle / 180.0 * math.pi nx, ny = x, y = center if new_center != center: (nx, ny) = new_center sx = sy = 1.0 if scale: (sx, sy) = scale cosine = math.cos(angle) sine = math.sin(angle) a = cosine / sx b = sine / sx c = x - nx * a - ny * b d = -sine / sy e = cosine / sy f = y - nx * d - ny * e return image.transform(image.size, Image.AFFINE, (a,b,c,d,e,f), resample=Image.BICUBIC)
def skew_image(img, angle): """ Skew image using some math :param img: PIL image object :param angle: Angle in radians (function doesn't do well outside the range -1 -> 1, but still works) :return: PIL image object """ width, height = img.size # Get the width that is to be added to the image based on the angle of skew xshift = tan(abs(angle)) * height new_width = width + int(xshift) if new_width < 0: return img # Apply transform img = img.transform( (new_width, height), Image.AFFINE, (1, angle, -xshift if angle > 0 else 0, 0, 1, 0), Image.BICUBIC ) return img
def warpImage(background, image, parallelogram): ''' @params: background is unchanged image image is image to be warped parallelogram is the coordinates to warp the image to, starting at upper left and going clockwise returns a new image that is the composition of background and image after image has been warped ''' mapped = np.array([[parallelogram[0].x, parallelogram[1].x, parallelogram[2].x], [parallelogram[0].y, parallelogram[1].y, parallelogram[2].y], [1,1,1]]) width, height = image.size original = np.array([[0, width, width],[0, 0, height]]) #solve for affine matrix solution = np.dot(original, inv(mapped)) #unroll matrix into a sequence affine = (solution[0][0], solution[0][1], solution[0][2], solution[1][0], solution[1][1], solution[1][2]) transformed = image.transform(background.size, Image.AFFINE, affine) white = Image.new("RGBA", (width, height), "white") transformedMask = white.transform(background.size, Image.AFFINE, affine) background.paste(transformed, (0,0), transformedMask) return background
def ScaleRotateTranslate(image, angle, center = None, new_center = None, scale = None, resample=Image.BICUBIC): if (scale is None) and (center is None): return image.rotate(angle=angle, resample=resample) nx,ny = x,y = center sx=sy=1.0 if new_center: (nx,ny) = new_center if scale: (sx,sy) = (scale, scale) cosine = math.cos(angle) sine = math.sin(angle) a = cosine/sx b = sine/sx c = x-nx*a-ny*b d = -sine/sy e = cosine/sy f = y-nx*d-ny*e return image.transform(image.size, Image.AFFINE, (a,b,c,d,e,f), resample=resample)
def getEmoji(self): img = Image.new("RGBA",self.imageSize,self.backColor) draw = ImageDraw.Draw(img) l = len(self.textList) for i in range(0,l): img_str = Image.new("RGBA",(len(self.textList[i])*128,128),self.backColor) draw = ImageDraw.Draw(img_str) (size,x0,y0,x1,y1) = self.cutEffectiveRange(self.textList[i],len(self.textList[i])*64,int(128/l)) #(size,x0,y0,x1,y1) = self.cutEffectiveRange(self.textList[i],256,128/l) font = self.getFont(size) draw.text((x0,y0), self.textList[i], fill=self.fontColor, font=font) img_str.crop((0,0,x1,y1)) if x1 > 128: img_str = img_str.transform(img_str.size,Image.AFFINE,(x1/128.0,0,0,0,1,0),Image.BICUBIC) image_paste_x = 0 else: image_paste_x = int((128-x1)/2) if l != 1: img.paste(img_str,(image_paste_x,int((128/l)*i))) else: img.paste(img_str,(image_paste_x,int((128-y1)/2))) return img
def load_image_transform(self, idx, scale, rotation, trans_h, trans_w, flip): img_W = np.int( self.W*(1.0 + scale) ) img_H = np.int( self.H*(1.0 + scale) ) print >> sys.stderr, 'loading {}/00000.jpg'.format(idx) print >> sys.stderr, 'scale: {}; rotation: {}; translation: ({},{}); flip: {}.'.format(scale, rotation, trans_w, trans_h, flip) im = Image.open('{}/JPEGImages/480p/{}/00000.jpg'.format(self.davis_dir, idx)) im = im.resize((img_W,img_H)) im = im.transform((img_W,img_H),Image.AFFINE,(1,0,trans_w,0,1,trans_h)) im = im.rotate(rotation) if flip: im = im.transpose(Image.FLIP_LEFT_RIGHT) if scale>0: box = (np.int((img_W - self.W)/2), np.int((img_H - self.H)/2), np.int((img_W - self.W)/2)+self.W, np.int((img_H - self.H)/2)+self.H) im = im.crop(box) else: im = im.resize((self.W, self.H)) # im_name = 'img_{}_{}.jpg'.format(trans_h,trans_w) # im.save(im_name,"JPEG") # print(im.size) in_ = np.array(im, dtype=np.float32) in_ = in_[:,:,::-1] in_ -= self.mean return in_
def load_label_transform(self, idx, scale, rotation, trans_h, trans_w, flip): img_W = np.int( self.W*(1.0 + scale) ) img_H = np.int( self.H*(1.0 + scale) ) # print >> sys.stderr, 'loading {}'.format(idx) # print >> sys.stderr, 'scale: {}; rotation: {}; translation: ({},{}); flip: {}.'.format(scale, rotation, trans_w, trans_h, flip) im = Image.open('{}/Annotations/2017/{}/00000.png'.format(self.davis_dir, idx)) im = im.resize((img_W,img_H)) im = im.transform((img_W,img_H),Image.AFFINE,(1,0,trans_w,0,1,trans_h)) im = im.rotate(rotation) if flip: im = im.transpose(Image.FLIP_LEFT_RIGHT) if scale>0: w_start = np.int(random.random()*(img_W - self.W)) h_start = np.int(random.random()*(img_H - self.H)) box = (w_start, h_start, w_start+self.W, h_start+self.H) im = im.crop(box) else: im = im.resize((self.W, self.H)) # im_name = 'label_{}_{}.png'.format(trans_h,trans_w) # im.save(im_name,"PNG") # print(im.size) label = np.array(im, dtype=np.uint8) print >> sys.stderr, 'Number of Objects: {}'.format(np.max(label)) label = np.uint8((label>0)) return label
def load_image_transform(self, idx, scale, rotation, trans_h, trans_w, flip): img_W = np.int( self.W*(1.0 + scale) ) img_H = np.int( self.H*(1.0 + scale) ) print >> sys.stderr, 'loading {}'.format(idx) print >> sys.stderr, 'scale: {}; rotation: {}; translation: ({},{}); flip: {}.'.format(scale, rotation, trans_w, trans_h, flip) im = Image.open('{}/{}'.format(self.davis_dir, idx)) im = im.resize((img_W,img_H)) im = im.transform((img_W,img_H),Image.AFFINE,(1,0,trans_w,0,1,trans_h)) im = im.rotate(rotation) if flip: im = im.transpose(Image.FLIP_LEFT_RIGHT) if scale>0: box = (np.int((img_W - self.W)/2), np.int((img_H - self.H)/2), np.int((img_W - self.W)/2)+self.W, np.int((img_H - self.H)/2)+self.H) im = im.crop(box) else: im = im.resize((self.W, self.H)) # im_name = 'img_{}_{}.jpg'.format(trans_h,trans_w) # im.save(im_name,"JPEG") # print(im.size) in_ = np.array(im, dtype=np.float32) in_ = in_[:,:,::-1] in_ -= self.mean return in_
def load_label_transform(self, idx, scale, rotation, trans_h, trans_w, flip): img_W = np.int( self.W*(1.0 + scale) ) img_H = np.int( self.H*(1.0 + scale) ) # print >> sys.stderr, 'loading {}'.format(idx) # print >> sys.stderr, 'scale: {}; rotation: {}; translation: ({},{}); flip: {}.'.format(scale, rotation, trans_w, trans_h, flip) im = Image.open('{}/{}'.format(self.davis_dir, idx)) im = im.resize((img_W,img_H)) im = im.transform((img_W,img_H),Image.AFFINE,(1,0,trans_w,0,1,trans_h)) im = im.rotate(rotation) if flip: im = im.transpose(Image.FLIP_LEFT_RIGHT) if scale>0: w_start = np.int(random.random()*(img_W - self.W)) h_start = np.int(random.random()*(img_H - self.H)) box = (w_start, h_start, w_start+self.W, h_start+self.H) im = im.crop(box) else: im = im.resize((self.W, self.H)) # im_name = 'label_{}_{}.png'.format(trans_h,trans_w) # im.save(im_name,"PNG") # print(im.size) label = np.array(im, dtype=np.uint8) print >> sys.stderr, 'Number of Objects: {}'.format(np.max(label)) label = np.uint8((label>0)) return label
def affine(cls, old_paste, w, h, a): assert old_paste.is_image img_size = (int(w), int(h)) img = Image.open(old_paste.path).transform( img_size, Image.AFFINE, a, Image.BILINEAR) return cls.create_by_img(img, old_paste.filename, old_paste.mimetype)
def adjustImg(self): if self.img: xf = self.canvas.xform() xf = deepcopy(xf) xf = la.inverse(xf) # canvas coordinate of image origin ox, oy = self.canvas.canvasxy(self.imgbbox[0], self.imgbbox[1]) fx, fy = self.canvas.canvasxy(self.imgbbox[2], self.imgbbox[3]) cw, ch = self.canvas.winfo_width(), self.canvas.winfo_height() # max image width,height bx, by = self.canvas.canvasxy(0, 0) ex, ey = self.canvas.canvasxy(cw, ch) cbx, cby = self.canvas.canvasx(0), self.canvas.canvasy(0) cex, cey = self.canvas.canvasx(cw), self.canvas.canvasy(ch) cox, coy = self.canvas.canvasx(self.imgbbox[0]), self.canvas.canvasy(self.imgbbox[1]) cfx, cfy = self.canvas.canvasx(self.imgbbox[2]), self.canvas.canvasy(self.imgbbox[3]) ix = bx iy = by iw = cw ih = ch # print((ox,oy),(fx,fy),(bx,by),(ex,ey)) # print((ix,iy),(iw,ih)) # scale image contents, max size of cw,ch make sure to not overblow image size # self.simg = self.img.transform((iw,ih),Image.AFFINE,data=(xf[0][0],xf[0][1],xf[0][3],xf[1][0],xf[1][1],xf[1][3])) self.simg = self.img.transform((iw, ih), Image.AFFINE, data=(xf[0][0], xf[0][1], ix, xf[1][0], xf[1][1], iy)) self.canvas.coords(self.imgcid, ix, iy) # adjust image origin self.pimg = ImageTk.PhotoImage(self.simg) self.canvas.itemconfig(self.imgcid, image=self.pimg) # set new image self.canvas.tag_lower(self.imgcid, "segment") # just below segments # sys.stdout.flush()
def onWheel(self, ev): cx,cy = self.canvas.canvasxy(ev.x,ev.y) sf = 1.1 if (ev.delta < 0): sf = 1/sf # scale all objects on canvas self.canvas.zoom(cx, cy, sf) if self.img: imgsc = tuple(int(sf * c) for c in self.simg.size) ox,oy = self.canvas.canvasxy(0,0) cw,ch = self.canvas.winfo_reqwidth(),self.canvas.winfo_reqheight() cow,coh = self.canvas.canvasxy(cw,ch) if imgsc[0] > cw and imgsc[1] > ch: isz = (cw,ch) else: isz = imgsc xf = self.canvas.xform() xf = deepcopy(xf) xf = inverse(xf) #self.simg = self.img.transform(self.img.size,Image.AFFINE,data=(xf[0][0],xf[0][1],xf[0][3],xf[1][0],xf[1][1],xf[1][3])) self.simg = self.img.transform(imgsc,Image.AFFINE,data=(xf[0][0],0,0,0,xf[1][1],0)) self.pimg = ImageTk.PhotoImage(self.simg) #self.canvas.itemconfig(self.imgcid, image = self.pimg) x,y = self.canvas.coords(self.imgcid) print(x,y,self.img.size,ox,oy,cow,coh) if self.imgcid: self.canvas.delete(self.imgcid) self.imgcid = self.canvas.create_image(0, 0, image=self.pimg, anchor=tk.NW) sys.stdout.flush()
def __call__(self, img): """ Args: img (PIL.Image): Image to be translated. Returns: PIL.Image: Randomly translated image. """ if np.random.random() < 0.5: hshift = np.random.randint(-self.max_hshift,self.max_hshift) vshift = np.random.randint(-self.max_vshift,self.max_vshift) return img.transform(img.size, Image.AFFINE, (1, 0, hshift, 0, 1, vshift)) return img
def transform_sample(self, sample, rotate=0, scale=[1,1], shear=[0,0], offset=[0,0], mirror = False, bilinear=False): fname,im,meta = sample rot = numpy.matrix([[math.cos(rotate), math.sin(rotate)], [-math.sin(rotate), math.cos(rotate)]]) scale = numpy.matrix([[1.0/scale[0], 0], [0, 1.0 / scale[1]]]) shear_x = numpy.matrix([[1,shear[0]], [0,1]]) shear_y = numpy.matrix([[1,0], [shear[1],1]]) tr = rot*scale*shear_x*shear_y if self.get_data_type() == "image": mode = Image.BILINEAR if bilinear else Image.NEAREST center = (im.size[0]/2, im.size[1]/2) ox = center[0]-center[0]*tr[0,0]-center[1]*tr[0,1] - offset[0] oy = center[1]-center[0]*tr[1,0]-center[1]*tr[1,1] - offset[1] im = im.transform(im.size, Image.AFFINE, (tr[0,0],tr[0,1],ox,tr[1,0],tr[1,1],oy), resample=resample) if mirror: im = ImageOps.mirror(im) elif self.get_data_type() == "array": order = 1 if bilinear else 0 center=(im.shape[1]/2, im.shape[2]/2) ox=center[0] - center[0]*tr[0,0] - center[1]*tr[0,1] - offset[0] oy=center[1] - center[0]*tr[1,0] - center[1]*tr[1,1] - offset[1] r = numpy.zeros_like(im) r[0,:,:] = scipy.ndimage.interpolation.affine_transform(im[0,:,:], tr[0:2,0:2], [ox,oy], order=order) r[1,:,:] = scipy.ndimage.interpolation.affine_transform(im[1,:,:], tr[0:2,0:2], [ox,oy], order=order) r[2,:,:] = scipy.ndimage.interpolation.affine_transform(im[2,:,:], tr[0:2,0:2], [ox,oy], order=order) if mirror: r = r[:,:,::-1] im = r return (fname, im, meta) #random affine distorted images (destructive)
