我们从Python开源项目中，提取了以下17个代码示例，用于说明如何使用PIL.Image.ADAPTIVE。
def crop(self, file, coordinates): """Crop filename and overwrite it.""" try: filename = file.filename extension = self.get_filename_extension(filename) from io import BytesIO m = BytesIO() im = Image.open(file) target = im.crop(coordinates) # target = target.resize(self.size, Image.ANTIALIAS) # Scale down the image to Indexed mode scale_down_img = target.convert('P', colors=255, palette=Image.ADAPTIVE) scale_down_img.save(m, format=extension) file.stream = m file.stream.seek(0) return True except: return False
def extract_image(net, image_file): batch_size = 1 transformer = set_transformer(net) if image_file.split('.')[-1] == 'gif': img = Image.open(image_file).convert("P",palette=Image.ADAPTIVE, colors=256) newfile = ''.join(image_file.split('.')[:-1])+'.png' for i, frame in enumerate(iter_frames(img)): frame.save(newfile,**frame.info) image_file = newfile img = cv2.imread(image_file) img = img.astype('float') / 255 net.blobs['data'].data[:] = transformer.preprocess('data', img) net.forward() blobs_out_pool5 = net.blobs['pool5'].data[0,:,0,0] return blobs_out_pool5
def save_target_image(self, source, name, x_offset, y_offset, x_size, y_size, flip, convert): m_img = Image.open(source) if x_size!=0 and y_size!=0: m_img = m_img.crop((x_offset, y_offset, x_offset + x_size, y_offset + y_size)) if flip is True: m_img = m_img.transpose(Image.FLIP_LEFT_RIGHT) if convert is True: m_img.load() alpha = m_img.split()[- 1] m_img = m_img.convert('RGB').convert('P', palette=Image.ADAPTIVE, colors=255) mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0) m_img.paste(255, mask) m_img.save(join(self.emotedb_path, name) + ".png", transparency=255, optimize=True) else: m_img.save(join(self.emotedb_path, name) + ".png", optimize=True)
def save_target_image(self, source, name, x_offset, y_offset, x_size, y_size, flip, convert): m_img = Image.open(source) if x_size!=0 and y_size!=0: m_img = m_img.crop((x_offset, y_offset, x_offset + x_size, y_offset + y_size)) if flip is True: m_img = m_img.transpose(Image.FLIP_LEFT_RIGHT) if convert is True: m_img.load() alpha = m_img.split()[- 1] m_img = m_img.convert('RGB').convert('P', palette=Image.ADAPTIVE, colors=255) mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0) m_img.paste(255, mask) m_img.save(join(self.emotes_path, name) + ".png", transparency=255, optimize=True) else: m_img.save(join(self.emotes_path, name) + ".png", optimize=True)
def set_text(self, text): font = ImageFont.truetype(self.fontname, 14) formatted_lines = [] max_width = 400 max_height = 0 offset = 5 for line in text.split("\n"): width, height = font.getsize(line) if width > max_width: max_width = width splits = textwrap.wrap(line, width=40) max_height += (height * len(splits)) formatted_lines.extend(splits) max_height += 10 spoiler_im = self.get_spoiler_text(max_width, max_height) im = Image.new("RGB", (max_width, max_height), (54, 57, 62)) draw = ImageDraw.Draw(im) for line in formatted_lines: width, height = font.getsize(line) draw.text((5, offset), line, font=font) offset += height content_im = im.convert('P', palette=Image.ADAPTIVE, colors=5) spoiler_im.save("res/temp.gif", "GIF", save_all=True, append_images=[content_im])
def get_spoiler_text(self, width=400, height=100): im = Image.new("RGB", (width, height), (54, 57, 62)) d = ImageDraw.Draw(im) font = ImageFont.truetype(self.fontname, 14) d.text((5, 5), "( Hover to reveal spoiler )", font=font) return im.convert('P', palette=Image.ADAPTIVE, colors=5)
def __upload_avatar(self, auth, upload): """ If a valid token for a user is presented, upload a new avatar to: private/images/avatars/{username.png} Since the path is fixed per username, this is a detail managed in the preferences form, but isn't tracked in the preferences token. """ pixel_width = 80 # 32 bits rgba per pixel, times number of pixels. File shouldn't be bigger max_image_size = pixel_width * pixel_width * 4 if upload == '' or upload == None or auth.account.valid == False: return # If file is too large for its bytes-size, return. if len(upload) > max_image_size: return try: # Check if it's an 80x80 PNG # If not, return an error response # If it is a decent image, write to the image path.tmp # Then atomic overwrite the existing image tmp = self.avatar + "." + self.cookie_id iotmp = BytesIO(upload) src = Image.open(iotmp) if src.size[0] == 80 and src.size[1] == 80: dst = src.convert('RGB').convert('P', palette=Image.ADAPTIVE, colors=128) dst.save(tmp, "PNG") rename(tmp, self.avatar) except OSError: syslog.syslog("oserror when dealing with image upload") return except IOError: syslog.syslog("ioerror, likely from the image failing to open") return
def _convert2pilp(im): Image = _getImage() return im.convert("P", dither=Image.NONE, palette=Image.ADAPTIVE)
def drawToPILP(d, dpi=72, bg=0xffffff, configPIL=None, showBoundary=rl_config._unset_): Image = _getImage() im = drawToPIL(d, dpi=dpi, bg=bg, configPIL=configPIL, showBoundary=showBoundary) return im.convert("P", dither=Image.NONE, palette=Image.ADAPTIVE)
def watermark(img, **kwargs): asset_dir = app.config.get('ASSET_PATH') mark = None if 'mark' not in kwargs else kwargs['mark'] filename = 'u.f.png' if 'filename' not in kwargs else kwargs['filename'] if mark is None: mark = Image.open(os.path.join(asset_dir, 'watermarks',filename)) x = 16 y = img.size[1] - 16 - mark.size[1] if img.mode != 'RGBA': img = img.convert('RGBA') img.paste(mark, box=(x, y), mask=mark) return img.convert('P', palette=Image.ADAPTIVE, colors=255)
def _limitImageColors(self, image, paletteSlots=0): # Convert the image to RGB, then posterize to clamp the color channels to 5 bit values image = image.convert("RGB") image = ImageOps.posterize(image, 5) return image.convert("P", palette=Image.ADAPTIVE, colors=paletteSlots) # Reads an npf image from buffer, and returns an array of RGBA pixels
def convertImagesToPIL(self, images, dither, nq=0): """ convertImagesToPIL(images, nq=0) Convert images to Paletted PIL images, which can then be written to a single animaged GIF. """ # Convert to PIL images images2 = [] for im in images: if isinstance(im, Image.Image): images2.append(im) elif np and isinstance(im, np.ndarray): if im.ndim==3 and im.shape[2]==3: im = Image.fromarray(im,'RGB') elif im.ndim==3 and im.shape[2]==4: im = Image.fromarray(im[:,:,:3],'RGB') elif im.ndim==2: im = Image.fromarray(im,'L') images2.append(im) # Convert to paletted PIL images images, images2 = images2, [] if nq >= 1: # NeuQuant algorithm for im in images: im = im.convert("RGBA") # NQ assumes RGBA nqInstance = NeuQuant(im, int(nq)) # Learn colors from image if dither: im = im.convert("RGB").quantize(palette=nqInstance.paletteImage()) else: im = nqInstance.quantize(im) # Use to quantize the image itself images2.append(im) else: # Adaptive PIL algorithm AD = Image.ADAPTIVE for im in images: im = im.convert('P', palette=AD, dither=dither) images2.append(im) # Done return images2
def convertImagesToPIL(self, images, dither, nq=0): """ convertImagesToPIL(images, nq=0) Convert images to Paletted PIL images, which can then be written to a single animaged GIF. """ # Convert to PIL images images2 = [] for im in images: if isinstance(im, Image.Image): images2.append(im) elif np and isinstance(im, np.ndarray): if im.ndim == 3 and im.shape[2] == 3: im = Image.fromarray(im, 'RGB') elif im.ndim == 3 and im.shape[2] == 4: im = Image.fromarray(im[:, :, :3], 'RGB') elif im.ndim == 2: im = Image.fromarray(im, 'L') images2.append(im) # Convert to paletted PIL images images, images2 = images2, [] if nq >= 1: # NeuQuant algorithm for im in images: im = im.convert("RGBA") # NQ assumes RGBA nqInstance = NeuQuant(im, int(nq)) # Learn colors from image if dither: im = im.convert("RGB").quantize(palette=nqInstance.paletteImage()) else: im = nqInstance.quantize(im) # Use to quantize the image itself images2.append(im) else: # Adaptive PIL algorithm AD = Image.ADAPTIVE for im in images: im = im.convert('P', palette=AD, dither=dither) images2.append(im) # Done return images2
def convertImagesToPIL(self, images, dither, nq=0, images_info=None): """ convertImagesToPIL(images, nq=0) Convert images to Paletted PIL images, which can then be written to a single animated GIF. """ # Convert to PIL images images2 = [] for im in images: if isinstance(im, Image.Image): images2.append(im) elif np and isinstance(im, np.ndarray): if im.ndim == 3 and im.shape[2] == 3: im = Image.fromarray(im, 'RGB') elif im.ndim == 3 and im.shape[2] == 4: # im = Image.fromarray(im[:,:,:3],'RGB') self.transparency = True im = Image.fromarray(im[:, :, :4], 'RGBA') elif im.ndim == 2: im = Image.fromarray(im, 'L') images2.append(im) # Convert to paletted PIL images images, images2 = images2, [] if nq >= 1: # NeuQuant algorithm for im in images: im = im.convert("RGBA") # NQ assumes RGBA nqInstance = NeuQuant(im, int(nq)) # Learn colors from image if dither: im = im.convert("RGB").quantize(palette=nqInstance.paletteImage(), colors=255) else: im = nqInstance.quantize(im, colors=255) # Use to quantize the image itself self.transparency = True # since NQ assumes transparency if self.transparency: alpha = im.split()[3] mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0) im.paste(255, mask=mask) images2.append(im) else: # Adaptive PIL algorithm AD = Image.ADAPTIVE # for index,im in enumerate(images): for i in range(len(images)): im = images[i].convert('RGB').convert('P', palette=AD, dither=dither, colors=255) if self.transparency: alpha = images[i].split()[3] mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0) im.paste(255, mask=mask) images2.append(im) # Done return images2
def _get_icon_info(self): """Get icon in PNG format and information for searching for similar icons @return: tuple of (image data in PNG format encoded as base64, md5 hash of image data, md5 hash of "simplified" image for fuzzy matching) """ if not self.pe: return None, None, None try: rt_group_icon_idx = [entry.id for entry in self.pe.DIRECTORY_ENTRY_RESOURCE.entries].index(pefile.RESOURCE_TYPE['RT_GROUP_ICON']) rt_group_icon_dir = self.pe.DIRECTORY_ENTRY_RESOURCE.entries[rt_group_icon_idx] entry = rt_group_icon_dir.directory.entries[0] offset = entry.directory.entries[0].data.struct.OffsetToData size = entry.directory.entries[0].data.struct.Size peicon = PEGroupIconDir(self.pe.get_memory_mapped_image()[offset:offset+size]) bigwidth = 0 bigheight = 0 bigbpp = 0 bigidx = -1 iconidx = 0 for idx,icon in enumerate(peicon.icons): if icon.bWidth >= bigwidth and icon.bHeight >= bigheight and icon.wBitCount >= bigbpp: bigwidth = icon.bWidth bigheight = icon.bHeight bigbpp = icon.wBitCount bigidx = icon.nID iconidx = idx rt_icon_idx = [entry.id for entry in self.pe.DIRECTORY_ENTRY_RESOURCE.entries].index(pefile.RESOURCE_TYPE['RT_ICON']) rt_icon_dir = self.pe.DIRECTORY_ENTRY_RESOURCE.entries[rt_icon_idx] for entry in rt_icon_dir.directory.entries: if entry.id == bigidx: offset = entry.directory.entries[0].data.struct.OffsetToData size = entry.directory.entries[0].data.struct.Size icon = peicon.get_icon_file(iconidx, self.pe.get_memory_mapped_image()[offset:offset+size]) strio = StringIO() output = StringIO() strio.write(icon) strio.seek(0) img = Image.open(strio) img.save(output, format="PNG") img = img.resize((8,8), Image.BILINEAR) img = img.convert("RGB").convert("P", palette=Image.ADAPTIVE, colors=2).convert("L") lowval = img.getextrema()[0] img = img.point(lambda i: 255 if i > lowval else 0) img = img.convert("1") simplified = bytearray(img.getdata()) m = hashlib.md5() m.update(output.getvalue()) fullhash = m.hexdigest() m = hashlib.md5() m.update(simplified) simphash = m.hexdigest() return base64.b64encode(output.getvalue()), fullhash, simphash except: pass return None, None, None
def writeGif(filename, images, duration=0.1, loops=0, dither=1): """ writeGif(filename, images, duration=0.1, loops=0, dither=1) Write an animated gif from the specified images. images should be a list of numpy arrays of PIL images. Numpy images of type float should have pixels between 0 and 1. Numpy images of other types are expected to have values between 0 and 255. """ if PIL is None: raise RuntimeError("Need PIL to write animated gif files.") AD = Image.ADAPTIVE images2 = [] # convert to PIL for im in images: if isinstance(im,Image.Image): images2.append( im.convert('P', palette=AD, dither=dither) ) elif np and isinstance(im, np.ndarray): if im.dtype == np.uint8: pass elif im.dtype in [np.float32, np.float64]: im = (im*255).astype(np.uint8) else: im = im.astype(np.uint8) # convert if len(im.shape)==3 and im.shape[2]==3: im = Image.fromarray(im,'RGB').convert('P', palette=AD, dither=dither) elif len(im.shape)==2: im = Image.fromarray(im,'L').convert('P', palette=AD, dither=dither) else: raise ValueError("Array has invalid shape to be an image.") images2.append(im) else: raise ValueError("Unknown image type.") # check duration if hasattr(duration, '__len__'): if len(duration) == len(images2): durations = [d for d in duration] else: raise ValueError("len(duration) doesn't match amount of images.") else: durations = [duration for im in images2] # open file fp = open(filename, 'wb') # write try: n = _writeGifToFile(fp, images2, durations, loops) print n, 'frames written' finally: fp.close()
