def screenshotter(): im = pyautogui.screenshot(region=(350, 180, 625, 370)) im_left = im.crop((10, 210, 75, 350)) im_right = im.crop((505, 200, 565, 315)) im_left = im_left.rotate(-9.8) im_right = im_right.rotate(10) im_left_blurred = im_left.filter(filter=ImageFilter.BoxBlur(1)) im_right_blurred = im_right.filter(filter=ImageFilter.BoxBlur(1)) im.paste(im_left_blurred, (10, 210)) im.paste(im_right_blurred, (505, 200)) return im
def visualize(self, layer, filt, lr=1e-2, opt_steps=36): sz = self.size img = Image.fromarray( np.uint8(np.random.uniform(150, 180, (sz, sz, 3)))) activations = SaveFeatures(list(self.target.children())[layer]) gaussian_filter = get_gaussian_kernel() self.model.zero_grad() for outer in tqdm(range(self.upscaling_steps), leave=False): img_var = torch.unsqueeze(ToTensor()(img), 0).cuda(ORDINAL).requires_grad_(True) img_var.requires_grad_(True).cuda(ORDINAL) optimizer = torch.optim.Adam([img_var], lr=lr, weight_decay=1e-6) pbar = tqdm(range(opt_steps), leave=False) for n in pbar: optimizer.zero_grad() self.model(img_var) loss = -activations.features[ 0, filt].mean() + 0.00 * torch.norm(img_var) loss.backward() pbar.set_description(f'Loss: {loss.item()}') optimizer.step() sz = int(sz * self.upscaling_factor) img = ToPILImage()(img_var.squeeze(0)) if outer != self.upscaling_steps: img = img.resize((sz, sz)) img = img.filter(ImageFilter.BoxBlur(2)) self.output = img.copy() self.save(layer, filt) activations.close()
async def box_blur(client, message): try: userid = str(message.chat.id) if not os.path.isdir(f"./DOWNLOADS/{userid}"): os.makedirs(f"./DOWNLOADS/{userid}") download_location = "./DOWNLOADS" + "/" + userid + "/" + userid + ".jpg" if not message.reply_to_message.empty: msg = await message.reply_to_message.reply_text( "Downloading image", quote=True) a = await client.download_media(message=message.reply_to_message, file_name=download_location) await msg.edit("Processing Image...") im1 = Image.open(a) im2 = im1.filter(ImageFilter.BoxBlur(0)) edit_img_loc = "./DOWNLOADS" + "/" + userid + "/" + "box_blur.jpg" im2.save(edit_img_loc) await message.reply_chat_action("upload_photo") await message.reply_to_message.reply_photo(edit_img_loc, quote=True) await msg.delete() else: await message.reply_text("Why did you delete that??") try: shutil.rmtree(f"./DOWNLOADS/{userid}") except Exception: pass except Exception as e: print("box_blur-error - " + str(e)) if "USER_IS_BLOCKED" in str(e): return try: await message.reply_to_message.reply_text("Something went wrong!", quote=True) except Exception: return
def transform(self, frame): frame = frame.resize((self._width, self._height), Image.BILINEAR) frame = frame.rotate(self._rotation, Image.BILINEAR) if self._blur_in_frames > 0 and self._count_frame_total < self._blur_in_frames and not self._deinit_started: strength = int((self._blur_in_frames - self._count_frame_total) * 10 / self._blur_in_frames) frame = frame.filter(ImageFilter.BoxBlur(strength)) if self.blur_out_frames > 0 and self._deinit_started: strength = 10 - int( (self._blur_out_frames - self._count_frame_total) * 10 / self.blur_out_frames) frame = frame.filter(ImageFilter.BoxBlur(strength)) return frame
def save(self, *args, **kwargs): super().save(*args, **kwargs) # Load Image from File img_read = storage.open(self.img.name, 'rb') img = Image.open(img_read) # Convert Image to Array pixels = asarray(img) # Create the Detector Using the Default Weights detector = MTCNN() # Detect Faces in the Image results = detector.detect_faces(pixels) numfaces = len(results) # Create For Loop to Blur All Faces in Photo for i in range(numfaces): # Extract the Bounding Box from the First Face x1, y1, width, height = results[i]['box'] # Negative Coordinates Bug Fix x1, y1 = abs(x1), abs(y1) x2, y2 = x1 + width, y1 + height # Extract the Face face = pixels[y1:y2, x1:x2] # Blur Out Face Pixels faceimage = Image.fromarray(face) faceimage = faceimage.filter(ImageFilter.BoxBlur(radius=100)) # Reattach Blurred Faces to Original Photo img = img.copy() img.paste(faceimage, (x1, y1)) # Show the New Image # image.show() # Save the New Image in_mem_file = io.BytesIO() img.save(in_mem_file, format='JPEG')
def __getitem__(self, idx): img_number = np.random.randint(0, high=len(self.imgs)) x_coord = np.random.randint(0, high=self.x_dim - self.patch_size) y_coord = np.random.randint(0, high=self.y_dim - self.patch_size) if self.num_modalities > 1: seed = np.random.randint(2147483647) I_list = [] for m in self.modalities: random.seed(seed) I = Image.open( os.path.join(self.data_dir, m, self.imgs[img_number])) I_list.append( self.transformation( I.crop((x_coord, y_coord, x_coord + self.patch_size, y_coord + self.patch_size)))) patch = torch.cat(I_list, dim=0) else: I = Image.open(os.path.join( self.data_dir, self.imgs[img_number])).convert(mode='L') if self.binary_data: I = I.filter(ImageFilter.BoxBlur(1)) patch = self.transformation( I.crop((x_coord, y_coord, x_coord + self.patch_size, y_coord + self.patch_size))) if self.binary_data: # clamp values away from boundaries if binarized data patch = torch.clamp(patch, -0.35, 0.25) return patch, torch.ones((1))
def upload_image_swarm(file: FileStorage, username: str, is_public) -> (str, str): image_format = DICTIONARY_FORMAT[secure_filename( file.filename).split('.')[-1].lower()] url = SWARM_URL_NODE if is_public: headers = {"content-type": f"image/{image_format}"} else: headers = { "content-type": f"image/{image_format}", "Swarm-Encrypt": "true" } result = requests.post(url, data=file, headers=headers) # swarm_hash = json.loads(result.content.decode('utf8'))["reference"] swarm_hash = result.content.decode('utf8') image = Image.open(file) output = io.BytesIO() image.save(output, format=image_format) hex_data = output.getvalue() blob_client = BlobClient.from_connection_string( BLOB_CONNECTION_STRING, IMAGES_CONTAINER, f"{username.lower()}/{swarm_hash}.{image_format}") blob_client.upload_blob(hex_data, overwrite=True) blurry_image = Image.open(file).filter(ImageFilter.BoxBlur(30)) blurry_output = io.BytesIO() blurry_image.save(blurry_output, format=image_format) blurry_hex_data = blurry_output.getvalue() blurry_blob_client = BlobClient.from_connection_string( BLOB_CONNECTION_STRING, BLURRY_IMAGES_CONTAINER, f"{username.lower()}/{swarm_hash}.{image_format}") blurry_blob_client.upload_blob(blurry_hex_data, overwrite=True) return swarm_hash
def box_blur(image, *params): global box_blur_radius if (not box_blur_radius): box_blur_radius = 0 box_blur_radius += 2 print("From box blur") return image.filter(ImageFilter.BoxBlur(box_blur_radius))
def blurFaces(self): for top, right, bottom, left in self.faceLocations: faceImage = self.image[top:bottom, left:right] faceImage = Image.fromarray(faceImage) faceImage = faceImage.filter(ImageFilter.BoxBlur(23)) self.image[top:bottom, left:right] = faceImage
def edit_image(img, width, height): x, y = img.size temp = img.resize((width, height), Image.ANTIALIAS) temp = temp.filter(ImageFilter.BoxBlur(9)) enhancer = ImageEnhance.Color(temp) temp = enhancer.enhance(.3) print('baground size : ', temp.size) print('required size : ', width, height) w_dif = x - width h_dif = y - height print('width diff : ', x - width, 'height diff : ', y - height) temp2 = temp if w_dif < h_dif: new_width = int(x / (y / height)) print('new_width : ', new_width) temp2 = img.resize((new_width, height), Image.ANTIALIAS) else: new_height = int(y / (x / width)) print('new_height : ', new_height) temp2 = img.resize((width, new_height), Image.ANTIALIAS) #print('temp = ',temp.size) #print('temp2 = ',temp2.size) img_w, img_h = temp2.size print('after editing size of image : ', temp2.size) offset = ((width - img_w) // 2, (height - img_h) // 2) temp.paste(temp2, offset) return temp
def create_classifications(s3, folder, image_name, polygons, border, inside, outside, window_width, window_height): im = Image.open(folder + "/" + image_name) im = im.filter(ImageFilter.BoxBlur(1)) px = im.load() width, height = im.size dim = len(px[0, 0]) assert (dim == 3) increment = 5 # Don't need to use every pixel for i in range(0, height * width, increment): y = math.floor(i / width) x = i % width [r, g, b] = px[x, y] window = create_window(px, x, y, window_width, window_height, width, height) if len(polygons) == 0: write(outside, window, OUTSIDE) else: found = False for polygon in polygons: if on_border(polygon, x, y): write(border, window, BORDER) found = True break elif in_polygon(polygon, x, y): write(inside, window, INSIDE) found = True if not found: write(outside, window, OUTSIDE)
def test_sanity(self): def filter(filter): for mode in ["L", "RGB", "CMYK"]: im = hopper(mode) out = im.filter(filter) self.assertEqual(out.mode, im.mode) self.assertEqual(out.size, im.size) filter(ImageFilter.BLUR) filter(ImageFilter.CONTOUR) filter(ImageFilter.DETAIL) filter(ImageFilter.EDGE_ENHANCE) filter(ImageFilter.EDGE_ENHANCE_MORE) filter(ImageFilter.EMBOSS) filter(ImageFilter.FIND_EDGES) filter(ImageFilter.SMOOTH) filter(ImageFilter.SMOOTH_MORE) filter(ImageFilter.SHARPEN) filter(ImageFilter.MaxFilter) filter(ImageFilter.MedianFilter) filter(ImageFilter.MinFilter) filter(ImageFilter.ModeFilter) filter(ImageFilter.GaussianBlur) filter(ImageFilter.GaussianBlur(5)) filter(ImageFilter.BoxBlur(5)) filter(ImageFilter.UnsharpMask) filter(ImageFilter.UnsharpMask(10)) self.assertRaises(TypeError, filter, "hello")
def add_random_alpha_variation(self, image: Image.Image) -> Image.Image: """ Randomly changes the alpha value of the image by applying a blur and max filter to a randomized array and setting the result array as new alpha channel # Arguments image: Image. the image to augment # Returns Image. the augmented image """ if image.mode == "L": # skip mask images return image img_array = np.array(image.convert('RGBA')) rand_alpha_array = np.random.randint(0, 255, img_array.shape[:2]) rand_alpha_img = Image.fromarray(rand_alpha_array, mode='L') rand_alpha_img = rand_alpha_img.filter(ImageFilter.MaxFilter(self.max_filter_size))\ .filter(ImageFilter.BoxBlur(self.blur_size)) new_alpha_array = np.rint(img_array[:, :, 3] * (np.asarray(rand_alpha_img) / 255)) img_array[:, :, 3] = new_alpha_array return Image.fromarray(img_array, mode='RGBA')
def test_sanity(): def apply_filter(filter_to_apply): for mode in ["L", "RGB", "CMYK"]: im = hopper(mode) out = im.filter(filter_to_apply) assert out.mode == im.mode assert out.size == im.size apply_filter(ImageFilter.BLUR) apply_filter(ImageFilter.CONTOUR) apply_filter(ImageFilter.DETAIL) apply_filter(ImageFilter.EDGE_ENHANCE) apply_filter(ImageFilter.EDGE_ENHANCE_MORE) apply_filter(ImageFilter.EMBOSS) apply_filter(ImageFilter.FIND_EDGES) apply_filter(ImageFilter.SMOOTH) apply_filter(ImageFilter.SMOOTH_MORE) apply_filter(ImageFilter.SHARPEN) apply_filter(ImageFilter.MaxFilter) apply_filter(ImageFilter.MedianFilter) apply_filter(ImageFilter.MinFilter) apply_filter(ImageFilter.ModeFilter) apply_filter(ImageFilter.GaussianBlur) apply_filter(ImageFilter.GaussianBlur(5)) apply_filter(ImageFilter.BoxBlur(5)) apply_filter(ImageFilter.UnsharpMask) apply_filter(ImageFilter.UnsharpMask(10)) with pytest.raises(TypeError): apply_filter("hello")
def output_mobile_images(image_file, destination, percent_viewport, quality): widths = [math.ceil(w * (percent_viewport / 100.0)) \ for w in MOBILE_FULL_WIDTHS_TO_TARGET] with Image.open(image_file) as image: ratio = image.height / float(image.width) for width in widths: resized = image.resize((width, round(width * ratio)), resample=Image.LANCZOS) # For example: some/path/mansfield.jpg => mansfield_1440.jpg filename = os.path.splitext(os.path.basename(image_file))[0] + \ "-" + str(width) + ".jpg" save_to = os.path.join(destination, filename) resized.save(save_to, "jpeg", quality=quality, optimize=True) # Save a placeholder for lazy loading images. placeholder_width = widths[-1] // 2 resized_for_placeholder = image.resize( (placeholder_width, round(placeholder_width * ratio)), resample=Image.LANCZOS) placeholder = resized_for_placeholder.filter(ImageFilter.BoxBlur(3)) filename = os.path.splitext(os.path.basename(image_file))[0] + \ "-placeholder.jpg" save_to = os.path.join(destination, filename) placeholder.save(save_to, "jpeg", quality=quality, optimize=True)
def save_bblur(): if request.method == "POST": image_src = 'static/uploads/img.png' im = Image.open(image_src) radius = int(request.get_data()) im = im.filter(ImageFilter.BoxBlur(radius)) im.save('static/uploads/img.png')
def show_box_blur(filename, r=1): original = Image.open(os.path.join(INPUT_DIR, filename)) filtered = original.filter(ImageFilter.BoxBlur(r)) #show image side-by-side show_horizontal(original, filtered) filtered.save( os.path.join(OUTPUT_DIR, '{}_boxblur_{}.jpg'.format(filename[:filename.index('.' ), r] ) ) )
def addBlur(self, pic: Image.Image): if self.settings["GaussianBlurRadius"]: pic = pic.filter( ImageFilter.GaussianBlur(self.settings["GaussianBlurRadius"])) if self.settings["BoxBlurRadiux"]: pic = pic.filter( ImageFilter.BoxBlur(self.settings["BoxBlurRadiux"])) return pic
def NF_instance(parameter_blur, parameter_size): boxImage = OriImage.filter(ImageFilter.BoxBlur(parameter_blur)) resizedImage = boxImage.resize( [int(parameter_size * s) for s in boxImage.size]) mode = resizedImage.mode data = resizedImage.tobytes() size = resizedImage.size return data, size, mode
def blur_and_print_image(image_full, image_name, output_path): file, ext = os.path.splitext(image_full) abrev_name, ext = os.path.splitext(image_name) image = PI.open(image_full) blur_ammount = random.randint(20, 60) image_blurred = image.filter(ImageFilter.BoxBlur(blur_ammount)) image_blurred_final = image_blurred.filter(ImageFilter.DETAIL) print('File Made: ' + output_path + '/' + abrev_name + ".jpeg") image_blurred_final.save(output_path + '/' + abrev_name + ".jpeg", "JPEG")
def test_box_filter(self): for radius in range(6): pil_image = self.load_image(self.backends["PIL"]) pil_image = pil_image.filter(ImageFilter.BoxBlur(radius)) torchvision_image = self.load_image(self.backends["torchvision"]) torchvision_image = tif.box_blur(torchvision_image, radius) self.assertImagesAlmostEqual(pil_image, torchvision_image)
def vhstext(image, pos=(0, 0), text="PLAY", fill=0xffffff, shadow=None, shadowradius=3, shadowfactor=5.0, size=12, align=C): image = image.convert("RGBA") fnt = font.font_variant(size=size) text_layer = Image.new("RGBA", image.size) shadow_layer = text_layer.copy() text_draw = ImageDraw.Draw(text_layer) if isinstance(fill, int): fill = "#%.6x" % fill if isinstance(shadow, int): shadow = "#%.6x" % shadow text_w, text_h = text_draw.textsize(text, font=fnt) if align == L: real_pos = (pos[0], pos[1]) if align == C: real_pos = (pos[0] - text_w // 2, pos[1]) if align == R: real_pos = (pos[0] - text_w, pos[1]) text_draw.multiline_text(real_pos, text, fill=fill, font=fnt, align=align) if shadow is not None: radius = shadowradius fil = ImageFilter.BoxBlur(radius) shadow_bbox = (text_w + radius * 2, text_h + radius * 2) if align == L: outpos = (pos[0] - radius, pos[1] - radius) if align == C: outpos = (pos[0] - shadow_bbox[0] // 2, pos[1] - radius) if align == R: outpos = (pos[0] - shadow_bbox[0] + radius, pos[1] - radius) shadow_box = Image.new("RGBA", shadow_bbox) shadow_draw = ImageDraw.Draw(shadow_box) shadow_draw.multiline_text((radius, radius), text, fill=shadow, font=fnt, align=align) shadow_box = shadow_box.filter(fil) chans = list(shadow_box.split()) chans[3] = ImageEnhance.Brightness(chans[3]).enhance(shadowfactor) shadow_box = Image.merge("RGBA", chans) shadow_layer.paste(shadow_box, outpos) text_with_shadow = Image.alpha_composite(shadow_layer, text_layer) image = Image.alpha_composite(image, text_with_shadow) return image.convert("RGB")
def do_work(parsed_image: dict, image_filename='image.jpg', metadata_filename='meta.json', path=Path(Path.home(), Path('.bing-desk'))): """Does the work""" image_path = Path(path, Path(image_filename)) split_image_path = os.path.splitext(image_path) if BLUR and path.is_dir(): blurred_image_path = Path(split_image_path[0] + '_blur' + split_image_path[1]) if blurred_image_path.is_file(): set_wallpaper(blurred_image_path) if HIRES: image = get_high_res(parsed_image) else: image = Image.open(BytesIO(r.get(parsed_image['image']).content)) if not isinstance(path, Path): path = Path(path) if not path.is_dir(): path.mkdir(parents=True, exist_ok=True) if WINDOWS: win.kernel32.SetFileAttributesW(str(path), 0x02) if BLUR: # Excessive, but I like it blurred_image = image.filter(ImageFilter.BoxBlur(radius=4)).filter( ImageFilter.BoxBlur(radius=4)) blurred_image.save(str(blurred_image_path)) set_wallpaper(blurred_image_path) time.sleep(.5) image.save(str(image_path)) metadata_path = Path(path, Path(metadata_filename)) parsed_image.update({'fetched': str(datetime.now())}) with metadata_path.open('wb') as file: file.write(json.dumps(parsed_image, ensure_ascii=False).encode('utf8')) set_wallpaper(image_path) return image_path, metadata_path
def glowFx(image, radius=0, brt=1.5): if radius > 0: base = image.copy() image = image.filter(ImageFilter.BoxBlur(radius=radius)) enhancer = ImageEnhance.Brightness(image) image = enhancer.enhance(brt) base.paste(image, (0, 0), image) return base else: return image
def test_main(xvfb, fs_with_data): main() # load and convert to gray # reference img_ref = Image.open("data/sitting.png").convert('LA') # compute/render image img_result = Image.open("output.png").convert('LA') # using blur (box or gaussian) for reducing the antialiasing differentiation img_ref = img_ref.filter(ImageFilter.BoxBlur(radius=4)) img_result = img_result.filter(ImageFilter.BoxBlur(radius=4)) diff = ImageChops.difference(img_ref, img_result) # evaluate difference with some energy quantification # not very precise and/or accurate, but enough for utest purpose max_energy = compute_energy(img_ref) diff_energy = compute_energy(diff) coef_error = diff_energy / max_energy assert coef_error <= 1e-3
def blurImage(self): text = self.blurRadius.text() if text and text.isnumeric() and self.img_pixmap: radius = int(text) if radius == 0: self.img_pixmap = self.img_pixmap_orig else: img = Image.fromqpixmap(self.img_pixmap) img = img.filter(ImageFilter.BoxBlur(radius)) self.img_pixmap = img.toqpixmap() self.notifyImageChange()
def show_box_blur(filename, r=1): '''Aplica um filtro BoxBlur à imagem, exibe e salva o resultado''' original = Image.open(in_file(filename)) filtered = original.filter(ImageFilter.BoxBlur(r)) #Mostrar as imagens lado a lado show_horizontal(original, filtered) filtered.save( out_file('{}_boxblur_{}.jpg'.format(filename[:filename.index('.')], r)))
def face_gaussian_blur(image, image_cv, radius=20): face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') gray_image_cv = cv2.cvtColor(image_cv, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray_image_cv, 1.1, 4) new_image = image for (x, y, w, h) in faces: box = (x, y, x+w, y+h) cropped_image = image.crop(box) cropped_image = cropped_image.filter(ImageFilter.BoxBlur(radius)) new_image.paste(cropped_image, box) return new_image
def __call__(self, image): ''' Arguments: image (numpy array or PIL Image) Returns: image (numpy array or PIL Image) ''' img = Image.fromarray(image) img = img.filter(ImageFilter.BoxBlur(self.radius)) return (np.array(img))
def applyBlurPG(img_path,blur_radius,blur_type): sharpimg = load_imgRGB(img_path) blurredimg=[] if blur_type is 'avg': blurredimg = sharpimg.filter(ImageFilter.BoxBlur(radius=blur_radius)) elif blur_type is 'gaus': blurredimg = sharpimg.filter(ImageFilter.GaussianBlur(radius=blur_radius)) else: raise ValueError("Unknown blur type. Please check the blur options in applyBlurPG function") return 0 return sharpimg,blurredimg