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How to use crop_element method in toolium
Best Python code snippet using toolium_python
autoencoderInvertible.py
Source:autoencoderInvertible.py 
...124 return x[:, :, :, :, start:end]125 return Lambda(func)126 except IndexError:127 print("Sorry, your index is out of range.")128def crop_element(k: tuple, r) -> tuple:129 """130 **A function taking out specific values.**131 132 + param **k**: tuple object to be processed, type `tuple`.133 + param **r**: value to be removed, type `int, float, string, None`.134 + return **k2**: cropped tuple object, type `tuple`.135 """136 k2 = list(k)137 k2.remove(r)138 return tuple(k2)139def invertible_subspace_dimension2D(units: int):140 """141 **A helper function converting dimensions into 2D convolution shapes.**142 This functions works only for quadratic dimension size. It reshapes the data143 according to an embedding with the same dimension, represented by a `2D` array.144 + param **units**: , type `int`.145 + return **embedding**: , type `tuple`.146 """147 embedding = (int(math.sqrt(units)), int(math.sqrt(units)), 1)148 return embedding149def dense_group(150 _input: np.ndarray,151 units: int,152 alpha: float = 5.5,153 useBias: bool = True,154 kernelInitializer: str = "uniform",155 biasInitializer: str = "zeros",156 padding: None = None,157 filterPower: None = None,158 kernelSize: None = None,159):160 """161 **This group can be extended for deep learning models and is a sequence of dense layers.**162 The dense layer is used with a `LeakyRelu` activation function. After the activation163 function batch-normalization is performed on default, to take care of the covariate shift.164 + param **_input**: data from previous convolutional layer, type `np.ndarray`.165 + param **filterPower**: multiple of the filters per layer, type `int`.166 + param **alpha**: parameter for `LeakyRelu` activation function, default `5.5`, type `float`.167 + param **kernelSize**: size of the `2D` kernel, default `(2,2)`, type `tuple`.168 + param **kernelInitializer**: keras kernel initializer, default `uniform`, type `str`.169 + param **padding**: padding for convolution, default `same`, type `str`.170 + param **useBias**: whether or not to use the bias term throughout the network, type `bool`.171 + param **biasInitializer**: initializing distribution of the bias values, type `str`.172 + return **data**: processed data by neural layers, type `np.ndarray`.173 """174 _dense = Dense(175 units=units,176 use_bias=useBias,177 kernel_initializer= kernelInitializer,178 bias_initializer=biasInitializer,179 )(_input)180 _activ = LeakyReLU(alpha=alpha)(_dense)181 _norm = BatchNormalization()(_activ)182 return _norm183def convolutional_group(184 _input: np.ndarray,185 filterPower: int = 2,186 alpha: float = 5.5,187 useBias: bool = True,188 kernelSize: tuple = (2, 2),189 kernelInitializer: str = "uniform",190 biasInitializer: str = "zeros",191 padding: str = "same",192 units: None = None193):194 """195 **This group can be extended for deep learning models and is a sequence of convolutional layers.**196 The convolutions is a `2D`-convolution and uses a `LeakyRelu` activation function. After the activation197 function batch-normalization is performed on default, to take care of the covariate shift. The padding198 is set to `same`, to avoid difficulties with convolution.199 + param **_input**: data from previous convolutional layer, type `np.ndarray`.200 + param **filterPower**: multiple of the filters per layer, type `int`.201 + param **alpha**: parameter for `LeakyRelu` activation function, default `5.5`, type `float`.202 + param **kernelSize**: size of the `2D` kernel, default `(2,2)`, type `tuple`.203 + param **kernelInitializer**: keras kernel initializer, default `uniform`, type `str`.204 + param **padding**: padding for convolution, default `same`, type `str`.205 + param **useBias**: whether or not to use the bias term throughout the network, type `bool`.206 + param **biasInitializer**: initializing distribution of the bias values, type `str`.207 + return **data**: processed data by neural layers, type `np.ndarray`.208 """209 _conv = Conv2D(210 filterPower,211 kernelSize,212 kernel_initializer=kernelInitializer,213 use_bias=useBias,214 bias_initializer=biasInitializer,215 padding=padding,216 )(_input)217 _activ = LeakyReLU(alpha=alpha)(_conv)218 _norm = BatchNormalization()(_activ)219 return _norm220def group_loop(221 group: Callable,222 element: np.ndarray,223 units: int = None,224 filterPower: int = 2,225 kernelSize: tuple = (2,2),226 groupLayers: int = 1,227 kernelInitializer: str = "uniform",228 biasInitializer: str = "zeros",229 useBias: bool = True,230) -> np.ndarray:231 """232 **This callable is a loop over a group specification.**233 The group is a stacking of neural network layers into a deep learning system.234 This functino stacks a certain amount of dense layers. The Dense layers are kept235 equally dimensioned within the group, to respect its mathematical sense.236 + param **group**: a callable that sets up the neural architecture, type `Callable`.237 + param **element**: data, type `np.ndarray`.238 + param **groupLayers**: depth of the neural network, type `int`.239 + param **units**: units for dense groups, type `int`.240 + param **useBias**: whether or not to use the bias term throughout the network, type `bool`.241 + param **biasInitializer**: initializing distribution of the bias values, type `str`.242 + return **data**: processed data by neural network, type `np.ndarray`.243 """244 data = element245 for i in range(0, groupLayers+1):246 try:247 if i == (groupLayers):248 data = group(249 data,250 units=units,251 filterPower=1,252 kernelSize= kernelSize,253 kernelInitializer=kernelInitializer,254 biasInitializer=biasInitializer,255 useBias=useBias256 )257 else:258 data = group(259 data,260 units = units,261 filterPower=filterPower ** ((groupLayers) - i),262 kernelSize=kernelSize,263 kernelInitializer=kernelInitializer,264 biasInitializer=biasInitializer,265 useBias=useBias266 )267 except TypeError:268 exit("TypeError on your convolutional layer size.")269 return data270def dense_invertible_layer(271 data: np.ndarray,272 units: int = None,273 groupLayers: int = 1,274 alpha: float = 5.5,275 croppingNumber: int = 2,276 kernelInitializer: str = "uniform",277 biasInitializer: str = "zeros",278 useBias: bool = True,279 kernelSize: None = None,280 padding: None = None281) -> np.ndarray:282 """283 **Returns an invertible dense neural network layer.**284 This neural network layer learns invertible subspaces, parameterized by higher dimensional285 functions with a trivial invertibility. The higher dimensional functions are also neural286 subnetworks, trained during learning process.287 + param **data**: data from previous convolutional layer, type `np.ndarray`.288 + param **units**: units for dense groups, type `int`.289 + param **groupLayers**: depth of the neural network, type `int`.290 + param **alpha**: parameter for `LeakyRelu` activation function, default `5.5`, type `float`.291 + param **useBias**: whether or not to use the bias term throughout the network, type `bool`.292 + param **croppingFactor**: some quotient of the dimension, type `int`.293 + param **biasInitializer**: initializing distribution of the bias values, type `str`.294 + param **kernelInitializer**: keras kernel initializer, default `uniform`, type `str`.295 + return **data**: processed data, type `np.ndarray`.296 """297 data_shape = crop_element(K.int_shape(data), None)298 crop = int(data_shape[0] / croppingNumber)299 def _splitLayer(tensor):300 if (2 * crop) == data_shape[0]: 301 return tf.split(tensor, [crop,crop], axis=1)302 else:303 return tf.split(tensor, [crop,crop+1], axis=1)304 partVectorOne, partVectorTwo = Lambda(_splitLayer)(data)305 firstGroup = group_loop(306 group=dense_group,307 units = crop,308 groupLayers=groupLayers,309 element=partVectorTwo,310 kernelInitializer=kernelInitializer,311 useBias=useBias,312 biasInitializer=biasInitializer313 )314 firstMultiplication = multiply(315 [partVectorOne, firstGroup]316 )317 firstAddition = add([firstMultiplication,firstGroup])318 # Inverse process of learning.319 secondGroup = group_loop(320 group=dense_group,321 units=crop,322 groupLayers=groupLayers,323 element=firstAddition,324 kernelInitializer=kernelInitializer,325 useBias=useBias,326 biasInitializer=biasInitializer,327 )328 secondMultiplication = multiply(329 [partVectorTwo, firstGroup]330 )331 secondAddition = add(332 [secondMultiplication, secondGroup]333 )334 decoded_layer = Concatenate()([firstAddition, secondAddition])335 decoded_layer = Reshape(data_shape)(decoded_layer)336 return decoded_layer337def convolutional_invertible_layer(338 data: np.ndarray,339 groupLayers: int,340 alpha: float = 5.5,341 kernelSize: tuple = (2, 2),342 kernelInitializer: str = "uniform",343 filterPower: int = 2,344 croppingFactor: int = 2,345 useBias: bool = True,346 biasInitializer: str = "zeros",347) -> np.ndarray:348 """349 **Returns an invertible neural network layer.**350 This neural network layer learns invertible subspaces, parameterized by higher dimensional351 functions with a trivial invertibility. The higher dimensional functions are also neural352 subnetworks, trained during learning process.353 + param **data**: data from previous convolutional layer, type `np.ndarray`.354 + param **alpha**: parameter for `LeakyRelu` activation function, default `5.5`, type `float`.355 + param **groupLayers**: depth of the neural network, type `int`.356 + param **kernelSize**: size of the kernels, type `tuple`.357 + param **filterPower**: multiple of the filters per layer, type `int`.358 + param **croppingFactor**: should be a multiple of the strides length, type `int`.359 + param **useBias**: whether or not to use the bias term throughout the network, type `bool`.360 + param **biasInitializer**: initializing distribution of the bias values, type `str`.361 + return **data**: processed data, type `np.ndarray`.362 """363 data_shape = crop_element(K.int_shape(data), None)364 crop = int(data_shape[0] / croppingFactor)365 partVectorOne = Cropping2D(cropping=((0, 0), (0,crop)))(data)366 partVectorTwo = Cropping2D(cropping=((crop,0), (0, 0)))(data)367 # Storing the shapes for dynamic adaptation of parameters.368 partVectorOneShape = crop_element(K.int_shape(partVectorOne), None)369 partVectorTwoShape = crop_element(K.int_shape(partVectorTwo), None)370 if crop - ((groupLayers+1) * (kernelSize[0]-1)) <= 0:371 exit("The group layers will cause negative dimensions due to convolution in axis = 0.")372 elif crop - ((groupLayers+1) * (kernelSize[0]-1)) <= 0:373 exit("The group layers will cause negative dimensions due to convolution in axis = 1.")374 # Compute the dimension reduction caused by the convolutional layer.375 sizeReductionPerDimension = []376 for i in range(0, len(kernelSize)):377 sizeReductionPerDimension.append((kernelSize[i] - 1) * groupLayers)378 # First function for invertibility.379 dataDimension = np.prod(np.array(partVectorOneShape))380 dataReduced = dataDimension - np.prod(np.array(sizeReductionPerDimension))381 firstGroup = group_loop(382 group=convolutional_group,383 groupLayers=groupLayers,384 element=partVectorTwo,385 filterPower=filterPower,386 kernelSize=kernelSize,387 kernelInitializer=kernelInitializer,388 useBias=useBias,389 biasInitializer=biasInitializer,390 )391 firstMultiplication = multiply(392 [partVectorOne, Reshape(partVectorOneShape)(firstGroup)]393 )394 firstAddition = add([firstMultiplication, Reshape(partVectorOneShape)(firstGroup)])395 # Inverse process of learning.396 secondGroup = group_loop(397 group=convolutional_group,398 groupLayers=groupLayers,399 element=firstAddition,400 filterPower=filterPower,401 kernelSize=kernelSize,402 kernelInitializer=kernelInitializer,403 useBias=useBias,404 biasInitializer=biasInitializer,405 )406 secondMultiplication = multiply(407 [partVectorTwo, Reshape(partVectorTwoShape)(firstGroup)]408 )409 secondAddition = add(410 [secondMultiplication, Reshape(partVectorTwoShape)(secondGroup)]411 )412 # Storing the shapes for dynamic adaptation of parameters.413 outOneShape = crop_element(K.int_shape(firstAddition), None)414 outTwoShape = crop_element(K.int_shape(secondAddition), None)415 def _dot(tensors):416 return K.stack(tensors, axis = 1)417 decoded_layer = Lambda(_dot)([firstAddition, Reshape(outOneShape)(secondAddition)])418 decoded_layer = Reshape(data_shape)(decoded_layer)419 return decoded_layer420def dense_invertible_subspace_autoencoder(421 data: np.ndarray,422 units: int,423 invertibleLayers: int,424 alpha: float = 5.5,425 kernelInitializer: str = "uniform",426 biasInitializer: str = "zeros",427 groupLayers: int = 1,428 useBias: bool = True,429):430 """431 **A function returning an invertible dense autoencoder model.**432 This is a fully invertible dense autoencoder. The amount of hidden layers is specified through the433 `groupLayers` argument. Please use only an even embedding dimension, such that no problem with tensor434 transformations occure.435 + param **data**: data, type `np.ndarray`.436 + param **units**: units for dense layers, type `int`.437 + param **invertibleLayers**: amout of invertible layers in the middle of the network, type `int`.438 + param **alpha**: parameter for `LeakyRelu` activation function, default `5.5`, type `float`.439 + param **kernelInitializer**: initializing distribution of the kernel values, type `str`.440 + param **groupLayers**: depth of the neural network (counts from `0`), type `int`.441 + param **useBias**: whether or not to use the bias term throughout the network, type `bool`.442 + param **biasInitializer**: initializing distribution of the bias values, type `str`.443 + return **output**: an output layer for keras neural networks, type `np.ndarray`.444 """445 firstLayerFlattened = Flatten()(data)446 dataDimension = crop_element(K.int_shape(firstLayerFlattened), None)[0]447 data_shape = crop_element(K.int_shape(data), None)448 firstLayer = Dense(449 units=units,450 use_bias=True,451 kernel_initializer="glorot_uniform",452 bias_initializer="zeros",453 )(firstLayerFlattened)454 firstActivation = LeakyReLU(alpha=alpha)(firstLayer)455 firstNorm = BatchNormalization()(firstActivation)456 for i in range(0, invertibleLayers):457 firstNorm = add(458 [459 firstNorm,460 dense_invertible_layer(461 data=firstNorm,462 units=units,463 alpha=alpha,464 kernelInitializer=kernelInitializer,465 groupLayers=groupLayers,466 useBias=useBias,467 biasInitializer=biasInitializer,468 ),469 ]470 )471 lastLayer = Dense(472 units=dataDimension,473 use_bias=True,474 kernel_initializer="glorot_uniform",475 bias_initializer="zeros",476 )(firstNorm)477 lastActivation = LeakyReLU(alpha=alpha)(lastLayer)478 lastNorm = BatchNormalization()(lastActivation)479 output = Reshape(data_shape)(lastNorm)480 return output481def convolutional_invertible_subspace_autoencoder(482 data: np.ndarray,483 units: int,484 invertibleLayers: int,485 alpha: float = 5.5,486 kernelSize: tuple = (2, 2),487 kernelInitializer: str = "uniform",488 groupLayers: int = 1,489 filterPower: int = 2,490 useBias: bool = True,491 biasInitializer: str = "zeros",492):493 """494 **A function returning an invertible convolutional autoencoder model.**495 This model works only with a quadratic number as units. The convolutional embedding496 dimension in `2D` is determined, for the quadratic matrix, as the square root of the497 respective dimension of the dense layer. This module is for testing purposes and not498 meant to be part of a productive environment.499 + param **data**: data, type `np.ndarray`.500 + param **units**: projection dim. into lower dim. by dense layer, type `int`.501 + param **invertibleLayers**: amout of invertible layers in the middle of the network, type `int`.502 + param **alpha**: parameter for `LeakyRelu` activation function, default `5.5`, type `float`.503 + param **kernelSize**: size of the kernels, type `tuple`.504 + param **kernelInitializer**: initializing distribution of the kernel values, type `str`.505 + param **groupLayers**: depth of the neural network (counts from `0`), type `int`.506 + param **filterPower**: multiple of the filters per layer, type `int`.507 + param **useBias**: whether or not to use the bias term throughout the network, type `bool`.508 + param **biasInitializer**: initializing distribution of the bias values, type `str`.509 + param **filterPower**: an integer factor for each convolutional layer, type `int`.510 + return **output**: an output layer for keras neural networks, type `np.ndarray`.511 """512 firstLayerFlattened = Flatten()(data)513 dataDimension = crop_element(K.int_shape(firstLayerFlattened), None)[0]514 data_shape = crop_element(K.int_shape(data), None)515 firstLayer = Dense(516 units=units,517 use_bias=True,518 kernel_initializer=kernelInitializer,519 bias_initializer=biasInitializer,520 )(firstLayerFlattened)521 firstActivation = LeakyReLU(alpha=alpha)(firstLayer)522 firstNorm = BatchNormalization()(firstActivation)523 firstShape = crop_element(K.int_shape(firstLayerFlattened), None)524 shape = invertible_subspace_dimension2D(units)525 reshapedLayer = Reshape((shape))(firstNorm)526 for i in range(0, invertibleLayers):527 reshapedLayer = add(528 [529 reshapedLayer,530 convolutional_invertible_layer(531 data=reshapedLayer,532 alpha=alpha,533 kernelSize=kernelSize,534 kernelInitializer=kernelInitializer,535 groupLayers=groupLayers,536 filterPower=filterPower,537 useBias=useBias,...
visual_test.py
Source:visual_test.py
...107 img = Image.open(BytesIO(self.driver_wrapper.driver.get_screenshot_as_png()))108 img = self.remove_scrolls(img)109 img = self.mobile_resize(img)110 img = self.exclude_elements(img, exclude_web_elements)111 img = self.crop_element(img, web_element)112 img.save(output_file)113 DriverManager.visual_number += 1114 # Determine whether we should save the baseline image115 if self.save_baseline or not os.path.exists(baseline_file):116 # Copy screenshot to baseline117 shutil.copyfile(output_file, baseline_file)118 if self.driver_wrapper.config.getboolean_optional('VisualTests', 'complete_report'):119 self._add_result_to_report('baseline', report_name, output_file, None, 'Screenshot added to baseline')120 self.logger.debug("Visual screenshot '%s' saved in visualtests/baseline folder", filename)121 else:122 # Compare the screenshots123 self.compare_files(report_name, output_file, baseline_file, threshold)124 def get_scrolls_size(self):125 scroll_x = 0126 scroll_y = 0127 if (self.driver_wrapper.config.get('Driver', 'type').split('-')[0] in ['chrome', 'iexplore'] and128 not self.driver_wrapper.is_mobile_test()):129 scroll_height = self.driver_wrapper.driver.execute_script("return document.body.scrollHeight")130 scroll_width = self.driver_wrapper.driver.execute_script("return document.body.scrollWidth")131 window_height = self.driver_wrapper.driver.execute_script("return window.innerHeight")132 window_width = self.driver_wrapper.driver.execute_script("return window.innerWidth")133 scroll_size = 21 if self.driver_wrapper.config.get('Driver', 'type').split('-')[0] == 'iexplore' else 17134 scroll_x = scroll_size if scroll_width > window_width else 0135 scroll_y = scroll_size if scroll_height > window_height else 0136 return {'x': scroll_x, 'y': scroll_y}137 def remove_scrolls(self, img):138 scrolls_size = self.get_scrolls_size()139 if scrolls_size['x'] > 0 or scrolls_size['y'] > 0:140 new_image_width = img.size[0] - scrolls_size['y']141 new_image_height = img.size[1] - scrolls_size['x']142 img = img.crop((0, 0, new_image_width, new_image_height))143 return img144 def mobile_resize(self, img):145 if self.driver_wrapper.is_ios_test() or self.driver_wrapper.is_android_web_test():146 scale = img.size[0] / self.utils.get_window_size()['width']147 if scale != 1:148 new_image_size = (int(img.size[0] / scale), int(img.size[1] / scale))149 img = img.resize(new_image_size, Image.ANTIALIAS)150 return img151 def get_element_box(self, web_element):152 if not self.driver_wrapper.is_mobile_test():153 scroll_x = self.driver_wrapper.driver.execute_script("return window.pageXOffset")154 scroll_x = scroll_x if scroll_x else 0155 scroll_y = self.driver_wrapper.driver.execute_script("return window.pageYOffset")156 scroll_y = scroll_y if scroll_y else 0157 offset_x = -scroll_x158 offset_y = -scroll_y159 else:160 offset_x = 0161 offset_y = self.utils.get_safari_navigation_bar_height()162 location = web_element.location163 size = web_element.size164 return (int(location['x']) + offset_x, int(location['y'] + offset_y),165 int(location['x'] + offset_x + size['width']), int(location['y'] + offset_y + size['height']))166 def crop_element(self, img, web_element):167 if web_element:168 element_box = self.get_element_box(web_element)169 # Reduce element box if it is greater than image size170 element_max_x = img.size[0] if element_box[2] > img.size[0] else element_box[2]171 element_max_y = img.size[1] if element_box[3] > img.size[1] else element_box[3]172 element_box = (element_box[0], element_box[1], element_max_x, element_max_y)173 img = img.crop(element_box)174 return img175 def exclude_elements(self, img, web_elements):176 if web_elements and len(web_elements) > 0:177 img = img.convert("RGBA")178 pixel_data = img.load()179 for web_element in web_elements:180 element_box = self.get_element_box(web_element)...
process_dataset.py
Source:process_dataset.py
...37 ymin += -min(0, ymin)38 xmax += -min(0, xmin)39 xmin += -min(0, xmin)40 return image, xmin, xmax, ymin, ymax41def crop_element(image_path, output_path):42 """Crop off the whitespace in UI sketches to capture only the UI element's sketch43 Arguments:44 image_path {string} -- File path of input image file45 output_path {string} -- File path to store the cropped image46 """47 original_image = cv2.imread(image_path)48 # morph close kernel size is 10% of image width & crop offset is 1% of width49 height, width, _ = original_image.shape50 kernel_size = int(width * 0.1)51 offset = int(width * 0.01)52 # Convert original image to grayscale for further processing53 grayscale_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)54 # Copy the grayscale image for later reuse55 image = grayscale_image.copy()56 # Threshold to convert image black/white - remove all grays & colors57 _, thresh_binary_image = cv2.threshold(grayscale_image, 220, 255, cv2.THRESH_BINARY)58 # Apply gaussian blur on thresh binary image to remove noise59 denoised_image = cv2.GaussianBlur(thresh_binary_image, (7, 7), 0)60 # Find edges in the denoised image61 edged_image = cv2.Canny(denoised_image, 10, 250)62 # Close the edge detected image to form one combined element blob63 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))64 blob_image = cv2.morphologyEx(edged_image, cv2.MORPH_CLOSE, kernel)65 # Find all the contours66 (_, contours, _) = cv2.findContours(blob_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)67 # Pick only the largest contour based on area, crop it and save it in processed folder68 if contours:69 contour = max(contours, key=cv2.contourArea)70 xmin, ymin, width, height = cv2.boundingRect(contour)71 bndbox = (xmin - offset, ymin - offset, xmin + width + offset, ymin + height + offset)72 # Identify the regions of interest and save them73 roi = crop_image(image, bndbox)74 cv2.imwrite(output_path, roi)75if __name__ == "__main__":76 PARSER = argparse.ArgumentParser(description="Automatically crop labelled UI sketch elements.")77 PARSER.add_argument(78 "-i",79 "--input",80 required=True,81 dest="input_folder",82 help="Input folder containing labelled folders of UI sketches",83 )84 PARSER.add_argument(85 "-o",86 "--output",87 required=True,88 dest="output_folder",89 help="Output folder of cropped images",90 )91 ARGS = PARSER.parse_args()92 INPUT_FOLDER = ARGS.input_folder93 INPUT_FOLDER = INPUT_FOLDER.strip(os.sep)94 print(f"Input folder: {INPUT_FOLDER}")95 OUTPUT_FOLDER = ARGS.output_folder96 OUTPUT_FOLDER = OUTPUT_FOLDER.strip(os.sep)97 print(f"Output folder: {OUTPUT_FOLDER}")98 print("Creating folder structure similar to input folder in output folder.....")99 for folder in os.listdir(INPUT_FOLDER):100 if os.path.isdir(os.path.join(INPUT_FOLDER, folder)):101 os.makedirs(os.path.join(OUTPUT_FOLDER, folder), exist_ok=True)102 print("File structure cloned in output folder.")103 FILES = glob.glob(f"{INPUT_FOLDER}/**/*.jpg")104 print(f"Cropping {len(FILES)} images....")105 for image_file in FILES:106 output_file = image_file.replace(INPUT_FOLDER, OUTPUT_FOLDER)107 crop_element(image_file, output_file)...
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