Sourcery refactored master branch #1
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| for eachPrediction, eachProbability in zip(predictions, probabilities): | ||
| print(eachPrediction + " : " + eachProbability) No newline at end of file | ||
| print(f"{eachPrediction} : {eachProbability}") No newline at end of file |
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Lines 15-15 refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
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| predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "1.jpg"), result_count=5 ) | ||
| for eachPrediction, eachProbability in zip(predictions, probabilities): | ||
| print(eachPrediction + " : " + eachProbability) No newline at end of file | ||
| print(f"{eachPrediction} : {eachProbability}") No newline at end of file |
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Lines 13-13 refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
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| all_images_array = [] | ||
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| all_files = os.listdir(execution_path) | ||
| for each_file in all_files: | ||
| if(each_file.endswith(".jpg") or each_file.endswith(".png")): | ||
| all_images_array.append(each_file) | ||
| all_images_array = [ | ||
| each_file | ||
| for each_file in all_files | ||
| if (each_file.endswith(".jpg") or each_file.endswith(".png")) | ||
| ] | ||
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| results_array = multiple_prediction.predictMultipleImages(all_images_array, result_count_per_image=5) | ||
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| for each_result in results_array: | ||
| predictions, percentage_probabilities = each_result["predictions"], each_result["percentage_probabilities"] | ||
| for index in range(len(predictions)): | ||
| print(predictions[index] + " : " + percentage_probabilities[index]) | ||
| print(f"{predictions[index]} : {percentage_probabilities[index]}") |
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Lines 11-23 refactored with the following changes:
- Move assignment closer to its usage within a block (
move-assign-in-block) - Convert for loop into list comprehension (
list-comprehension) - Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
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| if(self.__modelLoaded == False): | ||
| if (self.__modelLoaded == False): | ||
| raise ValueError("You must call the loadModel() function before making object detection.") | ||
| elif(self.__modelLoaded == True): | ||
| elif self.__modelLoaded == True: |
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Function ObjectDetection.detectObjectsFromImage refactored with the following changes:
- Use f-string instead of string concatenation [×3] (
use-fstring-for-concatenation) - Merge nested if conditions (
merge-nested-ifs) - Merge dictionary assignment with declaration [×2] (
merge-dict-assign) - Remove unnecessary calls to
str()from formatted values in f-strings (remove-str-from-fstring) - Merge else clause's nested if statement into elif (
merge-else-if-into-elif) - Simplify conditional into switch-like form [×19] (
switch)
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| elif (self.__modelLoaded == True): | ||
| elif self.__modelLoaded == True: |
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Function ObjectDetection.detectCustomObjectsFromImage refactored with the following changes:
- Use f-string instead of string concatenation [×3] (
use-fstring-for-concatenation) - Merge nested if conditions (
merge-nested-ifs) - Merge dictionary assignment with declaration [×2] (
merge-dict-assign) - Remove unnecessary calls to
str()from formatted values in f-strings (remove-str-from-fstring) - Merge else clause's nested if statement into elif (
merge-else-if-into-elif) - Simplify conditional into switch-like form [×19] (
switch)
| else: | ||
| axis = 1 | ||
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| axis = 3 if keras.backend.image_data_format() == "channels_last" else 1 |
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Function TimeDistributedResNet refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp) - Swap if/else branches (
swap-if-else-branches) - Remove unnecessary else after guard condition (
remove-unnecessary-else)
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| # set bias to -log((1 - p)/p) for foregound | ||
| result = np.ones(shape, dtype=dtype) * -math.log((1 - self.probability) / self.probability) | ||
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| return result | ||
| return np.ones(shape, dtype=dtype) * -math.log( | ||
| (1 - self.probability) / self.probability | ||
| ) |
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Function PriorProbability.__call__ refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
This removes the following comments ( why? ):
# set bias to -log((1 - p)/p) for foregound
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| pred_boxes = keras.backend.stack([pred_boxes_x1, pred_boxes_y1, pred_boxes_x2, pred_boxes_y2], axis=2) | ||
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| return pred_boxes | ||
| return keras.backend.stack( | ||
| [pred_boxes_x1, pred_boxes_y1, pred_boxes_x2, pred_boxes_y2], axis=2 | ||
| ) |
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Function bbox_transform_inv refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
| from .tensorflow_backend import * | ||
| else: | ||
| raise ValueError("Unknown backend: " + str(_BACKEND)) | ||
| raise ValueError(f"Unknown backend: {str(_BACKEND)}") |
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Lines 19-19 refactored with the following changes:
- Use f-string instead of string concatenation (
use-fstring-for-concatenation)
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| if None not in input_shape[1:]: | ||
| total = np.prod(input_shape[1:3]) * self.num_anchors | ||
| return (input_shape[0], total, 4) | ||
| else: | ||
| if None in input_shape[1:]: | ||
| return (input_shape[0], None, 4) | ||
| total = np.prod(input_shape[1:3]) * self.num_anchors | ||
| return (input_shape[0], total, 4) |
Author
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Function Anchors.compute_output_shape refactored with the following changes:
- Swap if/else branches (
swap-if-else-branches) - Remove unnecessary else after guard condition (
remove-unnecessary-else)
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