Sourcery refactored master branch #1
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| detector = FaceDetector(cascade_path) | ||
| face_boxes = detector.detect(gray, 1.2, 5) | ||
| print("{} face(s) found".format(len(face_boxes))) | ||
| print(f"{len(face_boxes)} face(s) found") |
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Lines 15-15 refactored with the following changes:
- Replace call to format with f-string (
use-fstring-for-formatting)
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| # Detect faces in the image | ||
| boxes = self.face_cascade.detectMultiScale(image, scale_factor, min_neighbors, flags=cv2.CASCADE_SCALE_IMAGE) | ||
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| # Return the bounding boxes | ||
| return boxes | ||
| return self.face_cascade.detectMultiScale( | ||
| image, scale_factor, min_neighbors, flags=cv2.CASCADE_SCALE_IMAGE | ||
| ) |
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Function FaceDetector.detect refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
This removes the following comments ( why? ):
# Return the bounding boxes
# Detect faces in the image
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| # Detect faces in the image | ||
| boxes = self.face_cascade.detectMultiScale(image, scale_factor, min_neighbors, flags=cv2.CASCADE_SCALE_IMAGE) | ||
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| # Return the bounding boxes | ||
| return boxes | ||
| return self.face_cascade.detectMultiScale( | ||
| image, scale_factor, min_neighbors, flags=cv2.CASCADE_SCALE_IMAGE | ||
| ) |
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Function FaceDetector.detect refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
This removes the following comments ( why? ):
# Return the bounding boxes
# Detect faces in the image
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| # Return the translated image | ||
| return shifted | ||
| return cv2.warpAffine(image, matrix, (image.shape[1], image.shape[0])) |
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Function translate refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
This removes the following comments ( why? ):
# Return the translated image
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| rotated = cv2.warpAffine(image, matrix, (w, h)) | ||
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| # Return the rotated image | ||
| return rotated | ||
| return cv2.warpAffine(image, matrix, (w, h)) |
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Function rotate refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
This removes the following comments ( why? ):
# Return the rotated image
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| # Resize the image | ||
| resized = cv2.resize(image, dim, interpolation=inter) | ||
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| # Return the resized image | ||
| return resized | ||
| return cv2.resize(image, dim, interpolation=inter) |
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Function resize refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
This removes the following comments ( why? ):
# Return the resized image
# Resize the image
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| image_paths = sorted(glob.glob(path_to_images + "/*.png")) | ||
| mask_paths = sorted(glob.glob(path_to_masks + "/*.png")) | ||
| image_paths = sorted(glob.glob(f"{path_to_images}/*.png")) | ||
| mask_paths = sorted(glob.glob(f"{path_to_masks}/*.png")) |
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Lines 16-72 refactored with the following changes:
- Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation) - Replace call to format with f-string (
use-fstring-for-formatting)
Comment on lines
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| min_matches = 40 | ||
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| # Initialize the database dictionary of covers | ||
| database = {} | ||
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| # Loop over the database | ||
| for l in csv.reader(open(database_path)): | ||
| # Update the database using the image ID as the key | ||
| database[l[0]] = l[1:] | ||
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| # If SIFT is to be used, then update the parameters | ||
| if use_sift: | ||
| min_matches = 50 | ||
| database = {l[0]: l[1:] for l in csv.reader(open(database_path))} | ||
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| min_matches = 50 if use_sift else 40 | ||
| # Initialize the cover descriptor and cover matcher | ||
| cover_descriptor = CoverDescriptor(use_sift=use_sift) | ||
| cover_matcher = CoverMatcher(cover_descriptor, glob.glob(covers_path + "/*.png"), ratio=ratio, min_matches=min_matches, | ||
| use_hamming=use_hamming) | ||
| cover_matcher = CoverMatcher( | ||
| cover_descriptor, | ||
| glob.glob(f"{covers_path}/*.png"), | ||
| ratio=ratio, | ||
| min_matches=min_matches, | ||
| use_hamming=use_hamming, | ||
| ) |
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Lines 18-35 refactored with the following changes:
- Move assignment closer to its usage within a block (
move-assign-in-block) - Convert for loop into dictionary comprehension (
dict-comprehension) - Use f-string instead of string concatenation (
use-fstring-for-concatenation) - Move setting of default value for variable into
elsebranch (introduce-default-else) - Replace if statement with if expression (
assign-if-exp)
This removes the following comments ( why? ):
# If SIFT is to be used, then update the parameters
# Update the database using the image ID as the key
# Loop over the database
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| # If matches were found, sort them | ||
| if len(results) > 0: | ||
| if results: |
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Function CoverMatcher.search refactored with the following changes:
- Simplify sequence length comparison (
simplify-len-comparison)
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| matches = [] | ||
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| # Loop over the raw matches | ||
| for match in raw_matches: | ||
| # Ensure the distance is within a certain ratio of each other | ||
| if len(match) == 2 and match[0].distance < match[1].distance * self.ratio: | ||
| matches.append((match[0].trainIdx, match[0].queryIdx)) | ||
| matches = [ | ||
| (match[0].trainIdx, match[0].queryIdx) | ||
| for match in raw_matches | ||
| if len(match) == 2 and match[0].distance < match[1].distance * self.ratio | ||
| ] |
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Function CoverMatcher.match refactored with the following changes:
- Convert for loop into list comprehension (
list-comprehension)
This removes the following comments ( why? ):
# Ensure the distance is within a certain ratio of each other
# Loop over the raw matches
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