From 6d25f9e6347f177b960d245ee699b8e1cfa97d46 Mon Sep 17 00:00:00 2001
From: klein179 <roel.klein@wur.nl>
Date: Thu, 19 Sep 2024 15:56:27 +0200
Subject: [PATCH] formatting
---
box_converter.py | 129 +++++++++++++++++++++++++++--------------------
1 file changed, 74 insertions(+), 55 deletions(-)
diff --git a/box_converter.py b/box_converter.py
index 728b422..b4ce970 100755
--- a/box_converter.py
+++ b/box_converter.py
@@ -4,21 +4,22 @@ import pycocotools.mask
def x1y1x2y2_2_x1x2y1y2(this_list):
- new_list = [0]*4
- new_list[0] = this_list[0]
- new_list[1] = this_list[2]
- new_list[2] = this_list[1]
- new_list[3] = this_list[3]
- return new_list
+ new_list = [0] * 4
+ new_list[0] = this_list[0]
+ new_list[1] = this_list[2]
+ new_list[2] = this_list[1]
+ new_list[3] = this_list[3]
+ return new_list
def x1x2y1y2_2_x1y1x2y2(this_list):
- new_list = [0]*4
- new_list[0] = this_list[0]
- new_list[1] = this_list[2]
- new_list[2] = this_list[1]
- new_list[3] = this_list[3]
- return new_list
+ new_list = [0] * 4
+ new_list[0] = this_list[0]
+ new_list[1] = this_list[2]
+ new_list[2] = this_list[1]
+ new_list[3] = this_list[3]
+ return new_list
+
def xywh2xyxy_yolo(x):
y = np.copy(x)
@@ -29,15 +30,15 @@ def xywh2xyxy_yolo(x):
return y
-
def xywh2xxyy(list_input):
this_list = list_input.copy()
- this_list[0] = list_input[0] # x1
- this_list[1] = list_input[0] + list_input[2] # x2
- this_list[2] = list_input[1] # y1
- this_list[3] = list_input[1] + list_input[3] # y2
+ this_list[0] = list_input[0] # x1
+ this_list[1] = list_input[0] + list_input[2] # x2
+ this_list[2] = list_input[1] # y1
+ this_list[3] = list_input[1] + list_input[3] # y2
return this_list
+
def xywh2xyxy(list_input):
this_list = list_input.copy()
this_list[2] = list_input[0] + list_input[2]
@@ -46,56 +47,74 @@ def xywh2xyxy(list_input):
this_list[1] = list_input[1]
return this_list
-def xyxy2xywh(list_input):
- x1 = int(list_input[0])
- y1 = int(list_input[1])
- w = abs(int(list_input[2]-list_input[0]))
- h = abs(int(list_input[3]-list_input[1]))
- return [x1,y1,w,h]
+
+def xyxy2xywh(listinput):
+ x1 = int(listinput[0])
+ y1 = int(listinput[0])
+ w = int(listinput[2] - listinput[0])
+ h = int(listinput[3] - listinput[1])
+ assert w > 0
+ assert h > 0
+ return [x1, y1, w, h]
def xywh_2_x1y1x2y2(list_input):
- x1 = int(list_input[0])
- y1 = int(list_input[1])
- x2 = int(x1 + list_input[2])
- y2 = int(y1 + list_input[3])
- return [x1,y1,x2,y2]
-
-
-def x1y1x2y2_2_cxcywh(list_input,height_img, width_img):
- # conversin of [x1,y1,x2,y2] to normalized yolo! [center_x,center_y,width,height]
- center_x = (list_input[0]+list_input[2])/2
- center_y = (list_input[1]+list_input[3])/2
- width = (list_input[2]-list_input[0])
- height = (list_input[3]-list_input[1])
- return [center_x/width_img,center_y/height_img,width/width_img,height/height_img]
-
-def x1y1wh_2_cxcywh(list_input,height_img, width_img):
- # conversin of [x1,y1,x2,y2] to normalized yolo! [center_x,center_y,width,height]
- center_x = (list_input[0]+list_input[2]*0.5)
- center_y = (list_input[1]+list_input[3]*0.5)
- width = list_input[2]
- height = list_input[3]
- return [center_x/width_img,center_y/height_img,width/width_img,height/height_img]
+ x1 = int(list_input[0])
+ y1 = int(list_input[1])
+ x2 = int(x1 + list_input[2])
+ y2 = int(y1 + list_input[3])
+ return [x1, y1, x2, y2]
+
+
+def x1y1x2y2_2_cxcywh(list_input, height_img, width_img):
+ # conversin of [x1,y1,x2,y2] to normalized yolo! [center_x,center_y,width,height]
+ center_x = (list_input[0] + list_input[2]) / 2
+ center_y = (list_input[1] + list_input[3]) / 2
+ width = list_input[2] - list_input[0]
+ height = list_input[3] - list_input[1]
+ return [
+ center_x / width_img,
+ center_y / height_img,
+ width / width_img,
+ height / height_img,
+ ]
+
+
+def x1y1wh_2_cxcywh(list_input, height_img, width_img):
+ # conversin of [x1,y1,x2,y2] to normalized yolo! [center_x,center_y,width,height]
+ center_x = list_input[0] + list_input[2] * 0.5
+ center_y = list_input[1] + list_input[3] * 0.5
+ width = list_input[2]
+ height = list_input[3]
+ return [
+ center_x / width_img,
+ center_y / height_img,
+ width / width_img,
+ height / height_img,
+ ]
+
def binary_mask_to_rle(binary_mask):
- ## input is numpy array with either 0 or 1 : np.array([0,0,1,1,1,0,1])
- ## output: rle={'counts': [2, 3, 1, 1], 'size': [7]}
- rle = {'counts': [], 'size': list(binary_mask.shape)}
- counts = rle.get('counts')
- for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order='F'))):
+ ## input is numpy array with either 0 or 1 : np.array([0,0,1,1,1,0,1])
+ ## output: rle={'counts': [2, 3, 1, 1], 'size': [7]}
+ rle = {"counts": [], "size": list(binary_mask.shape)}
+ counts = rle.get("counts")
+ for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order="F"))):
if i == 0 and value == 1:
counts.append(0)
counts.append(len(list(elements)))
return rle
+
def rle_to_binary_mask(rle):
- ## converts rle={'counts': [2, 3, 1, 1], 'size': [7]} to np.array([0,0,1,1,1,0,1])
- compressed_rle = pycocotools.mask.frPyObjects(rle, rle.get('size')[0], rle.get('size')[1])
- mask = pycocotools.mask.decode(compressed_rle)
- return mask
+ ## converts rle={'counts': [2, 3, 1, 1], 'size': [7]} to np.array([0,0,1,1,1,0,1])
+ compressed_rle = pycocotools.mask.frPyObjects(
+ rle, rle.get("size")[0], rle.get("size")[1]
+ )
+ mask = pycocotools.mask.decode(compressed_rle)
+ return mask
def box2centroid(b):
- centroid = [int((b[2]+b[0])/2), int((b[3]+b[1])/2)]
+ centroid = [int((b[2] + b[0]) / 2), int((b[3] + b[1]) / 2)]
return centroid
--
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