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Comment updates on box coordinates (#852) 

* Update utils.py

Reusing function defined above

* Update utils.py

* Reverting change which break bbox coordinate computation

* Update utils.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Falak 2020-03-15 04:05:59 +05:30 committed by GitHub
parent a52c0abf8d
commit 666ba85ed3
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1 changed files with 12 additions and 14 deletions
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utils/utils.py
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@ -103,22 +103,22 @@ def weights_init_normal(m):
def xyxy2xywh(x): def xyxy2xywh(x):
# Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h] # Transform box coordinates from [x1, y1, x2, y2] (where xy1=top-left, xy2=bottom-right) to [x, y, w, h]
y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x) y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
y[:, 0] = (x[:, 0] + x[:, 2]) / 2 y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
y[:, 1] = (x[:, 1] + x[:, 3]) / 2 y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
y[:, 2] = x[:, 2] - x[:, 0] y[:, 2] = x[:, 2] - x[:, 0] # width
y[:, 3] = x[:, 3] - x[:, 1] y[:, 3] = x[:, 3] - x[:, 1] # height
return y return y
def xywh2xyxy(x): def xywh2xyxy(x):
# Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2] # Transform box coordinates from [x, y, w, h] to [x1, y1, x2, y2] (where xy1=top-left, xy2=bottom-right)
y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x) y = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2 y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2 y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
return y return y
@ -264,7 +264,7 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False):
if x1y1x2y2: # x1, y1, x2, y2 = box1 if x1y1x2y2: # x1, y1, x2, y2 = box1
b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
else: # x, y, w, h = box1 else: # transform from xywh to xyxy
b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
@ -670,8 +670,6 @@ def strip_optimizer(f='weights/last.pt'): # from utils.utils import *; strip_op
# Strip optimizer from *.pt files for lighter files (reduced by 2/3 size) # Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
x = torch.load(f, map_location=torch.device('cpu')) x = torch.load(f, map_location=torch.device('cpu'))
x['optimizer'] = None x['optimizer'] = None
# x['training_results'] = None # uncomment to create a backbone
# x['epoch'] = -1 # uncomment to create a backbone
torch.save(x, f) torch.save(x, f)
@ -1038,7 +1036,7 @@ def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_re
def plot_results(start=0, stop=0, bucket='', id=()): # from utils.utils import *; plot_results() def plot_results(start=0, stop=0, bucket='', id=()): # from utils.utils import *; plot_results()
# Plot training results files 'results*.txt' # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov3#training
fig, ax = plt.subplots(2, 5, figsize=(12, 6)) fig, ax = plt.subplots(2, 5, figsize=(12, 6))
ax = ax.ravel() ax = ax.ravel()
s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall', s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',