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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-15 16:27:13 +02:00
parent e73e247442
commit 4e5a00fb72
3 changed files with 13 additions and 5 deletions
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2
cfg/yolov3-spp-1cls.cfg
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@ -4,7 +4,7 @@
# subdivisions=1 # subdivisions=1
# Training # Training
batch=64 batch=64
subdivisions=64 subdivisions=32
width=608 width=608
height=608 height=608
channels=3 channels=3

3
test.py
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@ -88,6 +88,9 @@ def test(
stats.append(([], torch.Tensor(), torch.Tensor(), tcls)) stats.append(([], torch.Tensor(), torch.Tensor(), tcls))
continue continue
# Clip boxes to image bounds
clip_coords(pred, shapes[si])
# Append to text file # Append to text file
# with open('test.txt', 'a') as file: # with open('test.txt', 'a') as file:
# [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred] # [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred]

13
utils/utils.py
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@ -125,16 +125,21 @@ def xywh2xyxy(x):
def scale_coords(img1_shape, coords, img0_shape): def scale_coords(img1_shape, coords, img0_shape):
# Rescale coords1 (xyxy) from img1_shape to img0_shape # Rescale coords (xyxy) from img1_shape to img0_shape
gain = max(img1_shape) / max(img0_shape) # gain = old / new gain = max(img1_shape) / max(img0_shape) # gain = old / new
coords[:, [0, 2]] -= (img1_shape[1] - img0_shape[1] * gain) / 2 # x padding coords[:, [0, 2]] -= (img1_shape[1] - img0_shape[1] * gain) / 2 # x padding
coords[:, [1, 3]] -= (img1_shape[0] - img0_shape[0] * gain) / 2 # y padding coords[:, [1, 3]] -= (img1_shape[0] - img0_shape[0] * gain) / 2 # y padding
coords[:, :4] /= gain coords[:, :4] /= gain
coords[:, [0, 2]] = coords[:, [0, 2]].clamp(min=0, max=img0_shape[1]) # clip x clip_coords(coords, img0_shape)
coords[:, [1, 3]] = coords[:, [1, 3]].clamp(min=0, max=img0_shape[0]) # clip y
return coords return coords
def clip_coords(boxes, img_shape):
# Clip bounding xyxy bounding boxes to image shape (height, width)
boxes[:, [0, 2]] = boxes[:, [0, 2]].clamp(min=0, max=img_shape[1]) # clip x
boxes[:, [1, 3]] = boxes[:, [1, 3]].clamp(min=0, max=img_shape[0]) # clip y
def ap_per_class(tp, conf, pred_cls, target_cls): def ap_per_class(tp, conf, pred_cls, target_cls):
""" Compute the average precision, given the recall and precision curves. """ Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
@ -582,7 +587,7 @@ def kmeans_targets(path='./data/coco_64img.txt'): # from utils.utils import *;
# Plotting functions --------------------------------------------------------------------------------------------------- # Plotting functions ---------------------------------------------------------------------------------------------------
def plot_one_box(x, img, color=None, label=None, line_thickness=None): def plot_one_box(x, img, color=None, label=None, line_thickness=None):
# Plots one bounding box on image img # Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0]+img.shape[1])/2) + 1 # line thickness tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line thickness
color = color or [random.randint(0, 255) for _ in range(3)] color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl) cv2.rectangle(img, c1, c2, color, thickness=tl)