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YOLOv5 PyTorch Hub results.save() method retains filenames #2194

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merged 4 commits into from
Feb 12, 2021
Merged

YOLOv5 PyTorch Hub results.save() method retains filenames #2194

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435eec7
save results with name
dan0nchik Feb 11, 2021
7e779c3
debug
dan0nchik Feb 11, 2021
7869b42
save original imgs names
dan0nchik Feb 12, 2021
1e56487
Update common.py
glenn-jocher Feb 12, 2021
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20 changes: 12 additions & 8 deletions models/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -189,11 +189,10 @@ def forward(self, imgs, size=640, augment=False, profile=False):
# numpy: = np.zeros((720,1280,3)) # HWC
# torch: = torch.zeros(16,3,720,1280) # BCHW
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images

orig_imgs = imgs
p = next(self.model.parameters()) # for device and type
if isinstance(imgs, torch.Tensor): # torch
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference

# Pre-process
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
shape0, shape1 = [], [] # image and inference shapes
Expand Down Expand Up @@ -224,16 +223,17 @@ def forward(self, imgs, size=640, augment=False, profile=False):
for i in range(n):
scale_coords(shape1, y[i][:, :4], shape0[i])

return Detections(imgs, y, self.names)
return Detections(imgs, y, self.names, orig_imgs)


class Detections:
# detections class for YOLOv5 inference results
def __init__(self, imgs, pred, names=None):
def __init__(self, imgs, pred, names=None, orig_imgs=None):
super(Detections, self).__init__()
d = pred[0].device # device
gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
self.imgs = imgs # list of images as numpy arrays
self.orig_imgs = imgs # list of original names
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
self.names = names # class names
self.xyxy = pred # xyxy pixels
Expand All @@ -242,7 +242,7 @@ def __init__(self, imgs, pred, names=None):
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
self.n = len(self.pred)

def display(self, pprint=False, show=False, save=False, render=False, save_dir=''):
def display(self, pprint=False, show=False, save=False, render=False, save_dir='', save_orig_name=False):
colors = color_list()
for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
Expand All @@ -260,7 +260,11 @@ def display(self, pprint=False, show=False, save=False, render=False, save_dir='
if show:
img.show(f'image {i}') # show
if save:
f = Path(save_dir) / f'results{i}.jpg'
if save_orig_name:
f = Path(save_dir) / f'results{orig_imgs[i]}.jpg'
else:
f = Path(save_dir) / f'results_{i}.jpg'
print(f)
img.save(f) # save
print(f"{'Saving' * (i == 0)} {f},", end='' if i < self.n - 1 else ' done.\n')
if render:
Expand All @@ -272,8 +276,8 @@ def print(self):
def show(self):
self.display(show=True) # show results

def save(self, save_dir=''):
self.display(save=True, save_dir=save_dir) # save results
def save(self, save_dir='', save_orig_name=False):
self.display(save=True, save_dir=save_dir, save_orig_name=save_orig_name) # save results

def render(self):
self.display(render=True) # render results
Expand Down