Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-02-08 22:55:01 +01:00
parent c2436d8197
commit 334660d58f
2 changed files with 16 additions and 6 deletions
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6
detect.py
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@ -46,7 +46,7 @@ def detect(
color_list = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))] color_list = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]
for i, (path, img, img0) in enumerate(dataloader): for i, (path, img, img0) in enumerate(dataloader):
print('image %g/%g: %s' % (i + 1, len(dataloader), path)) print("%g/%g '%s': " % (i + 1, len(dataloader), path), end='')
t = time.time() t = time.time()
# Get detections # Get detections
@ -83,7 +83,7 @@ def detect(
for i in unique_classes: for i in unique_classes:
n = (detections[:, -1].cpu() == i).sum() n = (detections[:, -1].cpu() == i).sum()
print('%g %ss' % (n, classes[int(i)])) print('%g %ss' % (n, classes[int(i)]), end=', ')
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections: for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
# Rescale coordinates to original dimensions # Rescale coordinates to original dimensions
@ -110,7 +110,7 @@ def detect(
# Save generated image with detections # Save generated image with detections
cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img) cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img)
print('Done. (%.3fs)\n' % (time.time() - t)) print(' Done. (%.3fs)' % (time.time() - t))
if platform == 'darwin': # MacOS (local) if platform == 'darwin': # MacOS (local)
os.system('open ' + output) os.system('open ' + output)

16
utils/datasets.py
View File

@ -41,7 +41,7 @@ class load_images(): # for inference
assert img0 is not None, 'Failed to load ' + img_path assert img0 is not None, 'Failed to load ' + img_path
# Padded resize # Padded resize
img, _, _, _ = resize_square(img0, height=self.height, color=(127.5, 127.5, 127.5)) img, _, _, _ = letterbox(img0, height=self.height, color=(127.5, 127.5, 127.5))
# Normalize RGB # Normalize RGB
img = img[:, :, ::-1].transpose(2, 0, 1) img = img[:, :, ::-1].transpose(2, 0, 1)
@ -128,7 +128,7 @@ class load_images_and_labels(): # for training
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
h, w, _ = img.shape h, w, _ = img.shape
img, ratio, padw, padh = resize_square(img, height=height, color=(127.5, 127.5, 127.5)) img, ratio, padw, padh = letterbox(img, height=height, color=(127.5, 127.5, 127.5))
# Load labels # Load labels
if os.path.isfile(label_path): if os.path.isfile(label_path):
@ -189,7 +189,7 @@ class load_images_and_labels(): # for training
return self.nB # number of batches return self.nB # number of batches
def resize_square(img, height=416, color=(0, 0, 0)): # resize a rectangular image to a padded square def letterbox(img, height=416, color=(0, 0, 0)): # resize a rectangular image to a padded square
shape = img.shape[:2] # shape = [height, width] shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new ratio = float(height) / max(shape) # ratio = old / new
new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)] new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)]
@ -200,6 +200,16 @@ def resize_square(img, height=416, color=(0, 0, 0)): # resize a rectangular ima
img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA) # resized, no border img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA) # resized, no border
return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2 return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2
def letterbox_undo(img, height=416, color=(0, 0, 0)): # resize a rectangular image to a padded square
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new
new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)]
dw = height - new_shape[1] # width padding
dh = height - new_shape[0] # height padding
top, bottom = dh // 2, dh - (dh // 2)
left, right = dw // 2, dw - (dw // 2)
img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA) # resized, no border
return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2
def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2), def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
borderValue=(127.5, 127.5, 127.5)): borderValue=(127.5, 127.5, 127.5)):