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tensorflow2

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This commit is contained in:
Daniel Saavedra
2020-03-25 18:23:00 -03:00
parent 7010af8a58
commit 7cf0c577a1
25 changed files with 1016 additions and 309 deletions
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88
keras-yolo3-master/generator.py
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@@ -1,22 +1,22 @@
import cv2
import copy
import numpy as np
from keras.utils import Sequence
from tensorflow.keras.utils import Sequence
from utils.bbox import BoundBox, bbox_iou
from utils.image import apply_random_scale_and_crop, random_distort_image, random_flip, correct_bounding_boxes
class BatchGenerator(Sequence):
def __init__(self,
instances,
anchors,
labels,
def __init__(self,
instances,
anchors,
labels,
downsample=32, # ratio between network input's size and network output's size, 32 for YOLOv3
max_box_per_image=30,
batch_size=1,
min_net_size=320,
max_net_size=608,
shuffle=True,
jitter=True,
max_net_size=608,
shuffle=True,
jitter=True,
norm=None
):
self.instances = instances
@@ -30,13 +30,13 @@ class BatchGenerator(Sequence):
self.jitter = jitter
self.norm = norm
self.anchors = [BoundBox(0, 0, anchors[2*i], anchors[2*i+1]) for i in range(len(anchors)//2)]
self.net_h = 416
self.net_h = 416
self.net_w = 416
if shuffle: np.random.shuffle(self.instances)
def __len__(self):
return int(np.ceil(float(len(self.instances))/self.batch_size))
return int(np.ceil(float(len(self.instances))/self.batch_size))
def __getitem__(self, idx):
# get image input size, change every 10 batches
@@ -63,7 +63,7 @@ class BatchGenerator(Sequence):
dummy_yolo_1 = np.zeros((r_bound - l_bound, 1))
dummy_yolo_2 = np.zeros((r_bound - l_bound, 1))
dummy_yolo_3 = np.zeros((r_bound - l_bound, 1))
instance_count = 0
true_box_index = 0
@@ -71,18 +71,18 @@ class BatchGenerator(Sequence):
for train_instance in self.instances[l_bound:r_bound]:
# augment input image and fix object's position and size
img, all_objs = self._aug_image(train_instance, net_h, net_w)
for obj in all_objs:
# find the best anchor box for this object
max_anchor = None
max_anchor = None
max_index = -1
max_iou = -1
shifted_box = BoundBox(0,
shifted_box = BoundBox(0,
0,
obj['xmax']-obj['xmin'],
obj['ymax']-obj['ymin'])
obj['xmax']-obj['xmin'],
obj['ymax']-obj['ymin'])
for i in range(len(self.anchors)):
anchor = self.anchors[i]
iou = bbox_iou(shifted_box, anchor)
@@ -90,18 +90,18 @@ class BatchGenerator(Sequence):
if max_iou < iou:
max_anchor = anchor
max_index = i
max_iou = iou
max_iou = iou
# determine the yolo to be responsible for this bounding box
yolo = yolos[max_index//3]
grid_h, grid_w = yolo.shape[1:3]
# determine the position of the bounding box on the grid
center_x = .5*(obj['xmin'] + obj['xmax'])
center_x = center_x / float(net_w) * grid_w # sigma(t_x) + c_x
center_y = .5*(obj['ymin'] + obj['ymax'])
center_y = center_y / float(net_h) * grid_h # sigma(t_y) + c_y
# determine the sizes of the bounding box
w = np.log((obj['xmax'] - obj['xmin']) / float(max_anchor.xmax)) # t_w
h = np.log((obj['ymax'] - obj['ymin']) / float(max_anchor.ymax)) # t_h
@@ -109,7 +109,7 @@ class BatchGenerator(Sequence):
box = [center_x, center_y, w, h]
# determine the index of the label
obj_indx = self.labels.index(obj['name'])
obj_indx = self.labels.index(obj['name'])
# determine the location of the cell responsible for this object
grid_x = int(np.floor(center_x))
@@ -126,25 +126,25 @@ class BatchGenerator(Sequence):
t_batch[instance_count, 0, 0, 0, true_box_index] = true_box
true_box_index += 1
true_box_index = true_box_index % self.max_box_per_image
true_box_index = true_box_index % self.max_box_per_image
# assign input image to x_batch
if self.norm != None:
if self.norm != None:
x_batch[instance_count] = self.norm(img)
else:
# plot image and bounding boxes for sanity check
for obj in all_objs:
cv2.rectangle(img, (obj['xmin'],obj['ymin']), (obj['xmax'],obj['ymax']), (255,0,0), 3)
cv2.putText(img, obj['name'],
(obj['xmin']+2, obj['ymin']+12),
0, 1.2e-3 * img.shape[0],
cv2.putText(img, obj['name'],
(obj['xmin']+2, obj['ymin']+12),
0, 1.2e-3 * img.shape[0],
(0,255,0), 2)
x_batch[instance_count] = img
# increase instance counter in the current batch
instance_count += 1
instance_count += 1
return [x_batch, t_batch, yolo_1, yolo_2, yolo_3], [dummy_yolo_1, dummy_yolo_2, dummy_yolo_3]
def _get_net_size(self, idx):
@@ -154,16 +154,16 @@ class BatchGenerator(Sequence):
#print("resizing: ", net_size, net_size)
self.net_h, self.net_w = net_size, net_size
return self.net_h, self.net_w
def _aug_image(self, instance, net_h, net_w):
image_name = instance['filename']
image = cv2.imread(image_name) # RGB image
if image is None: print('Cannot find ', image_name)
image = image[:,:,::-1] # RGB image
image_h, image_w, _ = image.shape
# determine the amount of scaling and cropping
dw = self.jitter * image_w;
dh = self.jitter * image_h;
@@ -177,33 +177,33 @@ class BatchGenerator(Sequence):
else:
new_w = int(scale * net_w);
new_h = int(net_w / new_ar);
dx = int(np.random.uniform(0, net_w - new_w));
dy = int(np.random.uniform(0, net_h - new_h));
# apply scaling and cropping
im_sized = apply_random_scale_and_crop(image, new_w, new_h, net_w, net_h, dx, dy)
# randomly distort hsv space
im_sized = random_distort_image(im_sized)
# randomly flip
flip = np.random.randint(2)
im_sized = random_flip(im_sized, flip)
# correct the size and pos of bounding boxes
all_objs = correct_bounding_boxes(instance['object'], new_w, new_h, net_w, net_h, dx, dy, flip, image_w, image_h)
return im_sized, all_objs
return im_sized, all_objs
def on_epoch_end(self):
if self.shuffle: np.random.shuffle(self.instances)
def num_classes(self):
return len(self.labels)
def size(self):
return len(self.instances)
return len(self.instances)
def get_anchors(self):
anchors = []
@@ -225,4 +225,4 @@ class BatchGenerator(Sequence):
return np.array(annots)
def load_image(self, i):
return cv2.imread(self.instances[i]['filename'])
return cv2.imread(self.instances[i]['filename'])