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PaddleX
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Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleX into develop_draw

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docs/apis/transforms/det_transforms.md
浏览文件 @ d80fb08c
......@@ -122,56 +122,42 @@ paddlex.det.transforms.MixupImage(alpha=1.5, beta=1.5, mixup_epoch=-1)
## RandomExpand类
```python
paddlex.det.transforms.RandomExpand(max_ratio=4., prob=0.5, mean=[127.5, 127.5, 127.5])
paddlex.det.transforms.RandomExpand(ratio=4., prob=0.5, fill_value=[123.675, 116.28, 103.53])
```
随机扩张图像，模型训练时的数据增强操作，模型训练时的数据增强操作。
1. 随机选取扩张比例（扩张比例大于1时才进行扩张）。
2. 计算扩张后图像大小。
3. 初始化像素值为数据集均值的图像，并将原图像随机粘贴于该图像上。
随机扩张图像，模型训练时的数据增强操作。
1. 随机选取扩张比例（扩张比例大于1时才进行扩张）。
2. 计算扩张后图像大小。
3. 初始化像素值为输入填充值的图像，并将原图像随机粘贴于该图像上。
4. 根据原图像粘贴位置换算出扩张后真实标注框的位置坐标。
5. 根据原图像粘贴位置换算出扩张后真实分割区域的位置坐标。
### 参数
* **max_ratio** (float): 图像扩张的最大比例。默认为4.0。
* **ratio** (float): 图像扩张的最大比例。默认为4.0。
* **prob** (float): 随机扩张的概率。默认为0.5。
* **mean** (list): 图像数据集的均值（0-255）。默认为[127.5, 127.5, 127.5]。
* **fill_value** (list): 扩张图像的初始填充值（0-255）。默认为[123.675, 116.28, 103.53]。
## RandomCrop类
```python
paddlex.det.transforms.RandomCrop(batch_sampler=None, satisfy_all=False, avoid_no_bbox=True)
paddlex.det.transforms.RandomCrop(aspect_ratio=[.5, 2.], thresholds=[.0, .1, .3, .5, .7, .9], scaling=[.3, 1.], num_attempts=50, allow_no_crop=True, cover_all_box=False)
```
随机裁剪图像，模型训练时的数据增强操作。
1. 根据batch_sampler计算获取裁剪候选区域的位置。
(1) 根据min scale、max scale、min aspect ratio、max aspect ratio计算随机剪裁的高、宽。
(2) 根据随机剪裁的高、宽随机选取剪裁的起始点。
(3) 筛选出裁剪候选区域：
* 当satisfy_all为True时，需所有真实标注框与裁剪候选区域的重叠度满足需求时，该裁剪候选区域才可保留。
* 当satisfy_all为False时，当有一个真实标注框与裁剪候选区域的重叠度满足需求时，该裁剪候选区域就可保留。
2. 遍历所有裁剪候选区域：
(1) 若真实标注框与候选裁剪区域不重叠，或其中心点不在候选裁剪区域，则将该真实标注框去除。
(2) 计算相对于该候选裁剪区域，真实标注框的位置，并筛选出对应的类别、混合得分。
(3) 若avoid_no_bbox为False，返回当前裁剪后的信息即可；反之，要找到一个裁剪区域中真实标注框个数不为0的区域，才返回裁剪后的信息。
1. 若allow_no_crop为True，则在thresholds加入’no_crop’。
2. 随机打乱thresholds。
3. 遍历thresholds中各元素：
(1) 如果当前thresh为’no_crop’，则返回原始图像和标注信息。
(2) 随机取出aspect_ratio和scaling中的值并由此计算出候选裁剪区域的高、宽、起始点。
(3) 计算真实标注框与候选裁剪区域IoU，若全部真实标注框的IoU都小于thresh，则继续第3步。
(4) 如果cover_all_box为True且存在真实标注框的IoU小于thresh，则继续第3步。
(5) 筛选出位于候选裁剪区域内的真实标注框，若有效框的个数为0，则继续第3步，否则进行第4步。
4. 换算有效真值标注框相对候选裁剪区域的位置坐标。
5. 换算有效分割区域相对候选裁剪区域的位置坐标。
### 参数
* **batch_sampler** (list): 随机裁剪参数的多种组合，每种组合包含8个值，如下：
- max sample (int)：满足当前组合的裁剪区域的个数上限。
- max trial (int): 查找满足当前组合的次数。
- min scale (float): 裁剪面积相对原面积，每条边缩短比例的最小限制。
- max scale (float): 裁剪面积相对原面积，每条边缩短比例的最大限制。
- min aspect ratio (float): 裁剪后短边缩放比例的最小限制。
- max aspect ratio (float): 裁剪后短边缩放比例的最大限制。
- min overlap (float): 真实标注框与裁剪图像重叠面积的最小限制。
- max overlap (float): 真实标注框与裁剪图像重叠面积的最大限制。
默认值为None，当为None时采用如下设置：
[[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]]
* **satisfy_all** (bool): 是否需要所有标注框满足条件，裁剪候选区域才保留。默认为False。
* **avoid_no_bbox** (bool)： 是否对裁剪图像不存在标注框的图像进行保留。默认为True。
* **aspect_ratio** (list): 裁剪后短边缩放比例的取值范围，以[min, max]形式表示。默认值为[.5, 2.]。
* **thresholds** (list): 判断裁剪候选区域是否有效所需的IoU阈值取值列表。默认值为[.0, .1, .3, .5, .7, .9]。
* **scaling** (list): 裁剪面积相对原面积的取值范围，以[min, max]形式表示。默认值为[.3, 1.]。
* **num_attempts** (int): 在放弃寻找有效裁剪区域前尝试的次数。默认值为50。
* **allow_no_crop** (bool): 是否允许未进行裁剪。默认值为True。
* **cover_all_box** (bool): 是否要求所有的真实标注框都必须在裁剪区域内。默认值为False。
docs/deploy.md
浏览文件 @ d80fb08c
......@@ -4,10 +4,10 @@
在服务端部署的模型需要首先将模型导出为inference格式模型，导出的模型将包括`__model__`、`__params__`和`model.yml`三个文名，分别为模型的网络结构，模型权重和模型的配置文件（包括数据预处理参数等等）。在安装完PaddleX后，在命令行终端使用如下命令导出模型到当前目录`inferece_model`下。
> 可直接下载垃圾检测模型测试本文档的流程[garbage_epoch_12.tar.gz](https://bj.bcebos.com/paddlex/models/garbage_epoch_12.tar.gz)
> 可直接下载小度熊分拣模型测试本文档的流程[xiaoduxiong_epoch_12.tar.gz](https://bj.bcebos.com/paddlex/models/xiaoduxiong_epoch_12.tar.gz)
```
paddlex --export_inference --model_dir=./garbage_epoch_12 --save_dir=./inference_model
paddlex --export_inference --model_dir=./xiaoduxiong_epoch_12 --save_dir=./inference_model
```
## 模型C++和Python部署方案预计一周内推出...
docs/quick_start.md
浏览文件 @ d80fb08c
# 10分钟快速上手使用
本文档在一个小数据集上展示了如何通过PaddleX进行训练，您可以阅读PaddleX的**使用教程**来了解更多模型任务的训练使用方式。本示例同步在AIStudio上，可直接[在线体验模型训练](https://aistudio.baidu.com/aistudio/projectdetail/423472)
本文档在一个小数据集上展示了如何通过PaddleX进行训练，您可以阅读PaddleX的**使用教程**来了解更多模型任务的训练使用方式。本示例同步在AIStudio上，可直接[在线体验模型训练](https://aistudio.baidu.com/aistudio/projectdetail/439860)
## 1. 准备蔬菜分类数据集
```
......
paddlex/cv/transforms/box_utils.py
浏览文件 @ d80fb08c
......@@ -19,25 +19,6 @@ import cv2
import scipy
def meet_emit_constraint(src_bbox, sample_bbox):
center_x = (src_bbox[2] + src_bbox[0]) / 2
center_y = (src_bbox[3] + src_bbox[1]) / 2
if center_x >= sample_bbox[0] and \
center_x <= sample_bbox[2] and \
center_y >= sample_bbox[1] and \
center_y <= sample_bbox[3]:
return True
return False
def clip_bbox(src_bbox):
src_bbox[0] = max(min(src_bbox[0], 1.0), 0.0)
src_bbox[1] = max(min(src_bbox[1], 1.0), 0.0)
src_bbox[2] = max(min(src_bbox[2], 1.0), 0.0)
src_bbox[3] = max(min(src_bbox[3], 1.0), 0.0)
return src_bbox
def bbox_area(src_bbox):
if src_bbox[2] < src_bbox[0] or src_bbox[3] < src_bbox[1]:
return 0.
......@@ -47,189 +28,6 @@ def bbox_area(src_bbox):
return width * height
def is_overlap(object_bbox, sample_bbox):
if object_bbox[0] >= sample_bbox[2] or \
object_bbox[2] <= sample_bbox[0] or \
object_bbox[1] >= sample_bbox[3] or \
object_bbox[3] <= sample_bbox[1]:
return False
else:
return True
def filter_and_process(sample_bbox, bboxes, labels, scores=None):
new_bboxes = []
new_labels = []
new_scores = []
for i in range(len(bboxes)):
new_bbox = [0, 0, 0, 0]
obj_bbox = [bboxes[i][0], bboxes[i][1], bboxes[i][2], bboxes[i][3]]
if not meet_emit_constraint(obj_bbox, sample_bbox):
continue
if not is_overlap(obj_bbox, sample_bbox):
continue
sample_width = sample_bbox[2] - sample_bbox[0]
sample_height = sample_bbox[3] - sample_bbox[1]
new_bbox[0] = (obj_bbox[0] - sample_bbox[0]) / sample_width
new_bbox[1] = (obj_bbox[1] - sample_bbox[1]) / sample_height
new_bbox[2] = (obj_bbox[2] - sample_bbox[0]) / sample_width
new_bbox[3] = (obj_bbox[3] - sample_bbox[1]) / sample_height
new_bbox = clip_bbox(new_bbox)
if bbox_area(new_bbox) > 0:
new_bboxes.append(new_bbox)
new_labels.append([labels[i][0]])
if scores is not None:
new_scores.append([scores[i][0]])
bboxes = np.array(new_bboxes)
labels = np.array(new_labels)
scores = np.array(new_scores)
return bboxes, labels, scores
def bbox_area_sampling(bboxes, labels, scores, target_size, min_size):
new_bboxes = []
new_labels = []
new_scores = []
for i, bbox in enumerate(bboxes):
w = float((bbox[2] - bbox[0]) * target_size)
h = float((bbox[3] - bbox[1]) * target_size)
if w * h < float(min_size * min_size):
continue
else:
new_bboxes.append(bbox)
new_labels.append(labels[i])
if scores is not None and scores.size != 0:
new_scores.append(scores[i])
bboxes = np.array(new_bboxes)
labels = np.array(new_labels)
scores = np.array(new_scores)
return bboxes, labels, scores
def generate_sample_bbox(sampler):
scale = np.random.uniform(sampler[2], sampler[3])
aspect_ratio = np.random.uniform(sampler[4], sampler[5])
aspect_ratio = max(aspect_ratio, (scale**2.0))
aspect_ratio = min(aspect_ratio, 1 / (scale**2.0))
bbox_width = scale * (aspect_ratio**0.5)
bbox_height = scale / (aspect_ratio**0.5)
xmin_bound = 1 - bbox_width
ymin_bound = 1 - bbox_height
xmin = np.random.uniform(0, xmin_bound)
ymin = np.random.uniform(0, ymin_bound)
xmax = xmin + bbox_width
ymax = ymin + bbox_height
sampled_bbox = [xmin, ymin, xmax, ymax]
return sampled_bbox
def generate_sample_bbox_square(sampler, image_width, image_height):
scale = np.random.uniform(sampler[2], sampler[3])
aspect_ratio = np.random.uniform(sampler[4], sampler[5])
aspect_ratio = max(aspect_ratio, (scale**2.0))
aspect_ratio = min(aspect_ratio, 1 / (scale**2.0))
bbox_width = scale * (aspect_ratio**0.5)
bbox_height = scale / (aspect_ratio**0.5)
if image_height < image_width:
bbox_width = bbox_height * image_height / image_width
else:
bbox_height = bbox_width * image_width / image_height
xmin_bound = 1 - bbox_width
ymin_bound = 1 - bbox_height
xmin = np.random.uniform(0, xmin_bound)
ymin = np.random.uniform(0, ymin_bound)
xmax = xmin + bbox_width
ymax = ymin + bbox_height
sampled_bbox = [xmin, ymin, xmax, ymax]
return sampled_bbox
def data_anchor_sampling(bbox_labels, image_width, image_height, scale_array,
resize_width):
num_gt = len(bbox_labels)
# np.random.randint range: [low, high)
rand_idx = np.random.randint(0, num_gt) if num_gt != 0 else 0
if num_gt != 0:
norm_xmin = bbox_labels[rand_idx][0]
norm_ymin = bbox_labels[rand_idx][1]
norm_xmax = bbox_labels[rand_idx][2]
norm_ymax = bbox_labels[rand_idx][3]
xmin = norm_xmin * image_width
ymin = norm_ymin * image_height
wid = image_width * (norm_xmax - norm_xmin)
hei = image_height * (norm_ymax - norm_ymin)
range_size = 0
area = wid * hei
for scale_ind in range(0, len(scale_array) - 1):
if area > scale_array[scale_ind] ** 2 and area < \
scale_array[scale_ind + 1] ** 2:
range_size = scale_ind + 1
break
if area > scale_array[len(scale_array) - 2]**2:
range_size = len(scale_array) - 2
scale_choose = 0.0
if range_size == 0:
rand_idx_size = 0
else:
# np.random.randint range: [low, high)
rng_rand_size = np.random.randint(0, range_size + 1)
rand_idx_size = rng_rand_size % (range_size + 1)
if rand_idx_size == range_size:
min_resize_val = scale_array[rand_idx_size] / 2.0
max_resize_val = min(2.0 * scale_array[rand_idx_size],
2 * math.sqrt(wid * hei))
scale_choose = random.uniform(min_resize_val, max_resize_val)
else:
min_resize_val = scale_array[rand_idx_size] / 2.0
max_resize_val = 2.0 * scale_array[rand_idx_size]
scale_choose = random.uniform(min_resize_val, max_resize_val)
sample_bbox_size = wid * resize_width / scale_choose
w_off_orig = 0.0
h_off_orig = 0.0
if sample_bbox_size < max(image_height, image_width):
if wid <= sample_bbox_size:
w_off_orig = np.random.uniform(xmin + wid - sample_bbox_size,
xmin)
else:
w_off_orig = np.random.uniform(xmin,
xmin + wid - sample_bbox_size)
if hei <= sample_bbox_size:
h_off_orig = np.random.uniform(ymin + hei - sample_bbox_size,
ymin)
else:
h_off_orig = np.random.uniform(ymin,
ymin + hei - sample_bbox_size)
else:
w_off_orig = np.random.uniform(image_width - sample_bbox_size, 0.0)
h_off_orig = np.random.uniform(image_height - sample_bbox_size,
0.0)
w_off_orig = math.floor(w_off_orig)
h_off_orig = math.floor(h_off_orig)
# Figure out top left coordinates.
w_off = float(w_off_orig / image_width)
h_off = float(h_off_orig / image_height)
sampled_bbox = [
w_off, h_off, w_off + float(sample_bbox_size / image_width),
h_off + float(sample_bbox_size / image_height)
]
return sampled_bbox
else:
return 0
def jaccard_overlap(sample_bbox, object_bbox):
if sample_bbox[0] >= object_bbox[2] or \
sample_bbox[2] <= object_bbox[0] or \
......@@ -249,143 +47,143 @@ def jaccard_overlap(sample_bbox, object_bbox):
return overlap
def intersect_bbox(bbox1, bbox2):
if bbox2[0] > bbox1[2] or bbox2[2] < bbox1[0] or \
bbox2[1] > bbox1[3] or bbox2[3] < bbox1[1]:
intersection_box = [0.0, 0.0, 0.0, 0.0]
else:
intersection_box = [
max(bbox1[0], bbox2[0]),
max(bbox1[1], bbox2[1]),
min(bbox1[2], bbox2[2]),
min(bbox1[3], bbox2[3])
]
return intersection_box
def bbox_coverage(bbox1, bbox2):
inter_box = intersect_bbox(bbox1, bbox2)
intersect_size = bbox_area(inter_box)
if intersect_size > 0:
bbox1_size = bbox_area(bbox1)
return intersect_size / bbox1_size
else:
return 0.
def iou_matrix(a, b):
tl_i = np.maximum(a[:, np.newaxis, :2], b[:, :2])
br_i = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
area_i = np.prod(br_i - tl_i, axis=2) * (tl_i < br_i).all(axis=2)
area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
area_o = (area_a[:, np.newaxis] + area_b - area_i)
return area_i / (area_o + 1e-10)
def satisfy_sample_constraint(sampler,
sample_bbox,
gt_bboxes,
satisfy_all=False):
if sampler[6] == 0 and sampler[7] == 0:
return True
satisfied = []
for i in range(len(gt_bboxes)):
object_bbox = [
gt_bboxes[i][0], gt_bboxes[i][1], gt_bboxes[i][2], gt_bboxes[i][3]
]
overlap = jaccard_overlap(sample_bbox, object_bbox)
if sampler[6] != 0 and \
overlap < sampler[6]:
satisfied.append(False)
continue
if sampler[7] != 0 and \
overlap > sampler[7]:
satisfied.append(False)
continue
satisfied.append(True)
if not satisfy_all:
return True
if satisfy_all:
return np.all(satisfied)
else:
return False
def crop_box_with_center_constraint(box, crop):
cropped_box = box.copy()
cropped_box[:, :2] = np.maximum(box[:, :2], crop[:2])
cropped_box[:, 2:] = np.minimum(box[:, 2:], crop[2:])
cropped_box[:, :2] -= crop[:2]
cropped_box[:, 2:] -= crop[:2]
centers = (box[:, :2] + box[:, 2:]) / 2
valid = np.logical_and(crop[:2] <= centers, centers < crop[2:]).all(axis=1)
valid = np.logical_and(
valid, (cropped_box[:, :2] < cropped_box[:, 2:]).all(axis=1))
return cropped_box, np.where(valid)[0]
def is_poly(segm):
if not isinstance(segm, (list, dict)):
raise Exception("Invalid segm type: {}".format(type(segm)))
return isinstance(segm, list)
def satisfy_sample_constraint_coverage(sampler, sample_bbox, gt_bboxes):
if sampler[6] == 0 and sampler[7] == 0:
has_jaccard_overlap = False
else:
has_jaccard_overlap = True
if sampler[8] == 0 and sampler[9] == 0:
has_object_coverage = False
else:
has_object_coverage = True
if not has_jaccard_overlap and not has_object_coverage:
return True
found = False
for i in range(len(gt_bboxes)):
object_bbox = [
gt_bboxes[i][0], gt_bboxes[i][1], gt_bboxes[i][2], gt_bboxes[i][3]
]
if has_jaccard_overlap:
overlap = jaccard_overlap(sample_bbox, object_bbox)
if sampler[6] != 0 and \
overlap < sampler[6]:
continue
if sampler[7] != 0 and \
overlap > sampler[7]:
continue
found = True
if has_object_coverage:
object_coverage = bbox_coverage(object_bbox, sample_bbox)
if sampler[8] != 0 and \
object_coverage < sampler[8]:
continue
if sampler[9] != 0 and \
object_coverage > sampler[9]:
continue
found = True
if found:
return True
return found
def crop_image_sampling(img, sample_bbox, image_width, image_height,
target_size):
# no clipping here
xmin = int(sample_bbox[0] * image_width)
xmax = int(sample_bbox[2] * image_width)
ymin = int(sample_bbox[1] * image_height)
ymax = int(sample_bbox[3] * image_height)
w_off = xmin
h_off = ymin
width = xmax - xmin
height = ymax - ymin
cross_xmin = max(0.0, float(w_off))
cross_ymin = max(0.0, float(h_off))
cross_xmax = min(float(w_off + width - 1.0), float(image_width))
cross_ymax = min(float(h_off + height - 1.0), float(image_height))
cross_width = cross_xmax - cross_xmin
cross_height = cross_ymax - cross_ymin
roi_xmin = 0 if w_off >= 0 else abs(w_off)
roi_ymin = 0 if h_off >= 0 else abs(h_off)
roi_width = cross_width
roi_height = cross_height
roi_y1 = int(roi_ymin)
roi_y2 = int(roi_ymin + roi_height)
roi_x1 = int(roi_xmin)
roi_x2 = int(roi_xmin + roi_width)
cross_y1 = int(cross_ymin)
cross_y2 = int(cross_ymin + cross_height)
cross_x1 = int(cross_xmin)
cross_x2 = int(cross_xmin + cross_width)
sample_img = np.zeros((height, width, 3))
sample_img[roi_y1: roi_y2, roi_x1: roi_x2] = \
img[cross_y1: cross_y2, cross_x1: cross_x2]
sample_img = cv2.resize(
sample_img, (target_size, target_size), interpolation=cv2.INTER_AREA)
return sample_img
def crop_image(img, crop):
x1, y1, x2, y2 = crop
return img[y1:y2, x1:x2, :]
def crop_segms(segms, valid_ids, crop, height, width):
def _crop_poly(segm, crop):
xmin, ymin, xmax, ymax = crop
crop_coord = [xmin, ymin, xmin, ymax, xmax, ymax, xmax, ymin]
crop_p = np.array(crop_coord).reshape(4, 2)
crop_p = Polygon(crop_p)
crop_segm = list()
for poly in segm:
poly = np.array(poly).reshape(len(poly) // 2, 2)
polygon = Polygon(poly)
if not polygon.is_valid:
exterior = polygon.exterior
multi_lines = exterior.intersection(exterior)
polygons = shapely.ops.polygonize(multi_lines)
polygon = MultiPolygon(polygons)
multi_polygon = list()
if isinstance(polygon, MultiPolygon):
multi_polygon = copy.deepcopy(polygon)
else:
multi_polygon.append(copy.deepcopy(polygon))
for per_polygon in multi_polygon:
inter = per_polygon.intersection(crop_p)
if not inter:
continue
if isinstance(inter, (MultiPolygon, GeometryCollection)):
for part in inter:
if not isinstance(part, Polygon):
continue
part = np.squeeze(
np.array(part.exterior.coords[:-1]).reshape(1, -1))
part[0::2] -= xmin
part[1::2] -= ymin
crop_segm.append(part.tolist())
elif isinstance(inter, Polygon):
crop_poly = np.squeeze(
np.array(inter.exterior.coords[:-1]).reshape(1, -1))
crop_poly[0::2] -= xmin
crop_poly[1::2] -= ymin
crop_segm.append(crop_poly.tolist())
else:
continue
return crop_segm
def _crop_rle(rle, crop, height, width):
if 'counts' in rle and type(rle['counts']) == list:
rle = mask_util.frPyObjects(rle, height, width)
mask = mask_util.decode(rle)
mask = mask[crop[1]:crop[3], crop[0]:crop[2]]
rle = mask_util.encode(np.array(mask, order='F', dtype=np.uint8))
return rle
crop_segms = []
for id in valid_ids:
segm = segms[id]
if is_poly(segm):
import copy
import shapely.ops
import logging
from shapely.geometry import Polygon, MultiPolygon, GeometryCollection
logging.getLogger("shapely").setLevel(logging.WARNING)
# Polygon format
crop_segms.append(_crop_poly(segm, crop))
else:
# RLE format
import pycocotools.mask as mask_util
crop_segms.append(_crop_rle(segm, crop, height, width))
return crop_segms
def expand_segms(segms, x, y, height, width, ratio):
def _expand_poly(poly, x, y):
expanded_poly = np.array(poly)
expanded_poly[0::2] += x
expanded_poly[1::2] += y
return expanded_poly.tolist()
def _expand_rle(rle, x, y, height, width, ratio):
if 'counts' in rle and type(rle['counts']) == list:
rle = mask_util.frPyObjects(rle, height, width)
mask = mask_util.decode(rle)
expanded_mask = np.full((int(height * ratio), int(width * ratio)),
0).astype(mask.dtype)
expanded_mask[y:y + height, x:x + width] = mask
rle = mask_util.encode(
np.array(expanded_mask, order='F', dtype=np.uint8))
return rle
expanded_segms = []
for segm in segms:
if is_poly(segm):
# Polygon format
expanded_segms.append([_expand_poly(poly, x, y) for poly in segm])
else:
# RLE format
import pycocotools.mask as mask_util
expanded_segms.append(
_expand_rle(segm, x, y, height, width, ratio))
return expanded_segms
def box_horizontal_flip(bboxes, width):
......@@ -409,15 +207,10 @@ def segms_horizontal_flip(segms, height, width):
if 'counts' in rle and type(rle['counts']) == list:
rle = mask_util.frPyObjects([rle], height, width)
mask = mask_util.decode(rle)
mask = mask[:, ::-1, :]
mask = mask[:, ::-1]
rle = mask_util.encode(np.array(mask, order='F', dtype=np.uint8))
return rle
def is_poly(segm):
if not isinstance(segm, (list, dict)):
raise Exception("Invalid segm type: {}".format(type(segm)))
return isinstance(segm, list)
flipped_segms = []
for segm in segms:
if is_poly(segm):
......
paddlex/cv/transforms/cls_transforms.py
浏览文件 @ d80fb08c
......@@ -385,15 +385,12 @@ class RandomDistort:
'saturation': self.saturation_prob,
'hue': self.hue_prob,
}
im = im.astype('uint8')
im = Image.fromarray(im)
for id in range(len(ops)):
params = params_dict[ops[id].__name__]
prob = prob_dict[ops[id].__name__]
params['im'] = im
if np.random.uniform(0, 1) < prob:
im = ops[id](**params)
im = np.asarray(im).astype('float32')
if label is None:
return (im, )
else:
......
paddlex/cv/transforms/det_transforms.py
浏览文件 @ d80fb08c
......@@ -12,13 +12,20 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .ops import *
from .box_utils import *
try:
from collections.abc import Sequence
except Exception:
from collections import Sequence
import random
import os.path as osp
import numpy as np
from PIL import Image, ImageEnhance
import cv2
from PIL import Image, ImageEnhance
from .ops import *
from .box_utils import *
class Compose:
......@@ -81,7 +88,7 @@ class Compose:
im = cv2.imread(im_file).astype('float32')
except:
raise TypeError(
'Can\'t read The image file {}!'.format(im_file))
'Can\'t read The image file {}!'.format(im_file))
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
# make default im_info with [h, w, 1]
im_info['im_resize_info'] = np.array(
......@@ -534,15 +541,13 @@ class RandomDistort:
'saturation': self.saturation_prob,
'hue': self.hue_prob
}
im = im.astype('uint8')
im = Image.fromarray(im)
for id in range(4):
params = params_dict[ops[id].__name__]
prob = prob_dict[ops[id].__name__]
params['im'] = im
if np.random.uniform(0, 1) < prob:
im = ops[id](**params)
im = np.asarray(im).astype('float32')
if label_info is None:
return (im, im_info)
else:
......@@ -591,7 +596,7 @@ class MixupImage:
img1.astype('float32') * factor
img[:img2.shape[0], :img2.shape[1], :] += \
img2.astype('float32') * (1.0 - factor)
return img.astype('uint8')
return img.astype('float32')
def __call__(self, im, im_info=None, label_info=None):
"""
......@@ -658,9 +663,17 @@ class MixupImage:
gt_score2 = im_info['mixup'][2]['gt_score']
gt_score = np.concatenate(
(gt_score1 * factor, gt_score2 * (1. - factor)), axis=0)
if 'gt_poly' in label_info:
gt_poly1 = label_info['gt_poly']
gt_poly2 = im_info['mixup'][2]['gt_poly']
label_info['gt_poly'] = gt_poly1 + gt_poly2
is_crowd1 = label_info['is_crowd']
is_crowd2 = im_info['mixup'][2]['is_crowd']
is_crowd = np.concatenate((is_crowd1, is_crowd2), axis=0)
label_info['gt_bbox'] = gt_bbox
label_info['gt_score'] = gt_score
label_info['gt_class'] = gt_class
label_info['is_crowd'] = is_crowd
im_info['augment_shape'] = np.array([im.shape[0],
im.shape[1]]).astype('int32')
im_info.pop('mixup')
......@@ -672,23 +685,30 @@ class MixupImage:
class RandomExpand:
"""随机扩张图像，模型训练时的数据增强操作。
1. 随机选取扩张比例（扩张比例大于1时才进行扩张）。
2. 计算扩张后图像大小。
3. 初始化像素值为数据集均值的图像，并将原图像随机粘贴于该图像上。
3. 初始化像素值为输入填充值的图像，并将原图像随机粘贴于该图像上。
4. 根据原图像粘贴位置换算出扩张后真实标注框的位置坐标。
5. 根据原图像粘贴位置换算出扩张后真实分割区域的位置坐标。
Args:
max_ratio (float): 图像扩张的最大比例。默认为4.0。
ratio (float): 图像扩张的最大比例。默认为4.0。
prob (float): 随机扩张的概率。默认为0.5。
mean (list): 图像数据集的均值（0-255）。默认为[127.5, 127.5, 127.5]。
fill_value (list): 扩张图像的初始填充值（0-255）。默认为[123.675, 116.28, 103.53]。
"""
def __init__(self, max_ratio=4., prob=0.5, mean=[127.5, 127.5, 127.5]):
self.max_ratio = max_ratio
self.mean = mean
def __init__(self,
ratio=4.,
prob=0.5,
fill_value=[123.675, 116.28, 103.53]):
super(RandomExpand, self).__init__()
assert ratio > 1.01, "expand ratio must be larger than 1.01"
self.ratio = ratio
self.prob = prob
assert isinstance(fill_value, Sequence), \
"fill value must be sequence"
if not isinstance(fill_value, tuple):
fill_value = tuple(fill_value)
self.fill_value = fill_value
def __call__(self, im, im_info=None, label_info=None):
"""
......@@ -696,7 +716,6 @@ class RandomExpand:
im (np.ndarray): 图像np.ndarray数据。
im_info (dict, 可选): 存储与图像相关的信息。
label_info (dict, 可选): 存储与标注框相关的信息。
Returns:
tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典；
当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、
......@@ -708,7 +727,6 @@ class RandomExpand:
其中n代表真实标注框的个数。
- gt_class (np.ndarray): 随机扩张后每个真实标注框对应的类别序号，形状为(n, 1)，
其中n代表真实标注框的个数。
Raises:
TypeError: 形参数据类型不满足需求。
"""
......@@ -723,108 +741,68 @@ class RandomExpand:
'gt_class' not in label_info:
raise TypeError('Cannot do RandomExpand! ' + \
'Becasuse gt_bbox/gt_class is not in label_info!')
prob = np.random.uniform(0, 1)
if np.random.uniform(0., 1.) < self.prob:
return (im, im_info, label_info)
augment_shape = im_info['augment_shape']
im_width = augment_shape[1]
im_height = augment_shape[0]
gt_bbox = label_info['gt_bbox']
gt_class = label_info['gt_class']
if prob < self.prob:
if self.max_ratio - 1 >= 0.01:
expand_ratio = np.random.uniform(1, self.max_ratio)
height = int(im_height * expand_ratio)
width = int(im_width * expand_ratio)
h_off = math.floor(np.random.uniform(0, height - im_height))
w_off = math.floor(np.random.uniform(0, width - im_width))
expand_bbox = [
-w_off / im_width, -h_off / im_height,
(width - w_off) / im_width, (height - h_off) / im_height
]
expand_im = np.ones((height, width, 3))
expand_im = np.uint8(expand_im * np.squeeze(self.mean))
expand_im = Image.fromarray(expand_im)
im = im.astype('uint8')
im = Image.fromarray(im)
expand_im.paste(im, (int(w_off), int(h_off)))
expand_im = np.asarray(expand_im)
for i in range(gt_bbox.shape[0]):
gt_bbox[i][0] = gt_bbox[i][0] / im_width
gt_bbox[i][1] = gt_bbox[i][1] / im_height
gt_bbox[i][2] = gt_bbox[i][2] / im_width
gt_bbox[i][3] = gt_bbox[i][3] / im_height
gt_bbox, gt_class, _ = filter_and_process(
expand_bbox, gt_bbox, gt_class)
for i in range(gt_bbox.shape[0]):
gt_bbox[i][0] = gt_bbox[i][0] * width
gt_bbox[i][1] = gt_bbox[i][1] * height
gt_bbox[i][2] = gt_bbox[i][2] * width
gt_bbox[i][3] = gt_bbox[i][3] * height
im = expand_im.astype('float32')
label_info['gt_bbox'] = gt_bbox
label_info['gt_class'] = gt_class
im_info['augment_shape'] = np.array([height,
width]).astype('int32')
if label_info is None:
return (im, im_info)
else:
height = int(augment_shape[0])
width = int(augment_shape[1])
expand_ratio = np.random.uniform(1., self.ratio)
h = int(height * expand_ratio)
w = int(width * expand_ratio)
if not h > height or not w > width:
return (im, im_info, label_info)
y = np.random.randint(0, h - height)
x = np.random.randint(0, w - width)
canvas = np.ones((h, w, 3), dtype=np.float32)
canvas *= np.array(self.fill_value, dtype=np.float32)
canvas[y:y + height, x:x + width, :] = im
im_info['augment_shape'] = np.array([h, w]).astype('int32')
if 'gt_bbox' in label_info and len(label_info['gt_bbox']) > 0:
label_info['gt_bbox'] += np.array([x, y] * 2, dtype=np.float32)
if 'gt_poly' in label_info and len(label_info['gt_poly']) > 0:
label_info['gt_poly'] = expand_segms(label_info['gt_poly'], x, y,
height, width, expand_ratio)
return (canvas, im_info, label_info)
class RandomCrop:
"""随机裁剪图像。
1. 根据batch_sampler计算获取裁剪候选区域的位置。
(1) 根据min scale、max scale、min aspect ratio、max aspect ratio计算随机剪裁的高、宽。
(2) 根据随机剪裁的高、宽随机选取剪裁的起始点。
(3) 筛选出裁剪候选区域：
- 当satisfy_all为True时，需所有真实标注框与裁剪候选区域的重叠度满足需求时，该裁剪候选区域才可保留。
- 当satisfy_all为False时，当有一个真实标注框与裁剪候选区域的重叠度满足需求时，该裁剪候选区域就可保留。
2. 遍历所有裁剪候选区域：
(1) 若真实标注框与候选裁剪区域不重叠，或其中心点不在候选裁剪区域，
则将该真实标注框去除。
(2) 计算相对于该候选裁剪区域，真实标注框的位置，并筛选出对应的类别、混合得分。
(3) 若avoid_no_bbox为False，返回当前裁剪后的信息即可；
反之，要找到一个裁剪区域中真实标注框个数不为0的区域，才返回裁剪后的信息。
1. 若allow_no_crop为True，则在thresholds加入’no_crop’。
2. 随机打乱thresholds。
3. 遍历thresholds中各元素：
(1) 如果当前thresh为’no_crop’，则返回原始图像和标注信息。
(2) 随机取出aspect_ratio和scaling中的值并由此计算出候选裁剪区域的高、宽、起始点。
(3) 计算真实标注框与候选裁剪区域IoU，若全部真实标注框的IoU都小于thresh，则继续第3步。
(4) 如果cover_all_box为True且存在真实标注框的IoU小于thresh，则继续第3步。
(5) 筛选出位于候选裁剪区域内的真实标注框，若有效框的个数为0，则继续第3步，否则进行第4步。
4. 换算有效真值标注框相对候选裁剪区域的位置坐标。
5. 换算有效分割区域相对候选裁剪区域的位置坐标。
Args:
batch_sampler (list): 随机裁剪参数的多种组合，每种组合包含8个值，如下：
- max sample (int)：满足当前组合的裁剪区域的个数上限。
- max trial (int): 查找满足当前组合的次数。
- min scale (float): 裁剪面积相对原面积，每条边缩短比例的最小限制。
- max scale (float): 裁剪面积相对原面积，每条边缩短比例的最大限制。
- min aspect ratio (float): 裁剪后短边缩放比例的最小限制。
- max aspect ratio (float): 裁剪后短边缩放比例的最大限制。
- min overlap (float): 真实标注框与裁剪图像重叠面积的最小限制。
- max overlap (float): 真实标注框与裁剪图像重叠面积的最大限制。
默认值为None，当为None时采用如下设置：
[[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]]
satisfy_all (bool): 是否需要所有标注框满足条件，裁剪候选区域才保留。默认为False。
avoid_no_bbox (bool)： 是否对裁剪图像不存在标注框的图像进行保留。默认为True。
aspect_ratio (list): 裁剪后短边缩放比例的取值范围，以[min, max]形式表示。默认值为[.5, 2.]。
thresholds (list): 判断裁剪候选区域是否有效所需的IoU阈值取值列表。默认值为[.0, .1, .3, .5, .7, .9]。
scaling (list): 裁剪面积相对原面积的取值范围，以[min, max]形式表示。默认值为[.3, 1.]。
num_attempts (int): 在放弃寻找有效裁剪区域前尝试的次数。默认值为50。
allow_no_crop (bool): 是否允许未进行裁剪。默认值为True。
cover_all_box (bool): 是否要求所有的真实标注框都必须在裁剪区域内。默认值为False。
"""
def __init__(self,
batch_sampler=None,
satisfy_all=False,
avoid_no_bbox=True):
if batch_sampler is None:
batch_sampler = [[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]]
self.batch_sampler = batch_sampler
self.satisfy_all = satisfy_all
self.avoid_no_bbox = avoid_no_bbox
aspect_ratio=[.5, 2.],
thresholds=[.0, .1, .3, .5, .7, .9],
scaling=[.3, 1.],
num_attempts=50,
allow_no_crop=True,
cover_all_box=False):
self.aspect_ratio = aspect_ratio
self.thresholds = thresholds
self.scaling = scaling
self.num_attempts = num_attempts
self.allow_no_crop = allow_no_crop
self.cover_all_box = cover_all_box
def __call__(self, im, im_info=None, label_info=None):
"""
......@@ -859,66 +837,84 @@ class RandomCrop:
'gt_class' not in label_info:
raise TypeError('Cannot do RandomCrop! ' + \
'Becasuse gt_bbox/gt_class is not in label_info!')
if len(label_info['gt_bbox']) == 0:
return (im, im_info, label_info)
augment_shape = im_info['augment_shape']
im_width = augment_shape[1]
im_height = augment_shape[0]
w = augment_shape[1]
h = augment_shape[0]
gt_bbox = label_info['gt_bbox']
gt_bbox_tmp = gt_bbox.copy()
for i in range(gt_bbox_tmp.shape[0]):
gt_bbox_tmp[i][0] = gt_bbox[i][0] / im_width
gt_bbox_tmp[i][1] = gt_bbox[i][1] / im_height
gt_bbox_tmp[i][2] = gt_bbox[i][2] / im_width
gt_bbox_tmp[i][3] = gt_bbox[i][3] / im_height
gt_class = label_info['gt_class']
gt_score = None
if 'gt_score' in label_info:
gt_score = label_info['gt_score']
sampled_bbox = []
gt_bbox_tmp = gt_bbox_tmp.tolist()
for sampler in self.batch_sampler:
found = 0
for i in range(sampler[1]):
if found >= sampler[0]:
break
sample_bbox = generate_sample_bbox(sampler)
if satisfy_sample_constraint(sampler, sample_bbox, gt_bbox_tmp,
self.satisfy_all):
sampled_bbox.append(sample_bbox)
found = found + 1
im = np.array(im)
while sampled_bbox:
idx = int(np.random.uniform(0, len(sampled_bbox)))
sample_bbox = sampled_bbox.pop(idx)
sample_bbox = clip_bbox(sample_bbox)
crop_bbox, crop_class, crop_score = \
filter_and_process(sample_bbox, gt_bbox_tmp, gt_class, gt_score)
if self.avoid_no_bbox:
if len(crop_bbox) < 1:
thresholds = list(self.thresholds)
if self.allow_no_crop:
thresholds.append('no_crop')
np.random.shuffle(thresholds)
for thresh in thresholds:
if thresh == 'no_crop':
return (im, im_info, label_info)
found = False
for i in range(self.num_attempts):
scale = np.random.uniform(*self.scaling)
min_ar, max_ar = self.aspect_ratio
aspect_ratio = np.random.uniform(
max(min_ar, scale**2), min(max_ar, scale**-2))
crop_h = int(h * scale / np.sqrt(aspect_ratio))
crop_w = int(w * scale * np.sqrt(aspect_ratio))
crop_y = np.random.randint(0, h - crop_h)
crop_x = np.random.randint(0, w - crop_w)
crop_box = [crop_x, crop_y, crop_x + crop_w, crop_y + crop_h]
iou = iou_matrix(gt_bbox, np.array([crop_box],
dtype=np.float32))
if iou.max() < thresh:
continue
xmin = int(sample_bbox[0] * im_width)
xmax = int(sample_bbox[2] * im_width)
ymin = int(sample_bbox[1] * im_height)
ymax = int(sample_bbox[3] * im_height)
im = im[ymin:ymax, xmin:xmax]
for i in range(crop_bbox.shape[0]):
crop_bbox[i][0] = crop_bbox[i][0] * (xmax - xmin)
crop_bbox[i][1] = crop_bbox[i][1] * (ymax - ymin)
crop_bbox[i][2] = crop_bbox[i][2] * (xmax - xmin)
crop_bbox[i][3] = crop_bbox[i][3] * (ymax - ymin)
label_info['gt_bbox'] = crop_bbox
label_info['gt_class'] = crop_class
label_info['gt_score'] = crop_score
im_info['augment_shape'] = np.array([ymax - ymin,
xmax - xmin]).astype('int32')
if label_info is None:
return (im, im_info)
else:
if self.cover_all_box and iou.min() < thresh:
continue
cropped_box, valid_ids = crop_box_with_center_constraint(
gt_bbox, np.array(crop_box, dtype=np.float32))
if valid_ids.size > 0:
found = True
break
if found:
if 'gt_poly' in label_info and len(label_info['gt_poly']) > 0:
crop_polys = crop_segms(label_info['gt_poly'], valid_ids,
np.array(crop_box, dtype=np.int64),
h, w)
if [] in crop_polys:
delete_id = list()
valid_polys = list()
for id, crop_poly in enumerate(crop_polys):
if crop_poly == []:
delete_id.append(id)
else:
valid_polys.append(crop_poly)
valid_ids = np.delete(valid_ids, delete_id)
if len(valid_polys) == 0:
return (im, im_info, label_info)
label_info['gt_poly'] = valid_polys
else:
label_info['gt_poly'] = crop_polys
im = crop_image(im, crop_box)
label_info['gt_bbox'] = np.take(cropped_box, valid_ids, axis=0)
label_info['gt_class'] = np.take(
label_info['gt_class'], valid_ids, axis=0)
im_info['augment_shape'] = np.array(
[crop_box[3] - crop_box[1],
crop_box[2] - crop_box[0]]).astype('int32')
if 'gt_score' in label_info:
label_info['gt_score'] = np.take(
label_info['gt_score'], valid_ids, axis=0)
if 'is_crowd' in label_info:
label_info['is_crowd'] = np.take(
label_info['is_crowd'], valid_ids, axis=0)
return (im, im_info, label_info)
if label_info is None:
return (im, im_info)
else:
return (im, im_info, label_info)
return (im, im_info, label_info)
class ArrangeFasterRCNN:
......
paddlex/cv/transforms/ops.py
浏览文件 @ d80fb08c
......@@ -111,32 +111,41 @@ def bgr2rgb(im):
return im[:, :, ::-1]
def brightness(im, brightness_lower, brightness_upper):
brightness_delta = np.random.uniform(brightness_lower, brightness_upper)
im = ImageEnhance.Brightness(im).enhance(brightness_delta)
def hue(im, hue_lower, hue_upper):
delta = np.random.uniform(hue_lower, hue_upper)
u = np.cos(delta * np.pi)
w = np.sin(delta * np.pi)
bt = np.array([[1.0, 0.0, 0.0], [0.0, u, -w], [0.0, w, u]])
tyiq = np.array([[0.299, 0.587, 0.114], [0.596, -0.274, -0.321],
[0.211, -0.523, 0.311]])
ityiq = np.array([[1.0, 0.956, 0.621], [1.0, -0.272, -0.647],
[1.0, -1.107, 1.705]])
t = np.dot(np.dot(ityiq, bt), tyiq).T
im = np.dot(im, t)
return im
def contrast(im, contrast_lower, contrast_upper):
contrast_delta = np.random.uniform(contrast_lower, contrast_upper)
im = ImageEnhance.Contrast(im).enhance(contrast_delta)
def saturation(im, saturation_lower, saturation_upper):
delta = np.random.uniform(saturation_lower, saturation_upper)
gray = im * np.array([[[0.299, 0.587, 0.114]]], dtype=np.float32)
gray = gray.sum(axis=2, keepdims=True)
gray *= (1.0 - delta)
im *= delta
im += gray
return im
def saturation(im, saturation_lower, saturation_upper):
saturation_delta = np.random.uniform(saturation_lower, saturation_upper)
im = ImageEnhance.Color(im).enhance(saturation_delta)
def contrast(im, contrast_lower, contrast_upper):
delta = np.random.uniform(contrast_lower, contrast_upper)
im *= delta
return im
def hue(im, hue_lower, hue_upper):
hue_delta = np.random.uniform(hue_lower, hue_upper)
im = np.array(im.convert('HSV'))
im[:, :, 0] = im[:, :, 0] + hue_delta
im = Image.fromarray(im, mode='HSV').convert('RGB')
def brightness(im, brightness_lower, brightness_upper):
delta = np.random.uniform(brightness_lower, brightness_upper)
im += delta
return im
def rotate(im, rotate_lower, rotate_upper):
rotate_delta = np.random.uniform(rotate_lower, rotate_upper)
im = im.rotate(int(rotate_delta))
......
paddlex/cv/transforms/seg_transforms.py
浏览文件 @ d80fb08c
......@@ -889,15 +889,12 @@ class RandomDistort:
'saturation': self.saturation_prob,
'hue': self.hue_prob
}
im = im.astype('uint8')
im = Image.fromarray(im)
for id in range(4):
params = params_dict[ops[id].__name__]
prob = prob_dict[ops[id].__name__]
params['im'] = im
if np.random.uniform(0, 1) < prob:
im = ops[id](**params)
im = np.asarray(im).astype('float32')
if label is None:
return (im, im_info)
else:
......
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