def read_img(img_read):
if IM_RESIZE:
img_read = cv2.resize(img_read, (640, 480),
interpolation=cv2.INTER_CUBIC)
img_raw = np.asarray(img_read, dtype=np.uint8)
print("dim2", img_raw.ndim)
img_raw_final = img_raw.copy()
img = np.asarray(img_read, dtype=np.float32)
D, H, W = img.shape
img = img.transpose((2, 0, 1))
a_D, a_H, a_W = img.shape
img = preprocess(img)
o_D, o_H, o_W = img.shape
scale = o_H / H
scale_ = D / o_H
print('D,H,W,a_D, a_H, a_W,o_D,o_H, o_W,scale:', D, H, W, a_D, a_H, a_W,
o_D, o_H, o_W, scale)
return img, img_raw_final, scale, scale_
def read_img(path):
f = Image.open(path)
if IM_RESIZE:
f = f.resize((640,480), Image.ANTIALIAS)
f.convert('RGB')
img_raw = np.asarray(f, dtype=np.uint8)
print("dim2",img_raw.ndim)
img_raw_final = img_raw.copy()
img = np.asarray(f, dtype=np.float32)
D, H, W = img.shape
img = img.transpose((2,0,1))
a_D, a_H, a_W = img.shape
img = preprocess(img)
o_D, o_H, o_W = img.shape
scale = o_H / H
scale_=D/o_H
print('D,H,W,a_D, a_H, a_W,o_D,o_H, o_W,scale:',D,H,W,a_D, a_H, a_W,o_D,o_H,o_W,scale)
return img, img_raw_final, scale,scale_
def read_img(frame):
# f = Image.open(path)
# if IM_RESIZE:
# f = f.resize((640,480), Image.ANTIALIAS)
# f.convert('RGB')
f = Image.fromarray(frame).convert('RGB')
img_raw = np.asarray(f, dtype=np.uint8)
img_raw_final = img_raw.copy()
img = np.asarray(f, dtype=np.float32)
_, H, W = img.shape
img = img.transpose((2, 0, 1))
img = preprocess(img)
_, o_H, o_W = img.shape
scale = o_H / H
f = f.resize((img.shape[2], img.shape[1]), Image.ANTIALIAS)
f.convert('RGB')
img_raw = np.asarray(f, dtype=np.uint8)
img_raw_final = img_raw.copy()
# print(type(img_raw_final))
# print(img_raw_final.shape)
return img, img_raw_final, scale
def getFeatureMap(self, imgs, sizes=None):
self.eval()
self.use_preset("visualize")
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
feature_maps = list()
for img, size in zip(prepared_imgs, sizes):
img = at.totensor(img[None]).float()
scale = img.shape[3] / size[1]
feature_map = self.extractor(img)
feature_maps.append(feature_map)
feature_maps = np.array(feature_maps)
self.use_preset("evaluate")
self.train()
return feature_maps
def predict(self, imgs, sizes=None, visualize=False):
"""Detect objects from images.
This method predicts objects for each image.
Args:
imgs (iterable of numpy.ndarray): Arrays holding images.
and the range of their value is :math:`[0, 255]`.
Returns:
tuple of lists:
This method returns a tuple of three lists,
:obj:`(bboxes, labels, scores)`.
* **bboxes**: (R, 4)
:math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
* **labels** :
Each value indicates the class of the bounding box. \
Values are in range :math:`[0, L - 1]`, where :math:`L` is the \
number of the foreground classes.
* **scores** :
Each value indicates how confident the prediction is.
"""
self.eval()
if visualize:
self.use_preset('visualize') # 本来是 visualize evaluate
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list()
labels = list()
scores = list()
for img, size in zip(prepared_imgs, sizes):
img = t.autograd.Variable(at.totensor(img).float()[None],
volatile=True)
scale = img.shape[3] / size[1]
roi_cls_loc, roi_scores, rois, _ = self(
img, scale=scale) # 这里调用了 forward 方法
# We are assuming that batch size is 1.
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
roi = at.totensor(rois) / scale
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
mean = t.Tensor(self.loc_normalize_mean). \
repeat(self.n_class)[None]
std = t.Tensor(self.loc_normalize_std). \
repeat(self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
cls_bbox = loc2bbox(
at.tonumpy(roi).reshape((-1, 4)),
at.tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = at.totensor(cls_bbox)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
# clip bounding box
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = at.tonumpy(F.softmax(at.tovariable(roi_score),
dim=1)) # 有趣 打出来的分，softmax后变成概率值
# 可以在这里看一下预测出来的最大概率是多少，如果太小就直接return出去，下面都不用跑了
# prob 是 300 x 21 的尺寸， np.sum(prob) = 300
raw_cls_bbox = at.tonumpy(cls_bbox)
raw_prob = at.tonumpy(prob)
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate')
self.train()
return bboxes, labels, scores
def predict(self, imgs, sizes=None, visualize=False):
"""Detect objects from images.
This method predicts objects for each image.
Args:
imgs (iterable of numpy.ndarray): Arrays holding images.
All images are in CHW and RGB format
and the range of their value is :math:`[0, 255]`.
Returns:
tuple of lists:
This method returns a tuple of three lists,
:obj:`(bboxes, labels, scores)`.
* **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
where :math:`R` is the number of bounding boxes in a image. \
Each bouding box is organized by \
:math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
in the second axis.
* **labels** : A list of integer arrays of shape :math:`(R,)`. \
Each value indicates the class of the bounding box. \
Values are in range :math:`[0, L - 1]`, where :math:`L` is the \
number of the foreground classes.
* **scores** : A list of float arrays of shape :math:`(R,)`. \
Each value indicates how confident the prediction is.
"""
self.eval()
if visualize:
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list()
labels = list()
scores = list()
for img, size in zip(prepared_imgs, sizes):
img = at.totensor(img[None]).float()
scale = img.shape[3] / size[1]
roi_cls_loc, roi_scores, rois, _ = self(img, scale=scale)
# We are assuming that batch size is 1.
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
roi = at.totensor(rois) / scale
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
mean = t.Tensor(self.loc_normalize_mean).cuda(). \
repeat(self.n_class)[None]
std = t.Tensor(self.loc_normalize_std).cuda(). \
repeat(self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
cls_bbox = loc2bbox(
at.tonumpy(roi).reshape((-1, 4)),
at.tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = at.totensor(cls_bbox)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
# clip bounding box
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = (F.softmax(at.totensor(roi_score), dim=1))
bbox, label, score = self._suppress(cls_bbox, prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate')
self.train()
return bboxes, labels, scores
def predict(self, imgs,sizes=None,visualize=False): #预测函数
"""Detect objects from images.
This method predicts objects for each image.
Args:
imgs (iterable of numpy.ndarray): Arrays holding images.
All images are in CHW and RGB format
and the range of their value is :math:`[0, 255]`.
Returns:
tuple of lists:
This method returns a tuple of three lists,
:obj:`(bboxes, labels, scores)`.
* **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
where :math:`R` is the number of bounding boxes in a image. \
Each bouding box is organized by \
:math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
in the second axis.
* **labels** : A list of integer arrays of shape :math:`(R,)`. \
Each value indicates the class of the bounding box. \
Values are in range :math:`[0, L - 1]`, where :math:`L` is the \
number of the foreground classes.
* **scores** : A list of float arrays of shape :math:`(R,)`. \
Each value indicates how confident the prediction is.
"""
self.eval() #网络设置为eval模式(禁用BatchNorm和Dropout)
if visualize: #可视化内容，（跳过）
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list() #最终的输出框
labels = list() #最终的输出label
scores = list() #最终的输出分数
for img, size in zip(prepared_imgs, sizes):
img = at.totensor(img[None]).float() #增加batch维
scale = img.shape[3] / size[1] #获得scale（待定）
roi_cls_loc, roi_scores, rois, _ = self(img, scale=scale) #前向
# We are assuming that batch size is 1.
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
roi = at.totensor(rois) / scale #把rois变回原图尺寸（待定）
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
mean = t.Tensor(self.loc_normalize_mean).cuda(). \
repeat(self.n_class)[None]
std = t.Tensor(self.loc_normalize_std).cuda(). \
repeat(self.n_class)[None]
#Q:看网上说ProposalCreator坐标归一化了所以这里要返回原图，但是我没看到。疑问
#A:我觉得"ProposalCreator坐标归一化了"这个有错误，这里要反归一化是因为训练的时候使用的loc归一化了(ProposalTargetCreator)，所以预测结果loc是归一化后的，并不是ProposalCreator时候归一化了
roi_cls_loc = (roi_cls_loc * std + mean) #坐标反归一化
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc) #一个框对应n_class个loc，所以要expand_as到同维度后面可以二次修正框
#二次修正框得到最后框
cls_bbox = loc2bbox(at.tonumpy(roi).reshape((-1, 4)),
at.tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = at.totensor(cls_bbox)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
# clip bounding box
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0]) #限制超出尺寸的框
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1]) #限制超出尺寸的框
#softmax得到每个框的类别概率
prob = at.tonumpy(F.softmax(at.totensor(roi_score), dim=1))
raw_cls_bbox = at.tonumpy(cls_bbox)
raw_prob = at.tonumpy(prob)
#输入框以及对应的类别概率，抑制输出
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
#输出坐标，类别，该类别概率
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate') #可视化内容，（跳过）
self.train() #返回train模式
return bboxes, labels, scores
def predict(self, imgs,sizes=None,visualize=False):
"""Detect objects from images.
从图像中检测物体
This method predicts objects for each image.
此方法预测每个图像的对象。
Args:
imgs (iterable of numpy.ndarray): Arrays holding images.
All images are in CHW and RGB format
and the range of their value is :math:`[0, 255]`.
Returns:
tuple of lists:
This method returns a tuple of three lists,
:obj:`(bboxes, labels, scores)`.
* **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
where :math:`R` is the number of bounding boxes in a image. \
Each bouding box is organized by \
:math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
in the second axis.
* **labels** : A list of integer arrays of shape :math:`(R,)`. \
Each value indicates the class of the bounding box. \
Values are in range :math:`[0, L - 1]`, where :math:`L` is the \
number of the foreground classes.
* **scores** : A list of float arrays of shape :math:`(R,)`. \
Each value indicates how confident the prediction is.
"""
#将模块设置为评估模式。这只对诸如Dropout或BatchNorm等模块有任何影响。module中的方法
self.eval()
#可视化
if visualize:
#设置为可视化 设置 self.nms_thresh = 0.3 self.score_thresh = 0.7
#评估模式 和 可视化模式 使用不同的nms最大化抑制 和阈值
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
# print('nei img shape is ', img.shape)
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list()
labels = list()
scores = list()
#size[600,800]
# print('sizes is ', sizes)
for img, size in zip(prepared_imgs, sizes):
#img由[3,600,800]转为[1，3,600,800] 转为变量，扩充一维 并设置为 预测模式
img = t.autograd.Variable(at.totensor(img).float()[None], volatile=True)
#scale 为1
scale = img.shape[3] / size[1]
roi_cls_loc, roi_scores, rois, _ = self(img, scale=scale)
# We are assuming that batch size is 1.
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
roi = at.totensor(rois) / scale
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
mean = t.Tensor(self.loc_normalize_mean).cuda(). \
repeat(self.n_class)[None]
std = t.Tensor(self.loc_normalize_std).cuda(). \
repeat(self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
cls_bbox = loc2bbox(at.tonumpy(roi).reshape((-1, 4)),
at.tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = at.totensor(cls_bbox)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
# clip bounding box
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = at.tonumpy(F.softmax(at.tovariable(roi_score), dim=1))
raw_cls_bbox = at.tonumpy(cls_bbox)
raw_prob = at.tonumpy(prob)
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate')
self.train()
return bboxes, labels, scores
def predict(self, imgs, sizes=None, visualize=False, prob_thre=0.7):
"""Detect objects from images.
This method predicts objects for each image.
Args:
imgs (iterable of numpy.ndarray): Arrays holding images.
All images are in CHW and RGB format
and the range of their value is :math:`[0, 255]`.
Returns:
tuple of lists:
This method returns a tuple of three lists,
:obj:`(bboxes, labels, scores)`.
* **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
where :math:`R` is the number of bounding boxes in a image. \
Each bouding box is organized by \
:math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
in the second axis.
* **labels** : A list of integer arrays of shape :math:`(R,)`. \
Each value indicates the class of the bounding box. \
Values are in range :math:`[0, L - 1]`, where :math:`L` is the \
number of the foreground classes.
* **scores** : A list of float arrays of shape :math:`(R,)`. \
Each value indicates how confident the prediction is.
"""
self.eval()
# sizes changes when visualize is set to different values
if visualize:
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:] # reshaped image size
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list()
labels = list()
scores = list()
for img, size in zip(prepared_imgs, sizes):
img = t.autograd.Variable(at.totensor(img).float()[None],
volatile=True)
# judge and change type if necessary
if t.is_tensor(size[1]):
size[1] = int(size[1])
if t.is_tensor(img.shape[3]):
img.shape[3] = int(img.shape[3])
scale = img.shape[3] / size[1]
(px, py), roi_scores, rois, search_regions, _ = self(img,
scale=scale)
# We are assuming that batch size is 1.
roi_score = roi_scores.data
px = px.data
py = py.data
roi = at.totensor(rois) / scale
search_regions = at.totensor(search_regions) / scale
# Convert to numpy array
px = at.tonumpy(px)
py = at.tonumpy(py)
search_regions = at.tonumpy(search_regions)
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
# use px, py and search_regions to generate boxes
cls_bbox = p2bbox(px, py, search_regions, threshold=prob_thre)
cls_bbox = at.totensor(cls_bbox)
# clip bounding box
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = at.tonumpy(F.softmax(at.tovariable(roi_score), dim=1))
raw_cls_bbox = at.tonumpy(cls_bbox)
raw_prob = at.tonumpy(prob)
# print("raw_cls_bbox shape : ", raw_cls_bbox.shape)
# print("raw_prob : ", raw_prob)
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate')
self.train()
return bboxes, labels, scores
def predict(self, imgs, sizes=None, visualize=False):
'''
对每张图片进行预测，
Args:
输入图片必须是CHW格式的RGB，是np.ndarry
Return:
返回的是一个tuple，包含：框的坐标，标签，得分
(bboxes,labels,scores)
'''
self.eval()
if visualize: #可视化
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:] #get width&height
#TODO:为什么可视化需要随机处理
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list()
labels = []
scores = []
for img, size in zip(prepared_imgs, sizes):
img = at.totensor(img[None]).float()
scale = img.shape[3] / size[1]
#TODO:调用forward函数，为什么可以这么调用
roi_cls_loc, roi_scores, rois, _ = self(img, scale=scale)
#TODO:.data是什么作用
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
roi = at.totensor(rois) / scale
mean = t.Tensor(self.loc_normalize_mean).cuda().repeat(
self.n_class)[None]
std = t.Tensor(self.loc_normalize_std).cuda().repeat(
self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
#TODO: 这个会有变形的作用吗
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
cls_bbox = loc2bbox(
at.tonumpy(roi).reshape((-1, 4)),
at.tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = at.totensor((cls_bbox))
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
'''clamp表示将tensor限制在其范围，让框不超过图片'''
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = at.tonumpy(F.softmax(at.totensor(roi_score), dim=1))
raw_cls_bbox = at.tonumpy(cls_bbox)
raw_prob = at.tonumpy(prob)
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate')
self.train()
return bboxes, labels, scores
def predict(self, imgs, sizes=None, visualize=False):
# 设置为eval模式
self.eval()
# 是否开启可视化
if visualize:
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list()
labels = list()
scores = list()
for img, size in zip(prepared_imgs, sizes):
img = at.totensor(img[None]).float()
# 对读入的图片求尺度scale，因为输入的图像经预处理就会有缩放，
# 所以需记录缩放因子scale，这个缩放因子在ProposalCreator
# 筛选roi时有用到，即将所有候选框按这个缩放因子映射回原图，
# 超出原图边框的区域将被截断。
scale = img.shape[3] / size[1]
# 执行forward
roi_cls_loc, roi_scores, rois, _ = self(img, scale=scale)
# We are assuming that batch size is 1.
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
roi = at.totensor(rois) / scale
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
# 为ProposalCreator对loc做了归一化（-mean /std）处理，所以这里
# 需要再*std+mean，此时的位置参数loc为roi_cls_loc。然后将这128
# 个roi利用roi_cls_loc进行微调，得到新的cls_bbox。
mean = t.Tensor(self.loc_normalize_mean).cuda(). \
repeat(self.n_class)[None]
std = t.Tensor(self.loc_normalize_std).cuda(). \
repeat(self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
# 解码过程
cls_bbox = loc2bbox(
at.tonumpy(roi).reshape((-1, 4)),
at.tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = at.totensor(cls_bbox)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
# clip bounding box
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
# 对于分类得分roi_scores，我们需要将其经过softmax后转为概率prob。
# 值得注意的是我们此时得到的是对所有输入128个roi以及位置参数、得分
# 的预处理，下面将筛选出最终的预测结果。
prob = at.tonumpy(F.softmax(at.totensor(roi_score), dim=1))
raw_cls_bbox = at.tonumpy(cls_bbox)
raw_prob = at.tonumpy(prob)
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate')
self.train()
return bboxes, labels, scores
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
if __name__ == '__main__':
from data.util import read_image
import cv2
cv_img = cv2.imread('/home/fengkai/dog.jpg')
src_img = read_image('/home/fengkai/dog.jpg')
from data.dataset import preprocess
img = preprocess(array_tool.tonumpy(src_img))
img = torch.from_numpy(img)[None]
C2, C3, C4, C5, = decom_resnet50()
c2_out = C2(img)
c3_out = C3(c2_out)
c4_out = C4(c3_out)
c5_out = C5(c4_out)
import numpy as np
from model.fpn import FPN
fpn = FPN(256)
p2, p3, p4, p5, p6 = fpn.forward(c2_out, c3_out, c4_out, c5_out)
rcnn_maps = [p2, p3, p4, p5]
feat_stride = [4, 8, 16, 32, 64]
spatial_scale = [1. / i for i in feat_stride]
for i, l in enumerate(range(2, 6)):
def predict(self, imgs, sizes=None, visualize=False):
"""
Detect objects from images.
This method predicts objects for each image.
"""
self.eval()
self.use_preset('evaluate')
if visualize:
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list()
labels = list()
scores = list()
for img, size in zip(prepared_imgs, sizes):
img = t.autograd.Variable(at.totensor(img).float()[None],
volatile=True)
scale = img.shape[3] / size[1]
roi_cls_loc, roi_scores, rois, _ = self(img, scale=scale)
# We are assuming that batch size is 1.
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
roi = at.totensor(rois) / scale
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
mean = t.Tensor(self.loc_normalize_mean).cuda(). \
repeat(self.n_class)[None]
std = t.Tensor(self.loc_normalize_std).cuda(). \
repeat(self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
cls_bbox = loc2bbox(
at.tonumpy(roi).reshape((-1, 4)),
at.tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = at.totensor(cls_bbox)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
# clip bounding box
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = at.tonumpy(F.softmax(at.tovariable(roi_score), dim=1))
raw_cls_bbox = at.tonumpy(cls_bbox)
raw_prob = at.tonumpy(prob)
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate')
self.train()
return bboxes, labels, scores
def extract(self, x, num_box):
num_batch = len(x)
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in x:
img = img.squeeze()
size = img.shape[1:]
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
roi_cls_locs = list()
roi_scores = list()
rpn_locs = list()
rpn_scores = list()
rois = list()
anchors = list()
features = t.zeros((num_batch, num_box, self.hidden_size))
scales = list()
hiddens = list()
for i, (img, size) in enumerate(zip(prepared_imgs, sizes)):
img = at.totensor(img[None]).float()
scale = img.shape[3] / size[1]
h = self.extractor(img)
rpn_loc, rpn_score, roi, roi_indices, anchor = \
self.rpn(h, size, scale)
roi_cls_loc_, roi_score_, feature = self.head(h, roi, roi_indices)
roi_score = F.softmax(at.totensor(roi_score_), dim=1)
# We are assuming that batch size is 1.
roi_score = roi_score.data
roi_cls_loc = roi_cls_loc_.data
roi = at.totensor(roi) / scale
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
mean = t.Tensor(self.loc_normalize_mean).cuda(). \
repeat(self.n_class)[None]
std = t.Tensor(self.loc_normalize_std).cuda(). \
repeat(self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
cls_bbox = loc2bbox(
at.tonumpy(roi).reshape((-1, 4)),
at.tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = at.totensor(cls_bbox)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
# clip bounding box
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = at.tonumpy(F.softmax(at.totensor(roi_score), dim=1))
raw_cls_bbox = at.tonumpy(cls_bbox)
raw_prob = at.tonumpy(prob)
bbox, label, score, feat = self._suppress_by_num(
raw_cls_bbox, raw_prob, feature, num_box)
features[i, :, :] = feat
roi_cls_locs.append(roi_cls_loc_)
roi_scores.append(roi_score_)
rpn_scores.append(rpn_score)
rpn_locs.append(rpn_loc)
rois.append(roi)
anchors.append(anchor)
scales.append(scale)
hiddens.append(h)
return features, roi_cls_locs, roi_scores, rpn_locs, rpn_scores, rois, anchors, scales, hiddens
ymin = 16 * float((-0.5 * affine[0] - 0.5 * affine[1] + affine[4]) +
c[0])
xmin = 16 * float((-0.5 * affine[3] - 0.5 * affine[2] + affine[5]) +
d[0])
ymax = 16 * float((0.5 * affine[0] + 0.5 * affine[1] + affine[4]) +
c[0])
xmax = 16 * float((0.5 * affine[3] + 0.5 * affine[2] + affine[5]) +
d[0])
bbox.append([xmin, ymin, xmax, ymax])
return bbox
if __name__ == "__main__":
#Prepare the test data
train_img, train_platetext, train_bbox, test_img, test_platetext, test_bbox = dataset.preprocess(
)
#Import the trained model
PD = PlateDetector()
PD.to(device)
PD.load_state_dict(torch.load('models/best_model.pt'))
#Generate output file ID
for file in os.listdir(f_img):
imgID = file.split('.')[0]
plate_indx.append(imgID)
bbox = WritePlate()
#Write to the xml file
for i, plate in enumerate(bbox):
"""
self.eval()
if visualize:
self.use_preset('visualize')
prepared_imgs = list()
<<<<<<< HEAD
prepared_imgs_depth = list()
=======
#prepared_imgs_depth = list()
>>>>>>> b43e1a358b5853ffb749ac931c9cd97a6dccf862
sizes = list()
#for img in imgs:
img=imgs
size = img.shape[1:]
<<<<<<< HEAD
img ,img_depth= preprocess(at.tonumpy(img),at.tonumpy(imgs_depth))
prepared_imgs.append(img)
prepared_imgs_depth.append(img_depth)
sizes.append(size)
else:
prepared_imgs = imgs
prepared_imgs_depth = imgs_depth
bboxes = list()
labels = list()
scores = list()
for img, img_depth, size in zip(prepared_imgs, prepared_imgs_depth, sizes):
img = t.autograd.Variable(at.totensor(img).float()[None], volatile=True)
img_depth = t.autograd.Variable(at.totensor(img_depth).float()[None], volatile=True)
scale = img.shape[3] / size[1]
roi_cls_loc, roi_scores, rois, _ = self(img, img_depth, scale=scale)
=======
def predict(self, imgs,sizes=None,visualize=False):
"""Detect objects from images.
This method predicts objects for each image.
Args:
imgs (iterable of numpy.ndarray): Arrays holding images.
All images are in CHW and RGB format
and the range of their value is :math:`[0, 255]`.
Returns:
tuple of lists:
This method returns a tuple of three lists,
:obj:`(bboxes, labels, scores)`.
* **bboxes**: A list of float arrays of shape :math:`(R, 4)`, \
where :math:`R` is the number of bounding boxes in a image. \
Each bouding box is organized by \
:math:`(y_{min}, x_{min}, y_{max}, x_{max})` \
in the second axis.
* **labels** : A list of integer arrays of shape :math:`(R,)`. \
Each value indicates the class of the bounding box. \
Values are in range :math:`[0, L - 1]`, where :math:`L` is the \
number of the foreground classes.
* **scores** : A list of float arrays of shape :math:`(R,)`. \
Each value indicates how confident the prediction is.
"""
self.eval()
if visualize:
self.use_preset('visualize')
prepared_imgs = list()
sizes = list()
for img in imgs:
size = img.shape[1:]
img = preprocess(at.tonumpy(img))
prepared_imgs.append(img)
sizes.append(size)
else:
prepared_imgs = imgs
bboxes = list()
labels = list()
scores = list()
for img, size in zip(prepared_imgs, sizes):
img = t.autograd.Variable(at.totensor(img).float()[None], volatile=True)
scale = img.shape[3] / size[1]
roi_cls_loc, roi_scores, rois, _ = self(img, scale=scale)
# We are assuming that batch size is 1.
roi_score = roi_scores.data
roi_cls_loc = roi_cls_loc.data
roi = at.totensor(rois) / scale
# Convert predictions to bounding boxes in image coordinates.
# Bounding boxes are scaled to the scale of the input images.
mean = t.Tensor(self.loc_normalize_mean).cuda(). \
repeat(self.n_class)[None]
std = t.Tensor(self.loc_normalize_std).cuda(). \
repeat(self.n_class)[None]
roi_cls_loc = (roi_cls_loc * std + mean)
roi_cls_loc = roi_cls_loc.view(-1, self.n_class, 4)
roi = roi.view(-1, 1, 4).expand_as(roi_cls_loc)
cls_bbox = loc2bbox(at.tonumpy(roi).reshape((-1, 4)),
at.tonumpy(roi_cls_loc).reshape((-1, 4)))
cls_bbox = at.totensor(cls_bbox)
cls_bbox = cls_bbox.view(-1, self.n_class * 4)
# clip bounding box
cls_bbox[:, 0::2] = (cls_bbox[:, 0::2]).clamp(min=0, max=size[0])
cls_bbox[:, 1::2] = (cls_bbox[:, 1::2]).clamp(min=0, max=size[1])
prob = at.tonumpy(F.softmax(at.tovariable(roi_score), dim=1))
raw_cls_bbox = at.tonumpy(cls_bbox)
raw_prob = at.tonumpy(prob)
bbox, label, score = self._suppress(raw_cls_bbox, raw_prob)
bboxes.append(bbox)
labels.append(label)
scores.append(score)
self.use_preset('evaluate')
self.train()
return bboxes, labels, scores