讲完了训练部分 接下来是检测部分
惯例看看结构
VOC_CLASS_BGR是不同类别应该用什么颜色画框容易区分，比如A用红色，B用绿色，不容易在途中颜色混在一起
画框框
def visualize_boxes(image_bgr, boxes, class_names, probs, name_bgr_dict=None, line_thickness=2):
if name_bgr_dict is None:
name_bgr_dict = VOC_CLASS_BGR
image_boxes = image_bgr.copy()#分配到新内存中去
for box, class_name, prob in zip(boxes, class_names, probs):
# Draw box on the image.
left_top, right_bottom = box
left, top = int(left_top[0]), int(left_top[1])
right, bottom = int(right_bottom[0]), int(right_bottom[1])
bgr = name_bgr_dict[class_name]
cv2.rectangle(image_boxes, (left, top), (right, bottom), bgr, thickness=line_thickness)
# Draw text on the image.
text = '%s %.2f' % (class_name, prob)
size, baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, thickness=2)
text_w, text_h = size
x, y = left, top
x1y1 = (x, y)
x2y2 = (x + text_w + line_thickness, y + text_h + line_thickness + baseline)
cv2.rectangle(image_boxes, x1y1, x2y2, bgr, -1)
cv2.putText(image_boxes, text, (x + line_thickness, y + 2*baseline + line_thickness),
cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.4, color=(255, 255, 255), thickness=1, lineType=8)
return image_boxes
传入计算好的box和class名和可能性的值 这里遍历画出来 此处四个值两个坐标全为以图片真实像素大小的值，不再是归一化
取出左上，右下坐标 cv2画出来，从name_bgr_dict中取出 本class应该对应的什么颜色 然后画框
接着做出文本，在以框左上角开始为起始坐标，往右下方向画小正方形填入种类名和概率， 就像这样
解析YOLODetector类
def __init__(self,
model_path, class_name_list=None, mean_rgb=[122.67891434, 116.66876762, 104.00698793],
conf_thresh=0.1, prob_thresh=0.1, nms_thresh=0.5,
gpu_id=0):
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
use_gpu = torch.cuda.is_available()
assert use_gpu, 'Current implementation does not support CPU mode. Enable CUDA.'
# Load YOLO model.
print("Loading YOLO model...")
self.yolo = resnet50()#这里就已经有了 随机的参数w权重
sd = torch.load(model_path)
self.yolo.load_state_dict(sd)#读取原来模型的权重
self.yolo.cuda()
print("Done loading!")
self.yolo.eval()
self.S = 7
self.B = 2
self.C = 20
self.class_name_list = class_name_list if (class_name_list is not None) else list(VOC_CLASS_BGR.keys())#给数据集里指定的list还是自己重新定义class list
assert len(self.class_name_list) == self.C
self.mean = np.array(mean_rgb, dtype=np.float32)
assert self.mean.shape == (3,)
self.conf_thresh = conf_thresh
self.prob_thresh = prob_thresh
self.nms_thresh = nms_thresh
self.to_tensor = transforms.ToTensor()
# Warm up. dummy_input 虚拟输入
dummy_input = Variable(torch.zeros((1, 3, 448, 448)))
dummy_input = dummy_input.cuda()
for i in range(3): #为了初始化权重？ 为什么 -预热操作的目的是让模型尽可能地填满加速器的缓存
self.yolo(dummy_input) #self.yolo.state_dict().get('conv1.weight')
yolo初始化模型，并读取训练好的model_path位置的权重，放入gpu
用dummy_input ，为gpu热身 先占满缓存不怕防止后面检测过程显存，内存或缓存爆了
def detect(self, image_bgr, image_size=448):
""" Detect objects from given image.
Args:
image_bgr: (numpy array) input image in BGR ids_sorted, sized [h, w, 3].
image_size: (int) image width and height to which input image is resized.
Returns:
boxes_detected: (list of tuple) box corner list like [((x1, y1), (x2, y2))_obj1, ...]. Re-scaled for original input image size.
class_names_detected: (list of str) list of class name for each detected boxe.
probs_detected: (list of float) list of probability(=confidence x class_score) for each detected box.
"""
h, w, _ = image_bgr.shape
img = cv2.resize(image_bgr, dsize=(image_size, image_size), interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # assuming the model is trained with RGB images.
img = (img - self.mean) / 255.0
img = self.to_tensor(img) # [image_size, image_size, 3] -> [3, image_size, image_size]
img = img[None, :, :, :] # [3, image_size, image_size] -> [1, 3, image_size, image_size]扩大维度 第一维是batch
img = Variable(img)
img = img.cuda()
with torch.no_grad():
pred_tensor = self.yolo(img)
pred_tensor = pred_tensor.cpu().data
pred_tensor = pred_tensor.squeeze(0) # squeeze batch dimension.
# Get detected boxes_detected, labels, confidences, class-scores.
boxes_normalized_all, class_labels_all, confidences_all, class_scores_all = self.decode(pred_tensor)
if boxes_normalized_all.size(0) == 0:
return [], [], [] # if no box found, return empty lists.
# Apply non maximum supression for boxes of each class.
boxes_normalized, class_labels, probs = [], [], []
for class_label in range(len(self.class_name_list)):
mask = (class_labels_all == class_label)
if torch.sum(mask) == 0:
continue # if no box found, skip that class.
# 找出所有同一类的 进行nms
boxes_normalized_masked = boxes_normalized_all[mask]
class_labels_maked = class_labels_all[mask]
confidences_masked = confidences_all[mask]
class_scores_masked = class_scores_all[mask]
ids = self.nms(boxes_normalized_masked, confidences_masked) #非极大抑制
boxes_normalized.append(boxes_normalized_masked[ids])
class_labels.append(class_labels_maked[ids])
probs.append(confidences_masked[ids] * class_scores_masked[ids])
boxes_normalized = torch.cat(boxes_normalized, 0)
class_labels = torch.cat(class_labels, 0)
probs = torch.cat(probs, 0)
# Postprocess for box, labels, probs.
boxes_detected, class_names_detected, probs_detected = [], [], []
for b in range(boxes_normalized.size(0)):
box_normalized = boxes_normalized[b]
class_label = class_labels[b]
prob = probs[b]
x1, x2 = w * box_normalized[0], w * box_normalized[2] # unnormalize x with image width. 图片真实坐标 从左上开始 0
y1, y2 = h * box_normalized[1], h * box_normalized[3] # unnormalize y with image height.
boxes_detected.append(((x1, y1), (x2, y2)))
class_label = int(class_label) # convert from LongTensor to int.
class_name = self.class_name_list[class_label]
class_names_detected.append(class_name)
prob = float(prob) # convert from Tensor to float.
probs_detected.append(prob)
return boxes_detected, class_names_detected, probs_detected
detect检测主函数：
因为opencv中读取出来是bgr，与rgb不一样 这是由于计算机视觉历史原因引起。后面cvt转成rgb
将图片uint8值先减去voc2007中的均值再归一化除以255， 减均值之后变成均值为0，/255 方差为1 符合正态分布
网络中输入大小应为(batch_size,3,448,448) 此处一张图片只有前3维，用None补一维度，放进网络输出预测detect时候的预测值 ，同时此时不需要计算梯度，提前设好no_grad 不然浪费机器计算性能。之后得到输出张量，并去掉第一维batch维
使用解码器decode解析网络预测输出的张量pred_tensor decode部分下面再讲 先看输出结果
得到4组预测出来的bbox 条件概率(假设含有物体条件下的该类别的概率)最高都是同一类别14 看txt文件可知是person 也就是人
接着以20个类别为循环条件开始循环，mask掩码，每次循环中看预测出来的是否再本次循环的类别中，有的话就赋予true，然后计算true里的类别内容 没有的话跳过
找出所有同一类的 进行nms，清除这些属于同一类别中，他们所有框的排列组合中iou过大，也可以视作重叠了，清除这些框，而比较的值是iou_threshold阈值
nms完事之后得到都不互相重叠的框的索引。加入汇总的list中。list再按行叠成张量便于后面返回给画框的部分。
最后的循环是按输出格式 重新格式化数据。
传入画框的地方需要四个值两个坐标全为以图片真实像素大小的值，不再是归一化。此处分别归一化乘对应的wh 变成实际大小
decode部分
def decode(self, pred_tensor):
""" Decode tensor into box coordinates, class labels, and probs_detected.
Args:
pred_tensor: (tensor) tensor to decode sized [S, S, 5 x B + C], 5=(x, y, w, h, conf)
Returns:
boxes: (tensor) [[x1, y1, x2, y2]_obj1, ...]. Normalized from 0.0 to 1.0 w.r.t. image width/height, sized [n_boxes, 4].
labels: (tensor) class labels for each detected boxe, sized [n_boxes,].
confidences: (tensor) objectness confidences for each detected box, sized [n_boxes,].
class_scores: (tensor) scores for most likely class for each detected box, sized [n_boxes,].
"""
S, B, C = self.S, self.B, self.C
boxes, labels, confidences, class_scores = [], [], [], []
cell_size = 1.0 / float(S)
#每个网格的置信度
conf = pred_tensor[:, :, 4].unsqueeze(2) # [S, S, 1]
for b in range(1, B):
conf = torch.cat((conf, pred_tensor[:, :, 5*b + 4].unsqueeze(2)), 2) #[S,S,2]
conf_mask = conf > self.conf_thresh # [S, S, B]
# TBM, further optimization may be possible by replacing the following for-loops with tensor operations.
for i in range(S): # for x-dimension.
for j in range(S): # for y-dimension.
class_score, class_label = torch.max(pred_tensor[j, i, 5*B:], 0) #找[j,i]网格的最大分类值
for b in range(B): #遍历两预测bbox
conf = pred_tensor[j, i, 5*b + 4]
prob = conf * class_score
if float(prob) < self.prob_thresh: #低于阈值门限继续
continue
# Compute box corner (x1, y1, x2, y2) from tensor.
box = pred_tensor[j, i, 5*b : 5*b + 4]
x0y0_normalized = torch.FloatTensor([i, j]) * cell_size # 该网格的坐上角归一化坐标
xy_normalized = box[:2] * cell_size + x0y0_normalized # 从对cell归一化的中心点位置还原出来 现在是对图片大小归一化
wh_normalized = box[2:] # 归一化的宽高
box_xyxy = torch.FloatTensor(4) # [4,]随便初始4个
box_xyxy[:2] = xy_normalized - 0.5 * wh_normalized # 归一化左上X-》应该是左下角角位置(x1, y1).
box_xyxy[2:] = xy_normalized + 0.5 * wh_normalized # 归一化右下X-》应该是有右上角角位置(x2, y2).
# Append result to the lists.
boxes.append(box_xyxy)
labels.append(class_label)
confidences.append(conf)
class_scores.append(class_score)
if len(boxes) > 0:
boxes = torch.stack(boxes, 0) # [n_boxes, 4] list转张量
labels = torch.stack(labels, 0) # [n_boxes, ]
confidences = torch.stack(confidences, 0) # [n_boxes, ]
class_scores = torch.stack(class_scores, 0) # [n_boxes, ]
else:
# If no box found, return empty tensors.
boxes = torch.FloatTensor(0, 4)
labels = torch.LongTensor(0)
confidences = torch.FloatTensor(0)
class_scores = torch.FloatTensor(0)
return boxes, labels, confidences, class_scores按照预测的张量返回四组数据 分别是box 标签（属于的类的下标） 置信度 类别条件概率
pred_tensor[:, :, 4]取出第一个框置信度，后面再加一维用来叠第二个框的置信度
代码中conf_mask没用到这里也不管了
按照像素进行循环取出每个像素对应最大的类别的下标和概率值
坐标计算顺序是：取出box四个值(cx,cy,w,h)，box中是相对该grid cell偏移并以cell归一化的xy偏移的真实框中心的值。这里转换成相对图片归一化的坐上，右下坐标值。
其中第三重遍历2(B)个框，小于实际概率的阈值全部取出，此处实际概率与conf * class_score比(置信度*类别条件概率，也就是此处有物体的概率*假如有物体的时候该类别的概率)
若概率符合要求我们取出这个框的四个数据， 加到四组汇总数据中。
四组list分别叠成张量形式返回
nms部分
def nms(self, boxes, scores):
""" Apply non maximum supression.
Args:
Returns:
"""
threshold = self.nms_thresh
x1 = boxes[:, 0] # [n,]
y1 = boxes[:, 1] # [n,]
x2 = boxes[:, 2] # [n,]
y2 = boxes[:, 3] # [n,]
areas = (x2 - x1) * (y2 - y1) # [n,]
_, ids_sorted = scores.sort(0, descending=True) # [n,]
ids = []
while ids_sorted.numel() > 0:
# Assume `ids_sorted` size is [m,] in the beginning of this iter.
#最后剩下一个的时候detach 脱离出tensor
i = ids_sorted.item() if (ids_sorted.numel() == 1) else ids_sorted[0]
ids.append(i)
if ids_sorted.numel() == 1:
break # If only one box is left (i.e., no box to supress), break.
inter_x1 = x1[ids_sorted[1:]].clamp(min=x1[i]) # [m-1, ]
inter_y1 = y1[ids_sorted[1:]].clamp(min=y1[i]) # [m-1, ]
inter_x2 = x2[ids_sorted[1:]].clamp(max=x2[i]) # [m-1, ] 画图就懂了
inter_y2 = y2[ids_sorted[1:]].clamp(max=y2[i]) # [m-1, ]
inter_w = (inter_x2 - inter_x1).clamp(min=0) # [m-1, ]
inter_h = (inter_y2 - inter_y1).clamp(min=0) # [m-1, ]
inters = inter_w * inter_h # intersections b/w/ box `i` and other boxes, sized [m-1, ].
unions = areas[i] + areas[ids_sorted[1:]] - inters # unions b/w/ box `i` and other boxes, sized [m-1, ].
ious = inters / unions # [m-1, ]
# Remove boxes whose IoU is higher than the threshold.#(ious <= threshold).nonzero() 形状(2,1)
ids_keep = (ious <= threshold).nonzero().squeeze() # [m-1, ]. Because `nonzero()` adds extra dimension, squeeze it.
if ids_keep.numel() == 0:
break # If no box left, break.
ids_sorted = ids_sorted[ids_keep+1] # `+1` is needed because `ids_sorted[0] = i`.
return torch.LongTensor(ids)
nms比较硬核，不懂需要先看李沐的13.4. 锚框 — 动手学深度学习 2.0.0 documentation 不过实现方式有些许不同
分别取出boxes中相对于图片归一化的左上右下坐标值。算出框面积
以分数（该类中的条件概率）按从大到小排序。inter的x1 x2 y1 y2分别对应需要比较的框的左下右上，
以分数做顺序基准，每次以第一个（下标0）
检查x1，与下标为0的x1做比较，若小于下标为0的x1则自动填充为下标为0的x1
检查y1，与下标为0的y1做比较，若小于下标为0的y1则自动填充为下标为0的y1
检查x2，与下标为0的x2做比较，若大于下标为0的x2则自动填充为下标为0的x2
检查y2，与下标为0的y2做比较，若大于下标为0的y2则自动填充为下标为0的y2
动手画图更容易懂
于是计算两者的w和h，此时两者框不重合 没有交集，w和h有一个就会为负，就会填充为0，于是iou必定为0，也就是表示无交集不可能重叠而保留，其余iou不为0部分与阈值比较，作为下一组的比较对象。同时比较基准放入ids，证明该框与筛选后的比较对象无重叠部分
就这样每次与其他剩余的框比较，筛选出所有互相不重叠或者iou阈值不足以认为是重叠的框返回回detect中处理
总
import torch
from torch.autograd import Variable
import torchvision.transforms as transforms
import os
import cv2
import numpy as np
from resnet_yolo import resnet50
# VOC class names and BGR color.
VOC_CLASS_BGR = {
'aeroplane': (128, 0, 0),
'bicycle': (0, 128, 0),
'bird': (128, 128, 0),
'boat': (0, 0, 128),
'bottle': (128, 0, 128),
'bus': (0, 128, 128),
'car': (128, 128, 128),
'cat': (64, 0, 0),
'chair': (192, 0, 0),
'cow': (64, 128, 0),
'diningtable': (192, 128, 0),
'dog': (64, 0, 128),
'horse': (192, 0, 128),
'motorbike': (64, 128, 128),
'person': (192, 128, 128),
'pottedplant': (0, 64, 0),
'sheep': (128, 64, 0),
'sofa': (0, 192, 0),
'train': (128, 192, 0),
'tvmonitor': (0, 64, 128)
}
def visualize_boxes(image_bgr, boxes, class_names, probs, name_bgr_dict=None, line_thickness=2):
if name_bgr_dict is None:
name_bgr_dict = VOC_CLASS_BGR
image_boxes = image_bgr.copy()#分配到新内存中去
for box, class_name, prob in zip(boxes, class_names, probs):
# Draw box on the image.
left_top, right_bottom = box
left, top = int(left_top[0]), int(left_top[1])
right, bottom = int(right_bottom[0]), int(right_bottom[1])
bgr = name_bgr_dict[class_name]
cv2.rectangle(image_boxes, (left, top), (right, bottom), bgr, thickness=line_thickness)
# Draw text on the image.
text = '%s %.2f' % (class_name, prob)
size, baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, thickness=2)
text_w, text_h = size
x, y = left, top
x1y1 = (x, y)
x2y2 = (x + text_w + line_thickness, y + text_h + line_thickness + baseline)
cv2.rectangle(image_boxes, x1y1, x2y2, bgr, -1)
cv2.putText(image_boxes, text, (x + line_thickness, y + 2*baseline + line_thickness),
cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.4, color=(255, 255, 255), thickness=1, lineType=8)
return image_boxes
class YOLODetector:
def __init__(self,
model_path, class_name_list=None, mean_rgb=[122.67891434, 116.66876762, 104.00698793],
conf_thresh=0.1, prob_thresh=0.1, nms_thresh=0.5,
gpu_id=0):
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
use_gpu = torch.cuda.is_available()
assert use_gpu, 'Current implementation does not support CPU mode. Enable CUDA.'
# Load YOLO model.
print("Loading YOLO model...")
self.yolo = resnet50()#这里就已经有了 随机的参数w权重
sd = torch.load(model_path)
self.yolo.load_state_dict(sd)#读取原来模型的权重
self.yolo.cuda()
print("Done loading!")
self.yolo.eval()
self.S = 7
self.B = 2
self.C = 20
self.class_name_list = class_name_list if (class_name_list is not None) else list(VOC_CLASS_BGR.keys())#给数据集里指定的list还是自己重新定义class list
assert len(self.class_name_list) == self.C
self.mean = np.array(mean_rgb, dtype=np.float32)
assert self.mean.shape == (3,)
self.conf_thresh = conf_thresh
self.prob_thresh = prob_thresh
self.nms_thresh = nms_thresh
self.to_tensor = transforms.ToTensor()
# Warm up. dummy_input 虚拟输入
dummy_input = Variable(torch.zeros((1, 3, 448, 448)))
dummy_input = dummy_input.cuda()
for i in range(3): #为了初始化权重？ 为什么 -预热操作的目的是让模型尽可能地填满加速器的缓存
self.yolo(dummy_input) #self.yolo.state_dict().get('conv1.weight')
def detect(self, image_bgr, image_size=448):
""" Detect objects from given image.
Args:
image_bgr: (numpy array) input image in BGR ids_sorted, sized [h, w, 3].
image_size: (int) image width and height to which input image is resized.
Returns:
boxes_detected: (list of tuple) box corner list like [((x1, y1), (x2, y2))_obj1, ...]. Re-scaled for original input image size.
class_names_detected: (list of str) list of class name for each detected boxe.
probs_detected: (list of float) list of probability(=confidence x class_score) for each detected box.
"""
h, w, _ = image_bgr.shape
img = cv2.resize(image_bgr, dsize=(image_size, image_size), interpolation=cv2.INTER_LINEAR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # assuming the model is trained with RGB images.
img = (img - self.mean) / 255.0
img = self.to_tensor(img) # [image_size, image_size, 3] -> [3, image_size, image_size]
img = img[None, :, :, :] # [3, image_size, image_size] -> [1, 3, image_size, image_size]扩大维度 第一维是batch
img = Variable(img)
img = img.cuda()
with torch.no_grad():
pred_tensor = self.yolo(img)
pred_tensor = pred_tensor.cpu().data
pred_tensor = pred_tensor.squeeze(0) # squeeze batch dimension.
# Get detected boxes_detected, labels, confidences, class-scores.
boxes_normalized_all, class_labels_all, confidences_all, class_scores_all = self.decode(pred_tensor)
if boxes_normalized_all.size(0) == 0:
return [], [], [] # if no box found, return empty lists.
# Apply non maximum supression for boxes of each class.
boxes_normalized, class_labels, probs = [], [], []
for class_label in range(len(self.class_name_list)):
mask = (class_labels_all == class_label)
if torch.sum(mask) == 0:
continue # if no box found, skip that class.
# 找出所有同一类的 进行nms
boxes_normalized_masked = boxes_normalized_all[mask]
class_labels_maked = class_labels_all[mask]
confidences_masked = confidences_all[mask]
class_scores_masked = class_scores_all[mask]
ids = self.nms(boxes_normalized_masked, confidences_masked) #非极大抑制
boxes_normalized.append(boxes_normalized_masked[ids])
class_labels.append(class_labels_maked[ids])
probs.append(confidences_masked[ids] * class_scores_masked[ids])
boxes_normalized = torch.cat(boxes_normalized, 0)
class_labels = torch.cat(class_labels, 0)
probs = torch.cat(probs, 0)
# Postprocess for box, labels, probs.
boxes_detected, class_names_detected, probs_detected = [], [], []
for b in range(boxes_normalized.size(0)):
box_normalized = boxes_normalized[b]
class_label = class_labels[b]
prob = probs[b]
x1, x2 = w * box_normalized[0], w * box_normalized[2] # unnormalize x with image width. 图片真实坐标 从左上开始 0
y1, y2 = h * box_normalized[1], h * box_normalized[3] # unnormalize y with image height.
boxes_detected.append(((x1, y1), (x2, y2)))
class_label = int(class_label) # convert from LongTensor to int.
class_name = self.class_name_list[class_label]
class_names_detected.append(class_name)
prob = float(prob) # convert from Tensor to float.
probs_detected.append(prob)
return boxes_detected, class_names_detected, probs_detected
def decode(self, pred_tensor):
""" Decode tensor into box coordinates, class labels, and probs_detected.
Args:
pred_tensor: (tensor) tensor to decode sized [S, S, 5 x B + C], 5=(x, y, w, h, conf)
Returns:
boxes: (tensor) [[x1, y1, x2, y2]_obj1, ...]. Normalized from 0.0 to 1.0 w.r.t. image width/height, sized [n_boxes, 4].
labels: (tensor) class labels for each detected boxe, sized [n_boxes,].
confidences: (tensor) objectness confidences for each detected box, sized [n_boxes,].
class_scores: (tensor) scores for most likely class for each detected box, sized [n_boxes,].
"""
S, B, C = self.S, self.B, self.C
boxes, labels, confidences, class_scores = [], [], [], []
cell_size = 1.0 / float(S)
#每个网格的置信度
conf = pred_tensor[:, :, 4].unsqueeze(2) # [S, S, 1]
for b in range(1, B):
conf = torch.cat((conf, pred_tensor[:, :, 5*b + 4].unsqueeze(2)), 2) #[S,S,2]
conf_mask = conf > self.conf_thresh # [S, S, B]
# TBM, further optimization may be possible by replacing the following for-loops with tensor operations.
for i in range(S): # for x-dimension.
for j in range(S): # for y-dimension.
class_score, class_label = torch.max(pred_tensor[j, i, 5*B:], 0) #找[j,i]网格的最大分类值
for b in range(B): #遍历两预测bbox
conf = pred_tensor[j, i, 5*b + 4]
prob = conf * class_score
if float(prob) < self.prob_thresh: #低于阈值门限继续
continue
# Compute box corner (x1, y1, x2, y2) from tensor.
box = pred_tensor[j, i, 5*b : 5*b + 4]
x0y0_normalized = torch.FloatTensor([i, j]) * cell_size # 该网格的坐上角归一化坐标
xy_normalized = box[:2] * cell_size + x0y0_normalized # 从对cell归一化的中心点位置还原出来 现在是对图片大小归一化
wh_normalized = box[2:] # 归一化的宽高
box_xyxy = torch.FloatTensor(4) # [4,]随便初始4个
box_xyxy[:2] = xy_normalized - 0.5 * wh_normalized # 归一化左上X-》应该是左下角角位置(x1, y1).
box_xyxy[2:] = xy_normalized + 0.5 * wh_normalized # 归一化右下X-》应该是有右上角角位置(x2, y2).
# Append result to the lists.
boxes.append(box_xyxy)
labels.append(class_label)
confidences.append(conf)
class_scores.append(class_score)
if len(boxes) > 0:
boxes = torch.stack(boxes, 0) # [n_boxes, 4] list转张量
labels = torch.stack(labels, 0) # [n_boxes, ]
confidences = torch.stack(confidences, 0) # [n_boxes, ]
class_scores = torch.stack(class_scores, 0) # [n_boxes, ]
else:
# If no box found, return empty tensors.
boxes = torch.FloatTensor(0, 4)
labels = torch.LongTensor(0)
confidences = torch.FloatTensor(0)
class_scores = torch.FloatTensor(0)
return boxes, labels, confidences, class_scores
def nms(self, boxes, scores):
""" Apply non maximum supression.
Args:
Returns:
"""
threshold = self.nms_thresh
x1 = boxes[:, 0] # [n,]
y1 = boxes[:, 1] # [n,]
x2 = boxes[:, 2] # [n,]
y2 = boxes[:, 3] # [n,]
areas = (x2 - x1) * (y2 - y1) # [n,]
_, ids_sorted = scores.sort(0, descending=True) # [n,]
ids = []
while ids_sorted.numel() > 0:
# Assume `ids_sorted` size is [m,] in the beginning of this iter.
#最后剩下一个的时候detach 脱离出tensor
i = ids_sorted.item() if (ids_sorted.numel() == 1) else ids_sorted[0]
ids.append(i)
if ids_sorted.numel() == 1:
break # If only one box is left (i.e., no box to supress), break.
inter_x1 = x1[ids_sorted[1:]].clamp(min=x1[i]) # [m-1, ]
inter_y1 = y1[ids_sorted[1:]].clamp(min=y1[i]) # [m-1, ]
inter_x2 = x2[ids_sorted[1:]].clamp(max=x2[i]) # [m-1, ] 画图就懂了
inter_y2 = y2[ids_sorted[1:]].clamp(max=y2[i]) # [m-1, ]
inter_w = (inter_x2 - inter_x1).clamp(min=0) # [m-1, ]
inter_h = (inter_y2 - inter_y1).clamp(min=0) # [m-1, ]
inters = inter_w * inter_h # intersections b/w/ box `i` and other boxes, sized [m-1, ].
unions = areas[i] + areas[ids_sorted[1:]] - inters # unions b/w/ box `i` and other boxes, sized [m-1, ].
ious = inters / unions # [m-1, ]
# Remove boxes whose IoU is higher than the threshold.#(ious <= threshold).nonzero() 形状(2,1)
ids_keep = (ious <= threshold).nonzero().squeeze() # [m-1, ]. Because `nonzero()` adds extra dimension, squeeze it.
if ids_keep.numel() == 0:
break # If no box left, break.
ids_sorted = ids_sorted[ids_keep+1] # `+1` is needed because `ids_sorted[0] = i`.
return torch.LongTensor(ids)
if __name__ == '__main__':
# Paths to input/output images.
image_path = '000369.jpg'
out_path = 'result.png'
# Path to the yolo weight.
model_path = 'weights/model_best.pth'
# GPU device on which yolo is loaded.
gpu_id = 0
# Load model.
yolo = YOLODetector(model_path, gpu_id=gpu_id, conf_thresh=0.15, prob_thresh=0.45, nms_thresh=0.35)
# Load image.
image = cv2.imread(image_path)#某些老的图像处理软件使用的是 BGR 格式，因此 OpenCV 采用 BGR 格式可以与这些软件兼容。
# Detect objects.
boxes, class_names, probs = yolo.detect(image)
# Visualize.
image_boxes = visualize_boxes(image, boxes, class_names, probs)
# Output detection result as an image.
cv2.imwrite(out_path, image_boxes)