Pytorch版本yolov3源碼閱讀
目錄
1. 閱讀test.py
1.1 參數解讀
parser = argparse.ArgumentParser()
parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file')
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.pt', help='path to weights file')
parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
parser.add_argument('-iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
parser.add_argument('-conf_thres', type=float, default=0.5, help='object confidence threshold')
parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
parser.add_argument('-n_cpu', type=int, default=0, help='number of cpu threads to use during batch generation')
parser.add_argument('-img_size', type=int, default=608, help='size of each image dimension')
opt = parser.parse_args()
print(opt)
- batch_size: 每個batch大小，跟darknet不太一樣，沒有subdivision
- cfg： 網絡配置文件
- data_config_path: coco.data文件，存儲相關信息
- weights_path: 權重文件路徑
- class_path: 類別文件，注意類別的順序，coco.names
- iou_thres: iou閾值
- conf_thres: 目標執行度閾值
- nms_thres: 非極大抑制閾值
- n_cpu: 實用多少個線程來創建batch
- img_size: 設置初始圖片大小
1.2 data文件解析
def parse_data_config(path):
"""Parses the data configuration file"""
options = dict()
options['gpus'] = '0,1'
options['num_workers'] = '10'
with open(path, 'r') as fp:
lines = fp.readlines()
for line in lines:
line = line.strip()
if line == '' or line.startswith('#'):
continue
key, value = line.split('=')
options[key.strip()] = value.strip()
return options
將data文件中內容存儲到options這個dict中，獲取的時候就可以對這個對象通過key進行提取value。
1.3 cfg文件解析
def parse_model_config(path):
"""Parses the yolo-v3 layer configuration file and returns module definitions"""
file = open(path, 'r')
lines = file.read().split('\n')
lines = [x for x in lines if x and not x.startswith('#')]
lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces
module_defs = []
for line in lines:
if line.startswith('['): # This marks the start of a new block
module_defs.append({})
module_defs[-1]['type'] = line[1:-1].rstrip()
if module_defs[-1]['type'] == 'convolutional':
module_defs[-1]['batch_normalize'] = 0
else:
key, value = line.split("=")
value = value.strip()
module_defs[-1][key.rstrip()] = value.strip()
return module_defs
返回的module_defs存儲的是所有的網絡參數信息，一個list中套了很多個dict.
1.4 根據cfg文件創建模塊
def create_modules(module_defs):
"""
Constructs module list of layer blocks from module configuration in module_defs
"""
#將第一層內容，也就是網絡超參數設定
hyperparams = module_defs.pop(0)
output_filters = [int(hyperparams['channels'])]
module_list = nn.ModuleList()
for i, module_def in enumerate(module_defs):
#一個時序容器。`Modules` 會以他們傳入的順序被添加到容器中。當然，也可以傳入一個`OrderedDict`
modules = nn.Sequential()
#根據不同的層進行不同的設計
if module_def['type'] == 'convolutional':
bn = int(module_def['batch_normalize'])
filters = int(module_def['filters'])
kernel_size = int(module_def['size'])
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
#將一個 `child module` 添加到當前 `modle`。 被添加的`module`可以通過 `name`屬性來獲取。
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
out_channels=filters,
kernel_size=kernel_size,
stride=int(module_def['stride']),
padding=pad,
bias=not bn))
if bn:
modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters))
if module_def['activation'] == 'leaky':
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
elif module_def['type'] == 'upsample':
# pytorch中的上采樣函數
upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest')
modules.add_module('upsample_%d' % i, upsample)
elif module_def['type'] == 'route':
# 對yolo cfg文件中的route層進行解析
# eg: route -1, 14
layers = [int(x) for x in module_def['layers'].split(',')]
# 將多個層進行以sum的形式合並
# 這個地方發現與darknet中不同，darknet中是以concate的方式進行的
filters = sum([output_filters[layer_i] for layer_i in layers])
modules.add_module('route_%d' % i, EmptyLayer())
elif module_def['type'] == 'shortcut':
# eg from yolov3.cfg
# from=-3
# activation = linear
# 未定義activation方式？？？
filters = output_filters[int(module_def['from'])]
modules.add_module('shortcut_%d' % i, EmptyLayer())
elif module_def['type'] == 'yolo':
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
# Extract anchors
anchors = [float(x) for x in module_def['anchors'].split(',')]
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
anchors = [anchors[i] for i in anchor_idxs]
num_classes = int(module_def['classes'])
img_height = int(hyperparams['height'])
# Define detection layer
yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs)
modules.add_module('yolo_%d' % i, yolo_layer)
# Register module list and number of output filters
# 將module添加到module_list中進行保存
module_list.append(modules)
output_filters.append(filters)
return hyperparams, module_list
這里開始就涉及到pytorch部分的內容了：
- module_list = nn.ModuleList(): 創建一個list，其中存放的是module
- nn.Sequential(): 一個時序容器。
Modules會以他們傳入的順序被添加到容器中。當然，也可以傳入一個OrderedDict。 - add_module(name,module):將一個
child module添加到當前modle。 被添加的module可以通過name屬性來獲取。
1.5 YOLOLayer
class YOLOLayer(nn.Module):
def __init__(self, anchors, nC, img_dim, anchor_idxs):
super(YOLOLayer, self).__init__()
anchors = [(a_w, a_h) for a_w, a_h in anchors] # (pixels)
nA = len(anchors)
self.anchors = anchors
self.nA = nA # number of anchors (3)
self.nC = nC # number of classes (80)
self.bbox_attrs = 5 + nC
self.img_dim = img_dim # from hyperparams in cfg file, NOT from parser
if anchor_idxs[0] == (nA * 2): # 6
stride = 32
elif anchor_idxs[0] == nA: # 3
stride = 16
else:
stride = 8
# Build anchor grids
nG = int(self.img_dim / stride)
self.grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).float()
self.grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).float()
self.scaled_anchors = torch.FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in anchors])
self.anchor_w = self.scaled_anchors[:, 0:1].view((1, nA, 1, 1))
self.anchor_h = self.scaled_anchors[:, 1:2].view((1, nA, 1, 1))
def forward(self, p, targets=None, requestPrecision=False):
FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
bs = p.shape[0] # batch size
nG = p.shape[2] # number of grid points
stride = self.img_dim / nG
if p.is_cuda and not self.grid_x.is_cuda:
self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda()
self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda()
# p.view(12, 255, 13, 13) -- > (12, 3, 13, 13, 80) # (bs, anchors, grid, grid, classes + xywh)
p = p.view(bs, self.nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
# Get outputs
x = torch.sigmoid(p[..., 0]) # Center x
y = torch.sigmoid(p[..., 1]) # Center y
# Width and height (yolo method)
w = p[..., 2] # Width
h = p[..., 3] # Height
width = torch.exp(w.data) * self.anchor_w
height = torch.exp(h.data) * self.anchor_h
# Width and height (power method)
# w = torch.sigmoid(p[..., 2]) # Width
# h = torch.sigmoid(p[..., 3]) # Height
# width = ((w.data * 2) ** 2) * self.anchor_w
# height = ((h.data * 2) ** 2) * self.anchor_h
# Add offset and scale with anchors (in grid space, i.e. 0-13)
pred_boxes = FT(bs, self.nA, nG, nG, 4)
pred_conf = p[..., 4] # Conf
pred_cls = p[..., 5:] # Class
# Training
if targets is not None:
MSELoss = nn.MSELoss(size_average=True)
BCEWithLogitsLoss = nn.BCEWithLogitsLoss(size_average=True)
CrossEntropyLoss = nn.CrossEntropyLoss()
if requestPrecision:
gx = self.grid_x[:, :, :nG, :nG]
gy = self.grid_y[:, :, :nG, :nG]
pred_boxes[..., 0] = x.data + gx - width / 2
pred_boxes[..., 1] = y.data + gy - height / 2
pred_boxes[..., 2] = x.data + gx + width / 2
pred_boxes[..., 3] = y.data + gy + height / 2
tx, ty, tw, th, mask, tcls, TP, FP, FN, TC = \
build_targets(pred_boxes, pred_conf, pred_cls, targets, self.scaled_anchors, self.nA, self.nC, nG,
requestPrecision)
tcls = tcls[mask]
if x.is_cuda:
tx, ty, tw, th, mask, tcls = tx.cuda(), ty.cuda(), tw.cuda(), th.cuda(), mask.cuda(), tcls.cuda()
# Mask outputs to ignore non-existing objects (but keep confidence predictions)
nT = sum([len(x) for x in targets]) # number of targets
nM = mask.sum().float() # number of anchors (assigned to targets)
nB = len(targets) # batch size
k = nM / nB
if nM > 0:
lx = k * MSELoss(x[mask], tx[mask])
ly = k * MSELoss(y[mask], ty[mask])
lw = k * MSELoss(w[mask], tw[mask])
lh = k * MSELoss(h[mask], th[mask])
# lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float())
lconf = k * BCEWithLogitsLoss(pred_conf, mask.float())
lcls = k * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
# lcls = k * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
else:
lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
# Add confidence loss for background anchors (noobj)
#lconf += k * BCEWithLogitsLoss(pred_conf[~mask], mask[~mask].float())
# Sum loss components
loss = lx + ly + lw + lh + lconf + lcls
# Sum False Positives from unassigned anchors
i = torch.sigmoid(pred_conf[~mask]) > 0.9
if i.sum() > 0:
FP_classes = torch.argmax(pred_cls[~mask][i], 1)
FPe = torch.bincount(FP_classes, minlength=self.nC).float().cpu() # extra FPs
else:
FPe = torch.zeros(self.nC)
return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \
nT, TP, FP, FPe, FN, TC
else:
pred_boxes[..., 0] = x.data + self.grid_x
pred_boxes[..., 1] = y.data + self.grid_y
pred_boxes[..., 2] = width
pred_boxes[..., 3] = height
# If not in training phase return predictions
output = torch.cat((pred_boxes.view(bs, -1, 4) * stride,
torch.sigmoid(pred_conf.view(bs, -1, 1)), pred_cls.view(bs, -1, self.nC)), -1)
return output.data
暫且放到這里，之后在做解析
1.6 初始化模型
model = Darknet(opt.cfg, opt.img_size)
轉到定義：
class Darknet(nn.Module):
"""YOLOv3 object detection model"""
def __init__(self, config_path, img_size=416):
super(Darknet, self).__init__()
self.module_defs = parse_model_config(config_path)
self.module_defs[0]['height'] = img_size
self.hyperparams, self.module_list = create_modules(self.module_defs)
self.img_size = img_size
self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nT', 'TP', 'FP', 'FPe', 'FN', 'TC']
def forward(self, x, targets=None, requestPrecision=False):
is_training = targets is not None
output = []
self.losses = defaultdict(float)
layer_outputs = []
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
if module_def['type'] in ['convolutional', 'upsample']:
x = module(x)
elif module_def['type'] == 'route':
layer_i = [int(x) for x in module_def['layers'].split(',')]
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
elif module_def['type'] == 'shortcut':
layer_i = int(module_def['from'])
x = layer_outputs[-1] + layer_outputs[layer_i]
elif module_def['type'] == 'yolo':
# Train phase: get loss
if is_training:
x, *losses = module[0](x, targets, requestPrecision)
for name, loss in zip(self.loss_names, losses):
self.losses[name] += loss
# Test phase: Get detections
else:
x = module(x)
output.append(x)
layer_outputs.append(x)
if is_training:
self.losses['nT'] /= 3
self.losses['TC'] /= 3
metrics = torch.zeros(4, len(self.losses['FPe'])) # TP, FP, FN, target_count
ui = np.unique(self.losses['TC'])[1:]
for i in ui:
j = self.losses['TC'] == float(i)
metrics[0, i] = (self.losses['TP'][j] > 0).sum().float() # TP
metrics[1, i] = (self.losses['FP'][j] > 0).sum().float() # FP
metrics[2, i] = (self.losses['FN'][j] == 3).sum().float() # FN
metrics[3] = metrics.sum(0)
metrics[1] += self.losses['FPe']
self.losses['TP'] = metrics[0].sum()
self.losses['FP'] = metrics[1].sum()
self.losses['FN'] = metrics[2].sum()
self.losses['TC'] = 0
self.losses['metrics'] = metrics
return sum(output) if is_training else torch.cat(output, 1)
梳理一下屬性值，以便更好理解：
- module_def: dict類型，存儲cfg文件中
- hyperparams: 超參數，整個網絡需要的參數被存儲到改屬性中
- module_list：整個網絡所有的模型加載到pytorch中的nn.ModuleList()
- loss_names: 有必要理解一下這里的loss中參數的含義
- loss
- x,y,w,h
- conf
- cls
- nT
- TP,FP,FPe,FN,TC
loss參數含義還不是很明白，留坑，待填坑
1.7 加載權重
都知道，pytorch版的yolov3權重文件是.pt結尾的，darknet版本的yolov3權重文件是.weights結尾的。
所以得知了這個版本可以使用加載weights文件。
# Load weights
if opt.weights_path.endswith('.weights'): # darknet format
load_weights(model, opt.weights_path)
elif opt.weights_path.endswith('.pt'): # pytorch format
checkpoint = torch.load(opt.weights_path, map_location='cpu')
model.load_state_dict(checkpoint['model'])
del checkpoint
1.8 計算mAP
print('Compute mAP...')
correct = 0
targets = None
outputs, mAPs, TP, confidence, pred_class, target_class = [], [], [], [], [], []
for batch_i, (imgs, targets) in enumerate(dataloader):
imgs = imgs.to(device)
with torch.no_grad():
output = model(imgs)
output = non_max_suppression(output, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres)
# Compute average precision for each sample
for sample_i in range(len(targets)):
correct = []
# Get labels for sample where width is not zero (dummies)
annotations = targets[sample_i]
# Extract detections
detections = output[sample_i]
if detections is None:
# If there are no detections but there are annotations mask as zero AP
if annotations.size(0) != 0:
mAPs.append(0)
continue
# Get detections sorted by decreasing confidence scores
detections = detections[np.argsort(-detections[:, 4])]
# If no annotations add number of detections as incorrect
if annotations.size(0) == 0:
target_cls = []
#correct.extend([0 for _ in range(len(detections))])
mAPs.append(0)
continue
else:
target_cls = annotations[:, 0]
# Extract target boxes as (x1, y1, x2, y2)
target_boxes = xywh2xyxy(annotations[:, 1:5])
target_boxes *= opt.img_size
detected = []
for *pred_bbox, conf, obj_conf, obj_pred in detections:
pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
# Compute iou with target boxes
iou = bbox_iou(pred_bbox, target_boxes)
# Extract index of largest overlap
best_i = np.argmax(iou)
# If overlap exceeds threshold and classification is correct mark as correct
if iou[best_i] > opt.iou_thres and obj_pred == annotations[best_i, 0] and best_i not in detected:
correct.append(1)
detected.append(best_i)
else:
correct.append(0)
# Compute Average Precision (AP) per class
AP = ap_per_class(tp=correct, conf=detections[:, 4], pred_cls=detections[:, 6], target_cls=target_cls)
# Compute mean AP for this image
mAP = AP.mean()
# Append image mAP to list
mAPs.append(mAP)
# Print image mAP and running mean mAP
print('+ Sample [%d/%d] AP: %.4f (%.4f)' % (len(mAPs), len(dataloader) * opt.batch_size, mAP, np.mean(mAPs)))
print('Mean Average Precision: %.4f' % np.mean(mAPs))
留坑，待填
2. 閱讀train.py
2.1 參數解讀
parser = argparse.ArgumentParser()
parser.add_argument('-epochs', type=int, default=68, help='number of epochs')
parser.add_argument('-batch_size', type=int, default=12, help='size of each image batch')
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='data config file path')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
parser.add_argument('-resume', default=False, help='resume training flag')
opt = parser.parse_args()
print(opt)
- epochs 設置循環的參數
- batch_size: 設置batch
- data_config_path: data文件位置
- cfg: 記錄cfg文件的位置
- img_size: 設置圖片大小
- resume: 是否恢復訓練（True or False）
2.2 隨機初始化
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
if cuda:
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
torch.backends.cudnn.benchmark = True
2.3 設置優化器
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3,momentum=.9, weight_decay=5e-4, nesterov=True)
使用SGD優化器，learning_rate=0.001,momentum=0.9,weight_decay=5e-4,使用nesterov動量
2.4 更新優化器
根據當前epoch來確定使用哪一個lr:
# Update scheduler (automatic)
# scheduler.step()
# Update scheduler (manual)
if epoch < 54:
lr = 1e-3
elif epoch < 61:
lr = 1e-4
else:
lr = 1e-5
for g in optimizer.param_groups:
g['lr'] = lr
可以自動更新參數，也可以手工更新參數。
2.5 loss指標
- mean_precision:
# Precision
precision = metrics[0] / (metrics[0] + metrics[1] + 1e-16)
k = (metrics[0] + metrics[1]) > 0
if k.sum() > 0:
mean_precision = precision[k].mean()
else:
mean_precision = 0
- mean_recall:
# Recall
recall = metrics[0] / (metrics[0] + metrics[2] + 1e-16)
k = (metrics[0] + metrics[2]) > 0
if k.sum() > 0:
mean_recall = recall[k].mean()
else:
mean_recall = 0
然后將所有指標寫到results.txt文件中
2.6 checkpoint相關
checkpoint參數：epoch, best_loss,model,optimizer
latest.pt: 最新的權重文件
best.pt: 當前最好的權重文件
# Save latest checkpoint
checkpoint = {'epoch': epoch,
'best_loss': best_loss,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()}
torch.save(checkpoint, 'checkpoints/latest.pt')
# Save best checkpoint
if best_loss == loss_per_target:
os.system('cp checkpoints/latest.pt checkpoints/best.pt')
# Save backup checkpoint
if (epoch > 0) & (epoch % 5 == 0):
os.system('cp checkpoints/latest.pt checkpoints/backup' + str(epoch) + '.pt')
3. 閱讀detect.py
3.1 參數解讀
parser.add_argument('-image_folder', type=str, default='data/samples', help='path to images')
parser.add_argument('-output_folder', type=str, default='output', help='path to outputs')
parser.add_argument('-plot_flag', type=bool, default=True)
parser.add_argument('-txt_out', type=bool, default=False)
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
parser.add_argument('-conf_thres', type=float, default=0.50, help='object confidence threshold')
parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
parser.add_argument('-batch_size', type=int, default=1, help='size of the batches')
parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
opt = parser.parse_args()
print(opt)
- image_folder: data/samples, 待檢測的圖片的文件夾
- output_folder: output,結果輸出文件
- plot_flag: True or False, 添加bbox, 保存圖片
- txt_out: True or False, 是否保存圖片檢測結果
- cfg: cfg文件路徑
- class_path: 類別名稱文件位置
- conf_thres, nms_thres: 目標檢測置信度，非極大抑制閾值
- batch_size: 一般設置為1，選用默認的即可
- img_size: 設置加載圖片時候的圖片大小
3.2 預測框的獲取
# Get detections
with torch.no_grad():
chip = torch.from_numpy(img).unsqueeze(0).to(device)
pred = model(chip)
pred = pred[pred[:, :, 4] > opt.conf_thres]
if len(pred) > 0:
detections = non_max_suppression(pred.unsqueeze(0), opt.conf_thres, opt.nms_thres)
img_detections.extend(detections)
imgs.extend(img_paths)
獲取預測框，非極大值抑制。
3.2 核心-迭代圖片畫出預測框
# Iterate through images and save plot of detections
for img_i, (path, detections) in enumerate(zip(imgs, img_detections)):
print("image %g: '%s'" % (img_i, path))
if opt.plot_flag:
img = cv2.imread(path)
# The amount of padding that was added
pad_x = max(img.shape[0] - img.shape[1], 0) * (opt.img_size / max(img.shape))
pad_y = max(img.shape[1] - img.shape[0], 0) * (opt.img_size / max(img.shape))
# Image height and width after padding is removed
unpad_h = opt.img_size - pad_y
unpad_w = opt.img_size - pad_x
# Draw bounding boxes and labels of detections
if detections is not None:
unique_classes = detections[:, -1].cpu().unique()
bbox_colors = random.sample(color_list, len(unique_classes))
# write results to .txt file
results_img_path = os.path.join(opt.output_folder, path.split('/')[-1])
results_txt_path = results_img_path + '.txt'
if os.path.isfile(results_txt_path):
os.remove(results_txt_path)
for i in unique_classes:
n = (detections[:, -1].cpu() == i).sum()
print('%g %ss' % (n, classes[int(i)]))
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
# Rescale coordinates to original dimensions
box_h = ((y2 - y1) / unpad_h) * img.shape[0]
box_w = ((x2 - x1) / unpad_w) * img.shape[1]
y1 = (((y1 - pad_y // 2) / unpad_h) * img.shape[0]).round().item()
x1 = (((x1 - pad_x // 2) / unpad_w) * img.shape[1]).round().item()
x2 = (x1 + box_w).round().item()
y2 = (y1 + box_h).round().item()
x1, y1, x2, y2 = max(x1, 0), max(y1, 0), max(x2, 0), max(y2, 0)
# write to file
if opt.txt_out:
with open(results_txt_path, 'a') as file:
file.write(('%g %g %g %g %g %g \n') % (x1, y1, x2, y2, cls_pred, cls_conf * conf))
if opt.plot_flag:
# Add the bbox to the plot
label = '%s %.2f' % (classes[int(cls_pred)], conf)
color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
plot_one_box([x1, y1, x2, y2], img, label=label, color=color)
if opt.plot_flag:
# Save generated image with detections
cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img)