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yolo.py
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yolo.py
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import colorsys
import os
import time
import numpy as np
import torch
import torch.nn as nn
from PIL import ImageDraw, ImageFont
import cv2
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from PIL import Image
from nets.yolo import YoloBody
from utils.utils import (cvtColor, get_classes, preprocess_input, resize_image,
show_config)
from utils.utils_bbox import decode_outputs, non_max_suppression
'''
训练自己的数据集必看注释!
'''
class YOLO(object):
_defaults = {
# --------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path!
# model_path指向logs文件夹下的权值文件,classes_path指向model_data下的txt
#
# 训练好后logs文件夹下存在多个权值文件,选择验证集损失较低的即可。
# 验证集损失较低不代表mAP较高,仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配,同时要注意训练时的model_path和classes_path参数的修改
# --------------------------------------------------------------------------#
"model_path": '/data/newnas/zhangHY/ToothData661s/s_model_logs/best_epoch_weights.pth',
"classes_path": 'model_data/voc_classes.txt',
# ---------------------------------------------------------------------#
# 输入图片的大小,必须为32的倍数。
# ---------------------------------------------------------------------#
"input_shape": [640, 640],
# ---------------------------------------------------------------------#
# 所使用的YoloX的版本。nano、tiny、s、m、l、x
# ---------------------------------------------------------------------#
"phi": 's',
# ---------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
# ---------------------------------------------------------------------#
"confidence": 0.5,
# ---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
# ---------------------------------------------------------------------#
"nms_iou": 0.3,
# ---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize,
# 在多次测试后,发现关闭letterbox_image直接resize的效果更好
# ---------------------------------------------------------------------#
"letterbox_image": True,
# -------------------------------#
# 是否使用Cuda
# 没有GPU可以设置成False
# -------------------------------#
"cuda": True,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
self._defaults[name] = value
self.class_names, self.num_classes = get_classes(self.classes_path)
hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
self.generate()
show_config(**self._defaults)
def generate(self, onnx=False):
self.net = YoloBody(self.num_classes, self.phi)
self.net.to(self.device)
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.net.load_state_dict(torch.load(self.model_path, map_location=self.device))
self.net = self.net.eval()
print('{} model, and classes loaded.'.format(self.model_path))
if not onnx:
if self.cuda:
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
def detect_image(self, image, crop=False, count=False):
image_shape = np.array(np.shape(image)[0:2])
image = cvtColor(image)
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.to(self.device)
outputs = self.net(images)
outputs = decode_outputs(outputs, self.input_shape)
results = non_max_suppression(outputs, self.num_classes, self.input_shape,
image_shape, self.letterbox_image, conf_thres=self.confidence,
nms_thres=self.nms_iou)
if results[0] is None:
return image
top_label = np.array(results[0][:, 6], dtype='int32')
top_conf = results[0][:, 4] * results[0][:, 5]
top_boxes = results[0][:, :4]
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = int(max((image.size[0] + image.size[1]) // np.mean(self.input_shape), 1))
if count:
print("top_label:", top_label)
classes_nums = np.zeros([self.num_classes])
for i in range(self.num_classes):
num = np.sum(top_label == i)
if num > 0:
print(self.class_names[i], " : ", num)
classes_nums[i] = num
print("classes_nums:", classes_nums)
if crop:
for i, c in list(enumerate(top_label)):
top, left, bottom, right = top_boxes[i]
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
dir_save_path = "img_crop"
if not os.path.exists(dir_save_path):
os.makedirs(dir_save_path)
crop_image = image.crop([left, top, right, bottom])
crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0)
print("save crop_" + str(i) + ".png to " + dir_save_path)
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label, top, left, bottom, right)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)
del draw
return image
def detect_bbox(self, image, crop=False, count=False):
image_shape = np.array(np.shape(image)[0:2])
image = cvtColor(image)
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
outputs = decode_outputs(outputs, self.input_shape)
results = non_max_suppression(outputs, self.num_classes, self.input_shape,
image_shape, self.letterbox_image, conf_thres=self.confidence,
nms_thres=self.nms_iou)
if results[0] is None:
return []
top_label = np.array(results[0][:, 6], dtype='int32')
top_conf = results[0][:, 4] * results[0][:, 5]
top_boxes = results[0][:, :4]
if count:
print("top_label:", top_label)
classes_nums = np.zeros([self.num_classes])
for i in range(self.num_classes):
num = np.sum(top_label == i)
if num > 0:
print(self.class_names[i], " : ", num)
classes_nums[i] = num
print("classes_nums:", classes_nums)
if crop:
for i, c in list(enumerate(top_label)):
top, left, bottom, right = top_boxes[i]
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
dir_save_path = "img_crop"
if not os.path.exists(dir_save_path):
os.makedirs(dir_save_path)
crop_image = image.crop([left, top, right, bottom])
crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0)
print("save crop_" + str(i) + ".png to " + dir_save_path)
bbox_list = []
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
bbox_list.append([top, left, bottom, right, predicted_class, score])
return bbox_list
def detect_heatmap(self, image, heatmap_save_path):
import cv2
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def sigmoid(x):
y = 1.0 / (1.0 + np.exp(-x))
return y
image_shape = np.array(np.shape(image)[0:2])
image = cvtColor(image)
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
outputs = [output.cpu().numpy() for output in outputs]
plt.imshow(image, alpha=1)
plt.axis('off')
mask = np.zeros((image.size[1], image.size[0]))
for sub_output in outputs:
b, c, h, w = np.shape(sub_output)
sub_output = np.transpose(sub_output, [0, 2, 3, 1])[0]
score = np.max(sigmoid(sub_output[..., 5:]), -1) * sigmoid(sub_output[..., 4])
score = cv2.resize(score, (image.size[0], image.size[1]))
normed_score = (score * 255).astype('uint8')
mask = np.maximum(mask, normed_score)
plt.imshow(mask, alpha=0.5, interpolation='nearest', cmap="jet")
plt.axis('off')
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0, 0)
plt.savefig(heatmap_save_path, dpi=200)
print("Save to the " + heatmap_save_path)
plt.cla()