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| import cv2
import numpy as np
# 引用 paddle inference 预测库
import paddle.inference as paddle_infer
from PIL import Image# 创建 config
config = paddle_infer.Config("./models/mobilenet_ssd/mobilenet-ssd-model",
"./models/mobilenet_ssd/mobilenet-ssd-params") # 根据 config 创建 predictor
predictor = paddle_infer.create_predictor(config) # 获取输入 Tensor
input_names = predictor.get_input_names()
input_tensor = predictor.get_input_handle(input_names[0]) print(input_names)
print(input_tensor) # 从 CPU 获取数据，设置到 Tensor 内部
imgPath = './frame/15.jpg' cap = cv2.VideoCapture('./models/mobilenet_ssd/sample.mp4')
frame = []
while True:
ret, frame = cap.read()
if ret == False:
break
# cv2.imwrite("./frame/frame.jpg", frame)
# imgPath = './frame/frame.jpg'
im = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))# Image.open(imgPath)
im = im.resize((300, 300), Image.BICUBIC) # img = cv2.cvtColor(numpy.asarray(im.resize((320, 240), Image.BICUBIC)), cv2.COLOR_RGB2BGR)#cv2.cvtColor(, cv2.COLOR_RGB2BGR) # 转换代码
# cv2.imshow('img', img) # opencv显示
# cv2.waitKey() im = np.array(im, dtype=np.float32).transpose(2, 0, 1).reshape((1, 3, 300, 300))
mean = [121.636447, 125.696666, 124.807721]
# mean = [127.5, 127.5, 127.5]
scale = [0.007843,
0.007843,
0.007843]
std = [ 69.480526,
66.625320,
50.616609] for ri in range(0, 300):
im[0][0][ri] = (im[0][0][ri] / 255)# - (scale[0] - mean[0] / 255)
for gi in range(0, 300):
im[0][1][gi] = (im[0][1][gi] / 255)# - (scale[1] - mean[1] / 255)
for bi in range(0, 300):
im[0][2][bi] = (im[0][2][bi] / 255)# - (scale[2] - mean[2] / 255) fake_input = im input_tensor1 = predictor.get_input_handle(input_names[0])
input_tensor1.copy_from_cpu(im)
#
# im = np.asarray([[320.0/240.0, 320.0/320.0]], dtype=np.float32)
# input_tensor2 = predictor.get_input_handle(input_names[1])
# input_tensor2.copy_from_cpu(im)
#
# im = np.asarray([[608, 608]], dtype=np.float32)
# input_tensor2 = predictor.get_input_handle(input_names[2])
# input_tensor2.copy_from_cpu(im) # 执行预测
predictor.run() # 获取输出 Tensor
output_names = predictor.get_output_names()
output_tensor = predictor.get_output_handle(output_names[0]) # 从 Tensor 中获取数据到 CPU
output_data = output_tensor.copy_to_cpu() # 获取 Tensor 的维度信息
output_shape = output_tensor.shape() # 获取 Tensor 的数据类型
output_type = output_tensor.type() results = []
i = 0
img = frame
label_list = ["background", "cone", "bridge", "pig", "tractor", "corn", "bump", "crosswalk", " "]
label_list = ["background", "cone", "granary", "bridge", "tractor", "corn", "pig", "crosswalk", "bump"]
img = cv2.resize(img, dsize=(300, 300), fx=1, fy=1, interpolation=cv2.INTER_LINEAR)
if len(output_data) > 1:
for data in output_data:
tmp = {}
if data[1] > 0.9:
tmp['type'] = data[0]
tmp['score'] = data[1]
tmp['x'] = data[2] * 300
tmp['y'] = data[3] * 300
tmp['width'] = data[4] * 300 - tmp['x']
tmp['height'] = data[5] * 300 - tmp['y']
results.insert(i, tmp)
# print(tmp)
i = i + 1
for i in results:
x = int(i['x'])
xmax = int(i['x'] + i['width'])
y = int(i['y'])
ymax = int(i['y'] + i['height'])
# print((x, y), (xmax, ymax))
cv2.rectangle(img, (x, y), (xmax, ymax), (0, 0, 255), 2)
# cv2.rectangle(img, (x, ymax), (xmax, y), (255, 0, 0), 2)
cv2.putText(img,
str(label_list[int(i['type'])]),
(int(x), int(y)),
cv2.FONT_HERSHEY_SIMPLEX,
0.75,
(0, int(i['type']) * 30, 255),
2)
img = cv2.resize(img, dsize=(320, 240), fx=1, fy=1, interpolation=cv2.INTER_LINEAR)
cv2.imshow('src', img)
cv2.waitKey(1) # 释放中间 Tensor
predictor.clear_intermediate_tensor() # 释放内存池中的所有临时 Tensor
predictor.try_shrink_memory()
|
