machine-learning - 测试用于对象检测的 Svm 模型时出错

Question

from skimage.transform import pyramid_gaussian
from sklearn.externals import joblib
from skimage import color
from skimage import io
from imutils.object_detection import non_max_suppression
import imutils
import numpy as np
import cv2
import os
import glob 


orientations = 9
pixels_per_cell = (8, 8)
cells_per_block = (2, 2)
threshold = .3


def sliding_window(image, stepSize, windowSize):
    for y in range(0, image.shape[0], stepSize):
        for x in range(0, image.shape[1], stepSize):
            yield (x, y, image[y: y + windowSize[1], x:x + windowSize[0]])

 model = joblib.load('model_name.npy')

scale = 0
detections = []

img= cv2.imread("pos\56_resized.jpg")


(winW, winH)= (180,320)
windowSize=(winW,winH)
downscale=1.5
for resized in pyramid_gaussian(img, downscale=1.5):
    for (x,y,window) in sliding_window(resized, stepSize=10, windowSize=(winW,winH)):
        if window.shape[0] != winH or window.shape[1] !=winW:
            continue
        window=color.rgb2gray(window)
        fds = hog(window, orientations, pixels_per_cell, cells_per_block, block_norm='L2')
        fds = fds.reshape(1, -1)
        pred = model.predict(fds)

        if pred == 1:
            if model.decision_function(fds) > 0.6:
                print("Detection:: Location -> ({}, {})".format(x, y))
                print("Scale ->  {} | Confidence Score {} \n".format(scale,model.decision_function(fds)))
                detections.append((int(x*(downscale**scale)),int(y*(downscale**scale)),model.decision_function(fds),int(windowSize[0]*(downscale**scale)),int(windowSize[1]*(downscale**scale))
    scale+=1

** ValueError Traceback (最近一次调用最后一次) in ----> 1 for img1 in pyramid_gaussian(img, downscale=2): 2 for (x,y,window) in rolling_window(resized, stepSize=10, windowSize=( winW,winH)): 3 if window.shape[0] != winH or window.shape[1] !=winW: 4 continue 5 window=color.rgb2gray(window)

D:\Anaconda3\lib\site-packages\skimage\transform\pyramids.py in pyramid_gaussian(image, max_layer, downscale, sigma, order, mode, cval, multichannel) 195 196 # 转换为浮点数以实现金字塔中一致的数据类型 - -> 197 图像 = img_as_float(图像) 198 199 层 = 0

D:\Anaconda3\lib\site-packages\skimage\util\dtype.py in img_as_float(image, force_copy) 411 412 """ --> 413 return convert(image, np.floating, force_copy) 414 415

D:\Anaconda3\lib\site-packages\skimage\util\dtype.py in convert(image, dtype, force_copy, uniform) 124 if not (dtype_in in _supported_types and dtype_out in _supported_types): 125 raise ValueError("Can not convert从 {} 到 {}。” --> 126 .format(dtypeobj_in, dtypeobj_out)) 127 128 def sign_loss():

ValueError：无法从对象转换为 float64。

**

标签： machine-learningcomputer-visionanacondajupytersvm