Evaluador

In [11]:

    

%matplotlib inline


    

In [12]:

    

from darkflow.net.build import TFNet
import cv2


    

In [13]:

    

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
from skimage import io


    

In [14]:

    

options = {"model": "cfg/yolo-1c.cfg", "load": -1, "threshold": 0.0}

tfnet = TFNet(options)


    

    




Parsing cfg/yolo-1c.cfg
Loading None ...
Finished in 0.00012683868408203125s

Building net ...
Source | Train? | Layer description                | Output size
-------+--------+----------------------------------+---------------
       |        | input                            | (?, 608, 608, 3)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 608, 608, 32)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 304, 304, 32)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 304, 304, 64)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 152, 152, 64)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)
 Init  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 152, 152, 64)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 76, 76, 128)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)
 Init  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 76, 76, 128)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 38, 38, 256)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)
 Init  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)
 Init  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 19, 19, 512)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Init  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Init  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | concat [16]                      | (?, 38, 38, 512)
 Init  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 64)
 Load  |  Yep!  | local flatten 2x2                | (?, 19, 19, 256)
 Load  |  Yep!  | concat [27, 24]                  | (?, 19, 19, 1280)
 Init  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Init  |  Yep!  | conv 1x1p0_1    linear           | (?, 19, 19, 30)
-------+--------+----------------------------------+---------------
Running entirely on CPU
Loading from ./ckpt/yolo-1c-4309
Finished in 4.332144260406494s

In [19]:

    

img = cv2.imread("./images/2017_5_26_14_392017_5_17_17_59Image_822.jpg")
imgski = io.imread("./images/2017_5_26_14_392017_5_17_17_59Image_822.jpg")

result = tfnet.return_predict(img)
result2 = tfnet.return_predict(imgski)


    

In [20]:

    

print("1", result)
print("2", result2)


    

    




1 []
2 []

In [17]:

    

# Mostramos las imagenes resultante
fig, ax = plt.subplots()
fig = plt.figure(figsize=(30, 30))

ax.imshow(img)
ax.axis('off')

for label in result:
    topleft = label['topleft']
    bottomright = label['bottomright']

    height = bottomright['y'] - topleft['y']
    width = bottomright['x'] - topleft['x']

    coord = [topleft['x'], topleft['y'], width, height]

    ax.add_patch(plt.Rectangle((topleft['x'], topleft['y']), width, height, edgecolor='red',
                               alpha=0.3, lw=2, facecolor='none'))
    ax.annotate(label['label'].capitalize(), xy=(topleft['x'], topleft['y']), xytext=(topleft['x'], topleft['y']))


    

    













    






<matplotlib.figure.Figure at 0x7fa6b4adcef0>