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new 1af29e9 Fixes for wine detection tutorial (#13886)
1af29e9 is described below
commit 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7
Author: Pedro Larroy <[email protected]>
AuthorDate: Mon Apr 29 15:47:13 2019 -0700
Fixes for wine detection tutorial (#13886)
---
docs/tutorials/embedded/wine_detector.md | 51 +++++++++++++++++++++-----------
1 file changed, 33 insertions(+), 18 deletions(-)
diff --git a/docs/tutorials/embedded/wine_detector.md
b/docs/tutorials/embedded/wine_detector.md
index f0ae827..6a9372c 100644
--- a/docs/tutorials/embedded/wine_detector.md
+++ b/docs/tutorials/embedded/wine_detector.md
@@ -91,6 +91,7 @@ The next step is to create a python script to load the model,
and run inference
import mxnet as mx
import numpy as np
+import time
import cv2, os, urllib
from collections import namedtuple
Batch = namedtuple('Batch', ['data'])
@@ -100,13 +101,14 @@ with open('synset.txt', 'r') as f:
synsets = [l.rstrip() for l in f]
# Load the network parameters
-sym, arg_params, aux_params = mx.model.load_checkpoint('Inception_BN', 0)
+sym, arg_params, aux_params = mx.model.load_checkpoint('Inception-BN', 126)
+
# Load the network into an MXNet module and bind the corresponding parameters
mod = mx.mod.Module(symbol=sym, context=mx.cpu())
mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))])
mod.set_params(arg_params, aux_params)
-
+
'''
Function to predict objects by giving the model a pointer to an image file and
running a forward pass through the model.
@@ -129,14 +131,14 @@ def predict(filename, mod, synsets, N=5):
img = np.swapaxes(img, 1, 2)
img = img[np.newaxis, :]
print "pre-processed image in "+str(time.time()-tic)
-
+
toc = time.time()
mod.forward(Batch([mx.nd.array(img)]))
prob = mod.get_outputs()[0].asnumpy()
prob = np.squeeze(prob)
print "forward pass in "+str(time.time()-toc)
-
-
+
+
topN = []
a = np.argsort(prob)[::-1]
for i in a[0:N]:
@@ -156,7 +158,7 @@ def predict_from_url(url, N=5):
return predict(filename, mod, synsets, N)
# Code to predict on a local file
-def predict_from_local_file(filename, N=5):
+def predict_from_local_file(filename, N=5):
return predict(filename, mod, synsets, N)
```
@@ -164,11 +166,24 @@ Now that we have defined inception_predict.py we can test
that the model is runn
```bash
python
->>> import inception_predict
->>> predict_from_url("http://imgur.com/HzafyBA";)
+>>> from inception_predict import *
+>>> predict_from_url("https://i.imgur.com/HzafyBA.jpg";)
```
-This should give a reasonable prediction for the fluffy cow in this
[image](http://imgur.com/HzafyBA).
+This should give a reasonable prediction for the fluffy cow in this
[image](http://imgur.com/HzafyBA).
+
+```
+pre-processed image in 0.20366191864
+forward pass in 63.2164611816
+probability=0.718524, class=n02403003 ox
+probability=0.176381, class=n02389026 sorrel
+probability=0.095558, class=n03868242 oxcart
+probability=0.002765, class=n02408429 water buffalo, water ox, Asiatic
buffalo, Bubalus bubalis
+probability=0.001262, class=n03935335 piggy bank, penny bank
+[(0.71852392, 'n02403003 ox'), (0.17638102, 'n02389026 sorrel'), (0.09555836,
'n03868242 oxcart'),
+(0.0027645244, 'n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus
bubalis'),
+(0.0012616422, 'n03935335 piggy bank, penny bank')]
+```
## Running an Inception on Real-Time Video From PiCamera
@@ -194,11 +209,11 @@ while True:
camera.start_preview()
camera.capture(filename)
camera.stop_preview()
-
+
# Run inception prediction on image
print "Predicting"
topn = inception_predict.predict_from_local_file(filename, N=5)
-
+
# Print the top N most likely objects in image (default set to 5, change
this in the function call above)
print topn
```
@@ -209,7 +224,7 @@ You can then run this file by entering the following
command:
python camera_test.py
```
-If camera_test.py is working you should see a preview every few seconds of the
image that is being captured and fed to the model, as well as predicted classes
for objects in the image being written to the terminal.
+If camera_test.py is working you should see a preview every few seconds of the
image that is being captured and fed to the model, as well as predicted classes
for objects in the image being written to the terminal.
Try pointing the PiCamera at a few different objects and see what predictions
the network comes out with.
@@ -248,13 +263,13 @@ def customCallback(client, userdata, message):
# Usage
usageInfo = """Usage:
-
+
Use certificate based mutual authentication:
python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -c <certFilePath> -k
<privateKeyFilePath>
-
+
Use MQTT over WebSocket:
python wine_alerter.py -e <endpoint> -r <rootCAFilePath> -w
-
+
Type "python wine_alerter.py -h" for available options.
"""
@@ -272,7 +287,7 @@ helpInfo = """-e, --endpoint
-h, --help
Help information
"""
-
+
# Read in command-line parameters
useWebsocket = False
host = ""
@@ -367,10 +382,10 @@ while True:
camera.capture(filename)
camera.stop_preview()
topn = inception_predict.predict_from_local_file(filename, N=5)
-
+
# Check if either of the top two predictions are wine related and publish
a message if it is
# you can change 'wine' here to anything you want to alert the server
about detecting
- if 'wine' in topn[0][1] or 'wine' in topn[1][1]:
+ if 'wine' in topn[0][1] or 'wine' in topn[1][1]:
myAWSIoTMQTTClient.publish("sdk/test/Python", "New Message: WINE
DETECTED!", 0)
```
![https://i.imgur.com/HzafyBA.jpg"](https://i.imgur.com/HzafyBA.jpg"){kind=link}