scripts/examples: Update examples.

This commit is contained in:
iabdalkader 2024-07-07 16:48:57 +03:00
parent 93e17a3a8d
commit eac75a75c6
5 changed files with 11 additions and 11 deletions

View File

@ -43,7 +43,7 @@ while True:
# This combines the labels and confidence values into a list of tuples
# and then sorts that list by the confidence values.
sorted_list = sorted(
zip(labels, model.predict(img)[0]), key=lambda x: x[1], reverse=True
zip(labels, model.predict([img])[0]), key=lambda x: x[1], reverse=True
)
for i in range(5):
print("%s = %f" % (sorted_list[i][0], sorted_list[i][1]))

View File

@ -45,11 +45,11 @@ colors = [ # Add more colors if you are detecting more than 7 types of classes
# position in the output image back to the original input image. The function then returns a
# list per class which each contain a list of (rect, score) tuples representing the detected
# objects.
def fomo_post_process(model, output, rect):
def fomo_post_process(model, inputs, outputs):
n, oh, ow, oc = model.output_shape[0]
nms = ml.NMS(ow, oh, rect)
nms = ml.NMS(ow, oh, inputs[0].roi)
for i in range(oc):
img = image.Image(output[0], shape=(oh, ow, 1), strides=(i, oc), scale=(255, 0))
img = image.Image(outputs[0], shape=(oh, ow, 1), strides=(i, oc), scale=(255, 0))
blobs = img.find_blobs(
threshold_list, x_stride=1, area_threshold=1, pixels_threshold=1
)
@ -69,7 +69,7 @@ while True:
img = sensor.snapshot()
for i, detection_list in enumerate(model.predict(img, callback=fomo_post_process)):
for i, detection_list in enumerate(model.predict([img], callback=fomo_post_process)):
if i == 0:
continue # background class
if len(detection_list) == 0:

View File

@ -5,8 +5,8 @@
# The MicroSpeech module is designed for real-time audio processing and speech recognition
# on microcontroller platforms. It leverages pre-trained models for audio preprocessing and
# speech recognition, specifically optimized for detecting keywords such as "Yes" and "No".
import ml
import time
from ml.apps import MicroSpeech
def callback(label, scores):
@ -17,7 +17,7 @@ def callback(label, scores):
# micro speech module for audio preprocessing and speech recognition, respectively. The
# user can override both by passing two models:
# MicroSpeech(preprocessor=ml.Model(...), micro_speech=ml.Model(...), labels=["label",...])
speech = ml.MicroSpeech()
speech = MicroSpeech()
# Starts the audio streaming and processes incoming audio to recognize speech commands.
# If a callback is passed, listen() will loop forever and call the callback when a keyword

View File

@ -5,8 +5,8 @@
# The MicroSpeech module is designed for real-time audio processing and speech recognition
# on microcontroller platforms. It leverages pre-trained models for audio preprocessing and
# speech recognition, specifically optimized for detecting keywords such as "Yes" and "No".
import ml
import time
from ml.apps import MicroSpeech
def callback(label, scores):
@ -17,7 +17,7 @@ def callback(label, scores):
# micro speech module for audio preprocessing and speech recognition, respectively. The
# user can override both by passing two models:
# MicroSpeech(preprocessor=ml.Model(...), micro_speech=ml.Model(...), labels=["label",...])
speech = ml.MicroSpeech()
speech = MicroSpeech()
# Starts the audio streaming and processes incoming audio to recognize speech commands.
# If a callback is passed, listen() will loop forever and call the callback when a keyword

View File

@ -5,8 +5,8 @@
# The MicroSpeech module is designed for real-time audio processing and speech recognition
# on microcontroller platforms. It leverages pre-trained models for audio preprocessing and
# speech recognition, specifically optimized for detecting keywords such as "Yes" and "No".
import ml
import time
from ml.apps import MicroSpeech
def callback(label, scores):
@ -17,7 +17,7 @@ def callback(label, scores):
# micro speech module for audio preprocessing and speech recognition, respectively. The
# user can override both by passing two models:
# MicroSpeech(preprocessor=ml.Model(...), micro_speech=ml.Model(...), labels=["label",...])
speech = ml.MicroSpeech()
speech = MicroSpeech()
# Starts the audio streaming and processes incoming audio to recognize speech commands.
# If a callback is passed, listen() will loop forever and call the callback when a keyword