scripts/examples: Clean up TensorFlow examples.

This commit is contained in:
iabdalkader 2022-07-10 18:35:31 +02:00
parent 379e187e07
commit 331bdd67c6
7 changed files with 25 additions and 245 deletions

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@ -1,31 +0,0 @@
# Face Collection
#
# Use this script to gather face images for building a TensorFlow dataset. This script automatically
# zooms in the largest face in the field of view which you can then save using the data set editor.
import sensor, image, time
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
largest_face = None
largest_face_timeout = 0
while(True):
clock.tick()
faces = sensor.snapshot().gamma_corr(contrast=1.5).find_features(image.HaarCascade("frontalface"))
if faces:
largest_face = max(faces, key = lambda f: f[2] * f[3])
largest_face_timeout = 20
if largest_face_timeout > 0:
sensor.get_fb().crop(roi=largest_face)
largest_face_timeout -= 1
print(clock.fps())

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# Face Recognition
#
# Use this script to run a TensorFlow lite image classifier on faces detected within an image.
# The classifier is free to do facial recognition, expression detection, or whatever.
import sensor, image, time, tf
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
net = tf.load("trained.tflite", load_to_fb=True)
labels = [l.rstrip('\n') for l in open("labels.txt")]
while(True):
clock.tick()
# Take a picture and brighten things up for the frontal face detector.
img = sensor.snapshot().gamma_corr(contrast=1.5)
# Returns a list of rects (x, y, w, h) where faces are.
faces = img.find_features(image.HaarCascade("frontalface"))
for f in faces:
# Classify a face and get the class scores list
scores = net.classify(img, roi=f)[0].output()
# Find the highest class score and lookup the label for that
label = labels[scores.index(max(scores))]
# Draw a box around the face
img.draw_rectangle(f)
# Draw the label above the face
img.draw_string(f[0]+3, f[1]-1, label, mono_space=False)
print(clock.fps())

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@ -48,14 +48,13 @@ while(True):
# down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%. # down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%.
# Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small... # Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small...
# default settings just do one detection... change them to search the image...
# Setting x_overlap=-1 forces the window to stay centered in the ROI in the x direction always. If # Setting x_overlap=-1 forces the window to stay centered in the ROI in the x direction always. If
# y_overlap is not -1 the method will search in all vertical positions. # y_overlap is not -1 the method will search in all vertical positions.
# Setting y_overlap=-1 forces the window to stay centered in the ROI in the y direction always. If # Setting y_overlap=-1 forces the window to stay centered in the ROI in the y direction always. If
# x_overlap is not -1 the method will serach in all horizontal positions. # x_overlap is not -1 the method will serach in all horizontal positions.
# default settings just do one detection... change them to search the image... for obj in tf.classify(mobilenet, img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0):
for obj in tf.classify(mobilenet, img, min_scale=1.0, scale_mul=0.5, x_overlap=-1, y_overlap=-1):
print("**********\nTop 5 Detections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect()) print("**********\nTop 5 Detections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect()) img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples # This combines the labels and confidence values into a list of tuples

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# TensorFlow Lite Mobilenet V1 Example
#
# Google's Mobilenet V1 detects 1000 classes of objects
#
# WARNING: Mobilenet is trained on ImageNet and isn't meant to classify anything
# in the real world. It's just designed to score well on the ImageNet dataset.
# This example just shows off running mobilenet on the OpenMV Cam. However, the
# default model is not really usable for anything. You have to use transfer
# learning to apply the model to a target problem by re-training the model.
#
# NOTE: This example only works on the OpenMV Cam H7 Pro (that has SDRAM) and better!
# To get the models please see the CNN Network library in OpenMV IDE under
# Tools -> Machine Vision. The labels are there too.
# You should insert a microSD card into your camera and copy-paste the mobilenet_labels.txt
# file and your chosen model into the root folder for ths script to work.
#
# In this example we slide the detector window over the image and get a list
# of activations. Note that use a CNN with a sliding window is extremely compute
# expensive so for an exhaustive search do not expect the CNN to be real-time.
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
mobilenet_version = "1" # 1
mobilenet_width = "0.5" # 1.0, 0.75, 0.50, 0.25
mobilenet_resolution = "128" # 224, 192, 160, 128
mobilenet = "mobilenet_v%s_%s_%s_quant.tflite" % (mobilenet_version, mobilenet_width, mobilenet_resolution)
labels = [line.rstrip('\n') for line in open("mobilenet_labels.txt")]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# net.classify() will run the network on an roi in the image (or on the whole image if the roi is not
# specified). A classification score output vector will be generated for each location. At each scale the
# detection window is moved around in the ROI using x_overlap (0-1) and y_overlap (0-1) as a guide.
# If you set the overlap to 0.5 then each detection window will overlap the previous one by 50%. Note
# the computational work load goes WAY up the more overlap. Finally, for multi-scale matching after
# sliding the network around in the x/y dimensions the detection window will shrink by scale_mul (0-1)
# down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%.
# Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small...
# default settings just do one detection... change them to search the image...
for obj in tf.classify(mobilenet, img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0):
print("**********\nTop 5 Detections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# 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, obj.output()), key = lambda x: x[1], reverse = True)
for i in range(5):
print("%s = %f" % (sorted_list[i][0], sorted_list[i][1]))
print(clock.fps(), "fps")

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@ -1,9 +1,8 @@
# TensorFlow Lite Object Detection Example # TensorFlow Lite Object Detection Example
# #
# This example shows off object detection. Object detect is much more powerful than # This examples uses the builtin FOMO model to detect faces.
# object classification. It can locate multiple objects in the image.
import sensor, image, time, os, tf import sensor, image, time, tf, math
sensor.reset() # Reset and initialize the sensor. sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE) sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
@ -11,13 +10,14 @@ sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window. sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust. sensor.skip_frames(time=2000) # Let the camera adjust.
net = tf.load('<object_detection_network>', load_to_fb=True) min_confidence = 0.4
labels = []
try: # Load labels if they exist # Load built-in FOMO face detection model
labels = [line.rstrip('\n') for line in open("labels.txt")] labels, net = tf.load_builtin_model("fomo_face_detection")
except:
pass # Alternatively, models can be loaded from the filesystem storage.
#net = tf.load('<object_detection_network>', load_to_fb=True)
#labels = [line.rstrip('\n') for line in open("labels.txt")]
colors = [ # Add more colors if you are detecting more than 7 types of classes at once. colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
(255, 0, 0), (255, 0, 0),
@ -35,17 +35,20 @@ while(True):
img = sensor.snapshot() img = sensor.snapshot()
# detect() segments an object using the provided segmentation model. This produces mutliple # detect() returns all objects found in the image (splitted out per class already)
# grayscale images per object class that we are trying to detect. detect() then runs # we skip class index 0, as that is the background, and then draw circles of the center
# find_blobs() internally on the segmented images to find all blob locations and then returns # of our objects
# the bound boxes of all blobs found per object class. So, detect() returns a list of lists of
# classification objects and the respective confidence level.
for i, detection_list in enumerate(net.detect(img, thresholds=[(128, 255)])): for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
if (i < len(labels)): if (i == 0): continue # background class
print("********** %s **********" % labels[i]) if (len(detection_list) == 0): continue # no detections for this class?
print("********** %s **********" % labels[i])
for d in detection_list: for d in detection_list:
print(d) [x, y, w, h] = d.rect()
img.draw_rectangle(d.rect(), color=colors[i]) center_x = math.floor(x + (w / 2))
center_y = math.floor(y + (h / 2))
print(f"x {center_x}\ty {center_y}")
img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)
print(clock.fps(), "fps", end="\n\n") print(clock.fps(), "fps", end="\n")

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# TensorFlow Lite Person Dection Example
#
# Google's Person Detection Model detects if a person is in view.
#
# In this example we slide the detector window over the image and get a list
# of activations. Note that use a CNN with a sliding window is extremely compute
# expensive so for an exhaustive search do not expect the CNN to be real-time.
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
# Load the built-in person detection network (the network is in your OpenMV Cam's firmware).
labels, net = tf.load_builtin_model('person_detection')
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# net.classify() will run the network on an roi in the image (or on the whole image if the roi is not
# specified). A classification score output vector will be generated for each location. At each scale the
# detection window is moved around in the ROI using x_overlap (0-1) and y_overlap (0-1) as a guide.
# If you set the overlap to 0.5 then each detection window will overlap the previous one by 50%. Note
# the computational work load goes WAY up the more overlap. Finally, for multi-scale matching after
# sliding the network around in the x/y dimensions the detection window will shrink by scale_mul (0-1)
# down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%.
# Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small...
# Setting x_overlap=-1 forces the window to stay centered in the ROI in the x direction always. If
# y_overlap is not -1 the method will search in all vertical positions.
# Setting y_overlap=-1 forces the window to stay centered in the ROI in the y direction always. If
# x_overlap is not -1 the method will serach in all horizontal positions.
# default settings just do one detection... change them to search the image...
for obj in net.classify(img, min_scale=0.5, scale_mul=0.5, x_overlap=-1, y_overlap=-1):
print("**********\nDetections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
for i in range(len(obj.output())):
print("%s = %f" % (labels[i], obj.output()[i]))
img.draw_rectangle(obj.rect())
img.draw_string(obj.x()+3, obj.y()-1, labels[obj.output().index(max(obj.output()))], mono_space = False)
print(clock.fps(), "fps")

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# TensorFlow Lite Person Dection Example
#
# Google's Person Detection Model detects if a person is in view.
#
# In this example we slide the detector window over the image and get a list
# of activations. Note that use a CNN with a sliding window is extremely compute
# expensive so for an exhaustive search do not expect the CNN to be real-time.
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
# Load the built-in person detection network (the network is in your OpenMV Cam's firmware).
labels, net = tf.load_builtin_model('person_detection')
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# net.classify() will run the network on an roi in the image (or on the whole image if the roi is not
# specified). A classification score output vector will be generated for each location. At each scale the
# detection window is moved around in the ROI using x_overlap (0-1) and y_overlap (0-1) as a guide.
# If you set the overlap to 0.5 then each detection window will overlap the previous one by 50%. Note
# the computational work load goes WAY up the more overlap. Finally, for multi-scale matching after
# sliding the network around in the x/y dimensions the detection window will shrink by scale_mul (0-1)
# down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%.
# Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small...
# default settings just do one detection... change them to search the image...
for obj in net.classify(img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0):
print("**********\nDetections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
for i in range(len(obj.output())):
print("%s = %f" % (labels[i], obj.output()[i]))
img.draw_rectangle(obj.rect())
img.draw_string(obj.x()+3, obj.y()-1, labels[obj.output().index(max(obj.output()))], mono_space = False)
print(clock.fps(), "fps")