Trying to find Haar Cascade

Arduino Boards
1

Hi,
I am new to all this micropython and openmv stuff so I need a lot of help but anyway, I was tring some of this face detection stuff and I keep running into this Haar Cascade thing and I was tring to figure out what it was and how to use it so please help!

Thanks,
Isaac

2

Hi, which OpenMV Cam are you using and what firmware version? We’re in the middle of large reactor right now and the tip of our latest firmware dev isn’t quite ready for use without OpenMV IDE being ready. Firmware v4.6.20 should be fine though.

3

I am using an Arduino Nicla Vision, it does have v.4.6.20.

4

Hi, okay, for the latest firmware for the Nicla the face cascade and eye cascade were disabled to save flash. It’s enabled on other OpenMV Cams.

We have an update coming at the end of the month which will re-enable these features by enabling a new feature called ROMFS.

Anyway, for now, please sue this for face detection:

# This work is licensed under the MIT license.
# Copyright (c) 2013-2024 OpenMV LLC. All rights reserved.
# https://github.com/openmv/openmv/blob/master/LICENSE
#
# TensorFlow Lite Object Detection Example
#
# This examples uses the builtin FOMO model to detect faces.

import sensor
import time
import ml
from ml.utils import NMS
import math
import image

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.

min_confidence = 0.4
threshold_list = [(math.ceil(min_confidence * 255), 255)]

# Load built-in FOMO face detection model
model = ml.Model("fomo_face_detection")
print(model)

# Alternatively, models can be loaded from the filesystem storage.
# model = ml.Model('<object_detection_modelwork>.tflite', 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.
    (255, 0, 0),
    (0, 255, 0),
    (255, 255, 0),
    (0, 0, 255),
    (255, 0, 255),
    (0, 255, 255),
    (255, 255, 255),
]


# FOMO outputs an image per class where each pixel in the image is the centroid of the trained
# object. So, we will get those output images and then run find_blobs() on them to extract the
# centroids. We will also run get_stats() on the detected blobs to determine their score.
# The Non-Max-Supression (NMS) object then filters out overlapping detections and maps their
# 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, inputs, outputs):
    n, oh, ow, oc = model.output_shape[0]
    nms = NMS(ow, oh, inputs[0].roi)
    for i in range(oc):
        img = image.Image(outputs[0][0, :, :, i] * 255)
        blobs = img.find_blobs(
            threshold_list, x_stride=1, area_threshold=1, pixels_threshold=1
        )
        for b in blobs:
            rect = b.rect()
            x, y, w, h = rect
            score = (
                img.get_statistics(thresholds=threshold_list, roi=rect).l_mean() / 255.0
            )
            nms.add_bounding_box(x, y, x + w, y + h, score, i)
    return nms.get_bounding_boxes()


clock = time.clock()
while True:
    clock.tick()

    img = sensor.snapshot()

    for i, detection_list in enumerate(model.predict([img], callback=fomo_post_process)):
        if i == 0:
            continue  # background class
        if len(detection_list) == 0:
            continue  # no detections for this class?

        print("********** %s **********" % model.labels[i])
        for (x, y, w, h), score in detection_list:
            center_x = math.floor(x + (w / 2))
            center_y = math.floor(y + (h / 2))
            print(f"x {center_x}\ty {center_y}\tscore {score}")
            img.draw_circle((center_x, center_y, 12), color=colors[i])

    print(clock.fps(), "fps", end="\n")
1 Like
5

Thanks @kwagyeman!