Pedestrian detector that sends people counter results to Ubidots. Libraries: OpenCV, requests, imutils

peopleCounter.py

from imutils.object_detection import non_max_suppression
import numpy as np
import imutils
import cv2
import requests
import time
import argparse
import time
import base64

'''
Usage:

python peopleCounter.py -i PATH_TO_IMAGE  # Reads and detect people in a single local stored image
python peopleCounter.py -c  # Attempts to detect people using webcam

IMPORTANT: This example is given AS IT IS without any warranty

Made by: Jose Garcia

'''

URL_EDUCATIONAL = "http://things.ubidots.com"
URL_INDUSTRIAL = "http://industrial.api.ubidots.com"
INDUSTRIAL_USER = True  # Set this to False if you are an educational user
TOKEN = "...."  # Put here your Ubidots TOKEN
DEVICE = "detector"  # Device where will be stored the result
VARIABLE = "people"  # Variable where will be stored the result

# Opencv pre-trained SVM with HOG people features 
HOGCV = cv2.HOGDescriptor()
HOGCV.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

def detector(image):
    '''
    @image is a numpy array
    '''

    clone = image.copy()

    (rects, weights) = HOGCV.detectMultiScale(image, winStride=(4, 4),
                                              padding=(8, 8), scale=1.05)

    # draw the original bounding boxes
    for (x, y, w, h) in rects:
        cv2.rectangle(clone, (x, y), (x + w, y + h), (0, 0, 255), 2)

    # Applies non-max supression from imutils package to kick-off overlapped
    # boxes
    rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
    result = non_max_suppression(rects, probs=None, overlapThresh=0.65)

    return result


def buildPayload(variable, value, context):
    return {variable: {"value": value, "context": context}}


def sendToUbidots(token, device, variable, value, context={}, industrial=True):
    # Builds the endpoint
    url = URL_INDUSTRIAL if industrial else URL_EDUCATIONAL
    url = "{}/api/v1.6/devices/{}".format(url, device)

    payload = buildPayload(variable, value, context)
    headers = {"X-Auth-Token": token, "Content-Type": "application/json"}

    attempts = 0
    status = 400

    while status >= 400 and attempts <= 5:
        req = requests.post(url=url, headers=headers, json=payload)
        status = req.status_code
        attempts += 1
        time.sleep(1)

    return req


def argsParser():
    ap = argparse.ArgumentParser()
    ap.add_argument("-i", "--image", default=None,
                    help="path to image test file directory")
    ap.add_argument("-c", "--camera", default=False,
                    help="Set as true if you wish to use the camera")
    args = vars(ap.parse_args())

    return args


def localDetect(image_path):
    result = []
    image = cv2.imread(image_path)
    image = imutils.resize(image, width=min(400, image.shape[1]))
    clone = image.copy()
    if len(image) <= 0:
        print("[ERROR] could not read your local image")
        return result
    print("[INFO] Detecting people")
    result = detector(image)

    # shows the result
    for (xA, yA, xB, yB) in result:
        cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)

    cv2.imshow("result", image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    cv2.imwrite("result.png", np.hstack((clone, image)))
    return (result, image)


def cameraDetect(token, device, variable, sample_time=5):

    cap = cv2.VideoCapture(0)
    init = time.time()

    # Allowed sample time for Ubidots is 1 dot/second
    if sample_time < 1:
        sample_time = 1

    while(True):
        # Capture frame-by-frame
        ret, frame = cap.read()
        frame = imutils.resize(frame, width=min(400, frame.shape[1]))
        result = detector(frame.copy())

        # shows the result
        for (xA, yA, xB, yB) in result:
            cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2)
        cv2.imshow('frame', frame)

        # Sends results
        if time.time() - init >= sample_time:
            print("[INFO] Sending actual frame results")
            # Converts the image to base 64 and adds it to the context
            b64 = convert_to_base64(frame)
            context = {"image": b64}
            sendToUbidots(token, device, variable,
                          len(result), context=context)
            init = time.time()

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # When everything done, release the capture
    cap.release()
    cv2.destroyAllWindows()


def convert_to_base64(image):
    image = imutils.resize(image, width=400)
    img_str = cv2.imencode('.png', image)[1].tostring()
    b64 = base64.b64encode(img_str)

    return b64.decode('utf-8')


def detectPeople(args):
    image_path = args["image"]
    camera = True if str(args["camera"]) == 'true' else False

    # Routine to read local image
    if image_path != None and not camera:
        print("[INFO] Image path provided, attempting to read image")
        (result, image) = localDetect(image_path)
        print("[INFO] sending results")
        # Converts the image to base 64 and adds it to the context
        b64 = convert_to_base64(image)
        context = {"image": b64}

        # Sends the result
        req = sendToUbidots(TOKEN, DEVICE, VARIABLE,
                            len(result), context=context)
        if req.status_code >= 400:
            print("[ERROR] Could not send data to Ubidots")
            return req

    # Routine to read images from webcam
    if camera:
        print("[INFO] reading camera images")
        cameraDetect(TOKEN, DEVICE, VARIABLE)


def main():
    args = argsParser()
    detectPeople(args)


if __name__ == '__main__':
    main()

Thank you

You left this line of code out, but it was present in your tutorial. It wasnt working but it worked when i added it
...........

Opencv pre-trained SVM with HOG people features

HOGCV = cv2.HOGDescriptor() HOGCV.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

Hello eyanam.
Are you sure of your ligne?
It still doesn't work for me.
++

Is anyone getting an AttributeError: 'NoneType' error on line 121? I'm new to this and trying to follow the tutorial but can't seem to get past this.

that error means that you are not getting any frame from your camera, check the cv2.VideoCapture() method settings.

All the best

Hi Jotathebest,

My name is Ger from Holland.
I try your script but get error :
File "peoplecounter.py", line 38, in detector
(rects, weights) = HOGCV.detectMultiScale(image, winStride=(4, 4),
AttributeError: 'str' object has no attribute 'detectMultiScale'

Can youy help me out to fix the problem so i can use your nice script?

Thanks in advance,
Ger

File "happy_i_love_you.py", line 182, in detectPeople
(result, image) = localDetect(image_path)
File "happy_i_love_you.py", line 115, in localDetect
result = detector(image)
File "happy_i_love_you.py", line 54, in detector
(rects, weights) = HOGCV.detectMultiScale(image, winStride=(4, 4),
NameError: name 'HOGCV' is not defined

please help me

Hi there, I missed to add the HOGCV instance in this example script, I have just updated it so please gently try again.

All the best

You left this line of code out, but it was present in your tutorial. It wasnt working but it worked when i added it
...........

Opencv pre-trained SVM with HOG people features

HOGCV = cv2.HOGDescriptor() HOGCV.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

in which line do we need to add this

Great job, got it working. I think you might be missing

import base64

Great job, got it working. I think you might be missing

import base64

Thanks for the report, I have just added the missed package at the beginning.

Best

How to generate graphical report from ubidots dashboard?
I have did the same but I can't understand it at the last because TOKEN not sending information from application towards dashboard?

@iMuhammadessa just follow the steps at https://help.ubidots.com/en/articles/1674356-people-counting-systems-with-opencv-python-and-ubidots