Leo Wallis

Leo Wallis

1629683924

Auto Face Blur App with Haar Cascade Classifier and Gaussian Blur

In this video, we will take a look into the use of Haar Cascade Classifier and run a Gaussian Blur algorithm on top of it to perform the Auto Face Blur application.

This is a continuation of the Smile Finder App where we already discussed in depth about the use of HAAR cascade classifier.

#opencv #kivymd 

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Auto Face Blur App with Haar Cascade Classifier and Gaussian Blur

A Lightweight Face Recognition and Facial Attribute Analysis

deepface

Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib.

Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.

Installation

The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well. The library is mainly based on TensorFlow and Keras.

pip install deepface

Then you will be able to import the library and use its functionalities.

from deepface import DeepFace

Facial Recognition - Demo

A modern face recognition pipeline consists of 5 common stages: detect, align, normalize, represent and verify. While Deepface handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code.

Face Verification - Demo

This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key.

result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")

Face recognition - Demo

Face recognition requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.

df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")

Face recognition models - Demo

Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models: VGG-Face , Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. The default configuration uses VGG-Face model.

models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])

FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments. You can find out the scores of those models below on both Labeled Faces in the Wild and YouTube Faces in the Wild data sets declared by its creators.

ModelLFW ScoreYTF Score
Facenet51299.65%-
ArcFace99.41%-
Dlib99.38 %-
Facenet99.20%-
VGG-Face98.78%97.40%
Human-beings97.53%-
OpenFace93.80%-
DeepID-97.05%

Similarity

Face recognition models are regular convolutional neural networks and they are responsible to represent faces as vectors. We expect that a face pair of same person should be more similar than a face pair of different persons.

Similarity could be calculated by different metrics such as Cosine Similarity, Euclidean Distance and L2 form. The default configuration uses cosine similarity.

metrics = ["cosine", "euclidean", "euclidean_l2"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1])

Euclidean L2 form seems to be more stable than cosine and regular Euclidean distance based on experiments.

Facial Attribute Analysis - Demo

Deepface also comes with a strong facial attribute analysis module including age, gender, facial expression (including angry, fear, neutral, sad, disgust, happy and surprise) and race (including asian, white, middle eastern, indian, latino and black) predictions.

obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'])

Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in its tutorial.

Streaming and Real Time Analysis - Demo

You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.

DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")

Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below.

user
├── database
│   ├── Alice
│   │   ├── Alice1.jpg
│   │   ├── Alice2.jpg
│   ├── Bob
│   │   ├── Bob.jpg

Face Detectors - Demo

Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. OpenCV, SSD, Dlib, MTCNN and RetinaFace detectors are wrapped in deepface.

All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.

backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface']

#face verification
obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = backends[4])

#face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])

#facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])

#face detection and alignment
face = DeepFace.detectFace(img_path = "img.jpg", target_size = (224, 224), detector_backend = backends[4])

Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment.

RetinaFace and MTCNN seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn.

The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Highlighted red points show some facial landmarks such as eyes, nose and mouth. That's why, alignment score of RetinaFace is high as well.

You can find out more about RetinaFace on this repo.

API - Demo

Deepface serves an API as well. You can clone /api/api.py and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.

python api.py

Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be http://127.0.0.1:5000/verify for face recognition, http://127.0.0.1:5000/analyze for facial attribute analysis, and http://127.0.0.1:5000/represent for vector representation. You should pass input images as base64 encoded string in this case. Here, you can find a postman project.

Tech Stack - Vlog, Tutorial

Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.

embedding = DeepFace.represent(img_path = "img.jpg", model_name = 'Facenet')

Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.

Contribution

Pull requests are welcome. You should run the unit tests locally by running test/unit_tests.py. Please share the unit test result logs in the PR. Deepface is currently compatible with TF 1 and 2 versions. Change requests should satisfy those requirements both.

Support

There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏

You can also support this work on Patreon

 

Citation

Please cite deepface in your publications if it helps your research. Here are its BibTeX entries:

@inproceedings{serengil2020lightface,
  title        = {LightFace: A Hybrid Deep Face Recognition Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
  pages        = {23-27},
  year         = {2020},
  doi          = {10.1109/ASYU50717.2020.9259802},
  url          = {https://doi.org/10.1109/ASYU50717.2020.9259802},
  organization = {IEEE}
}
@inproceedings{serengil2021lightface,
  title        = {HyperExtended LightFace: A Facial Attribute Analysis Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2021 International Conference on Engineering and Emerging Technologies (ICEET)},
  pages        = {1-4},
  year         = {2021},
  doi          = {10.1109/ICEET53442.2021.9659697},
  url.         = {https://doi.org/10.1109/ICEET53442.2021.9659697},
  organization = {IEEE}
}

Also, if you use deepface in your GitHub projects, please add deepface in the requirements.txt.

Author: Serengil
Source Code: https://github.com/serengil/deepface 
License: MIT License

#python #machine-learning 

Dominic  Feeney

Dominic Feeney

1648217849

Deepface: A Face Recognition and Facial Attribute Analysis for Python

deepface

Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib.

Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.

Installation

The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well. The library is mainly powered by TensorFlow and Keras.

pip install deepface

Then you will be able to import the library and use its functionalities.

from deepface import DeepFace

Facial Recognition - Demo

A modern face recognition pipeline consists of 5 common stages: detect, align, normalize, represent and verify. While Deepface handles all these common stages in the background, you don’t need to acquire in-depth knowledge about all the processes behind it. You can just call its verification, find or analysis function with a single line of code.

Face Verification - Demo

This function verifies face pairs as same person or different persons. It expects exact image paths as inputs. Passing numpy or based64 encoded images is also welcome. Then, it is going to return a dictionary and you should check just its verified key.

result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")

Face recognition - Demo

Face recognition requires applying face verification many times. Herein, deepface has an out-of-the-box find function to handle this action. It's going to look for the identity of input image in the database path and it will return pandas data frame as output.

df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")

Face recognition models - Demo

Deepface is a hybrid face recognition package. It currently wraps many state-of-the-art face recognition models: VGG-Face , Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. The default configuration uses VGG-Face model.

models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", model_name = models[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", model_name = models[1])

FaceNet, VGG-Face, ArcFace and Dlib are overperforming ones based on experiments. You can find out the scores of those models below on both Labeled Faces in the Wild and YouTube Faces in the Wild data sets declared by its creators.

ModelLFW ScoreYTF Score
Facenet51299.65%-
ArcFace99.41%-
Dlib99.38 %-
Facenet99.20%-
VGG-Face98.78%97.40%
Human-beings97.53%-
OpenFace93.80%-
DeepID-97.05%

Similarity

Face recognition models are regular convolutional neural networks and they are responsible to represent faces as vectors. We expect that a face pair of same person should be more similar than a face pair of different persons.

Similarity could be calculated by different metrics such as Cosine Similarity, Euclidean Distance and L2 form. The default configuration uses cosine similarity.

metrics = ["cosine", "euclidean", "euclidean_l2"]

#face verification
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", distance_metric = metrics[1])

#face recognition
df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db", distance_metric = metrics[1])

Euclidean L2 form seems to be more stable than cosine and regular Euclidean distance based on experiments.

Facial Attribute Analysis - Demo

Deepface also comes with a strong facial attribute analysis module including age, gender, facial expression (including angry, fear, neutral, sad, disgust, happy and surprise) and race (including asian, white, middle eastern, indian, latino and black) predictions.

obj = DeepFace.analyze(img_path = "img4.jpg", actions = ['age', 'gender', 'race', 'emotion'])

Age model got ± 4.65 MAE; gender model got 97.44% accuracy, 96.29% precision and 95.05% recall as mentioned in its tutorial.

Streaming and Real Time Analysis - Demo

You can run deepface for real time videos as well. Stream function will access your webcam and apply both face recognition and facial attribute analysis. The function starts to analyze a frame if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.

DeepFace.stream(db_path = "C:/User/Sefik/Desktop/database")

Even though face recognition is based on one-shot learning, you can use multiple face pictures of a person as well. You should rearrange your directory structure as illustrated below.

user
├── database
│   ├── Alice
│   │   ├── Alice1.jpg
│   │   ├── Alice2.jpg
│   ├── Bob
│   │   ├── Bob.jpg

Face Detectors - Demo

Face detection and alignment are important early stages of a modern face recognition pipeline. Experiments show that just alignment increases the face recognition accuracy almost 1%. OpenCV, SSD, Dlib, MTCNN, RetinaFace and MediaPipe detectors are wrapped in deepface.

All deepface functions accept an optional detector backend input argument. You can switch among those detectors with this argument. OpenCV is the default detector.

backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe']

#face verification
obj = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg", detector_backend = backends[4])

#face recognition
df = DeepFace.find(img_path = "img.jpg", db_path = "my_db", detector_backend = backends[4])

#facial analysis
demography = DeepFace.analyze(img_path = "img4.jpg", detector_backend = backends[4])

#face detection and alignment
face = DeepFace.detectFace(img_path = "img.jpg", target_size = (224, 224), detector_backend = backends[4])

Face recognition models are actually CNN models and they expect standard sized inputs. So, resizing is required before representation. To avoid deformation, deepface adds black padding pixels according to the target size argument after detection and alignment.

RetinaFace and MTCNN seem to overperform in detection and alignment stages but they are much slower. If the speed of your pipeline is more important, then you should use opencv or ssd. On the other hand, if you consider the accuracy, then you should use retinaface or mtcnn.

The performance of RetinaFace is very satisfactory even in the crowd as seen in the following illustration. Besides, it comes with an incredible facial landmark detection performance. Highlighted red points show some facial landmarks such as eyes, nose and mouth. That's why, alignment score of RetinaFace is high as well.

You can find out more about RetinaFace on this repo.

API - Demo

Deepface serves an API as well. You can clone /api/api.py and pass it to python command as an argument. This will get a rest service up. In this way, you can call deepface from an external system such as mobile app or web.

python api.py

Face recognition, facial attribute analysis and vector representation functions are covered in the API. You are expected to call these functions as http post methods. Service endpoints will be http://127.0.0.1:5000/verify for face recognition, http://127.0.0.1:5000/analyze for facial attribute analysis, and http://127.0.0.1:5000/represent for vector representation. You should pass input images as base64 encoded string in this case. Here, you can find a postman project.

Tech Stack - Vlog, Tutorial

Face recognition models represent facial images as vector embeddings. The idea behind facial recognition is that vectors should be more similar for same person than different persons. The question is that where and how to store facial embeddings in a large scale system. Herein, deepface offers a represention function to find vector embeddings from facial images.

embedding = DeepFace.represent(img_path = "img.jpg", model_name = 'Facenet')

Tech stack is vast to store vector embeddings. To determine the right tool, you should consider your task such as face verification or face recognition, priority such as speed or confidence, and also data size.

Contribution

Pull requests are welcome. You should run the unit tests locally by running test/unit_tests.py. Please share the unit test result logs in the PR. Deepface is currently compatible with TF 1 and 2 versions. Change requests should satisfy those requirements both.

Support

There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏

You can also support this work on Patreon

 

Citation

Please cite deepface in your publications if it helps your research. Here are BibTeX entries:

@inproceedings{serengil2020lightface,
  title        = {LightFace: A Hybrid Deep Face Recognition Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2020 Innovations in Intelligent Systems and Applications Conference (ASYU)},
  pages        = {23-27},
  year         = {2020},
  doi          = {10.1109/ASYU50717.2020.9259802},
  url          = {https://doi.org/10.1109/ASYU50717.2020.9259802},
  organization = {IEEE}
}
@inproceedings{serengil2021lightface,
  title        = {HyperExtended LightFace: A Facial Attribute Analysis Framework},
  author       = {Serengil, Sefik Ilkin and Ozpinar, Alper},
  booktitle    = {2021 International Conference on Engineering and Emerging Technologies (ICEET)},
  pages        = {1-4},
  year         = {2021},
  doi          = {10.1109/ICEET53442.2021.9659697},
  url          = {https://doi.org/10.1109/ICEET53442.2021.9659697},
  organization = {IEEE}
}

Also, if you use deepface in your GitHub projects, please add deepface in the requirements.txt.

Download Details:
Author: serengil
Source Code: https://github.com/serengil/deepface
License: MIT License

#tensorflow  #python #machinelearning 

Carmen  Grimes

Carmen Grimes

1595491178

Best Electric Bikes and Scooters for Rental Business or Campus Facility

The electric scooter revolution has caught on super-fast taking many cities across the globe by storm. eScooters, a renovated version of old-school scooters now turned into electric vehicles are an environmentally friendly solution to current on-demand commute problems. They work on engines, like cars, enabling short traveling distances without hassle. The result is that these groundbreaking electric machines can now provide faster transport for less — cheaper than Uber and faster than Metro.

Since they are durable, fast, easy to operate and maintain, and are more convenient to park compared to four-wheelers, the eScooters trend has and continues to spike interest as a promising growth area. Several companies and universities are increasingly setting up shop to provide eScooter services realizing a would-be profitable business model and a ready customer base that is university students or residents in need of faster and cheap travel going about their business in school, town, and other surrounding areas.

Electric Scooters Trends and Statistics

In many countries including the U.S., Canada, Mexico, U.K., Germany, France, China, Japan, India, Brazil and Mexico and more, a growing number of eScooter users both locals and tourists can now be seen effortlessly passing lines of drivers stuck in the endless and unmoving traffic.

A recent report by McKinsey revealed that the E-Scooter industry will be worth― $200 billion to $300 billion in the United States, $100 billion to $150 billion in Europe, and $30 billion to $50 billion in China in 2030. The e-Scooter revenue model will also spike and is projected to rise by more than 20% amounting to approximately $5 billion.

And, with a necessity to move people away from high carbon prints, traffic and congestion issues brought about by car-centric transport systems in cities, more and more city planners are developing more bike/scooter lanes and adopting zero-emission plans. This is the force behind the booming electric scooter market and the numbers will only go higher and higher.

Companies that have taken advantage of the growing eScooter trend develop an appthat allows them to provide efficient eScooter services. Such an app enables them to be able to locate bike pick-up and drop points through fully integrated google maps.

List of Best Electric Bikes for Rental Business or Campus Facility 2020:

It’s clear that e scooters will increasingly become more common and the e-scooter business model will continue to grab the attention of manufacturers, investors, entrepreneurs. All this should go ahead with a quest to know what are some of the best electric bikes in the market especially for anyone who would want to get started in the electric bikes/scooters rental business.

We have done a comprehensive list of the best electric bikes! Each bike has been reviewed in depth and includes a full list of specs and a photo.

Billy eBike

mobile-best-electric-bikes-scooters https://www.kickstarter.com/projects/enkicycles/billy-were-redefining-joyrides

To start us off is the Billy eBike, a powerful go-anywhere urban electric bike that’s specially designed to offer an exciting ride like no other whether you want to ride to the grocery store, cafe, work or school. The Billy eBike comes in 4 color options – Billy Blue, Polished aluminium, Artic white, and Stealth black.

Price: $2490

Available countries

Available in the USA, Europe, Asia, South Africa and Australia.This item ships from the USA. Buyers are therefore responsible for any taxes and/or customs duties incurred once it arrives in your country.

Features

  • Control – Ride with confidence with our ultra-wide BMX bars and a hyper-responsive twist throttle.
  • Stealth- Ride like a ninja with our Gates carbon drive that’s as smooth as butter and maintenance-free.
  • Drive – Ride further with our high torque fat bike motor, giving a better climbing performance.
  • Accelerate – Ride quicker with our 20-inch lightweight cutout rims for improved acceleration.
  • Customize – Ride your own way with 5 levels of power control. Each level determines power and speed.
  • Flickable – Ride harder with our BMX /MotoX inspired geometry and lightweight aluminum package

Specifications

  • Maximum speed: 20 mph (32 km/h)
  • Range per charge: 41 miles (66 km)
  • Maximum Power: 500W
  • Motor type: Fat Bike Motor: Bafang RM G060.500.DC
  • Load capacity: 300lbs (136kg)
  • Battery type: 13.6Ah Samsung lithium-ion,
  • Battery capacity: On/off-bike charging available
  • Weight: w/o batt. 48.5lbs (22kg), w/ batt. 54lbs (24.5kg)
  • Front Suspension: Fully adjustable air shock, preload/compression damping /lockout
  • Rear Suspension: spring, preload adjustment
  • Built-in GPS

Why Should You Buy This?

  • Riding fun and excitement
  • Better climbing ability and faster acceleration.
  • Ride with confidence
  • Billy folds for convenient storage and transportation.
  • Shorty levers connect to disc brakes ensuring you stop on a dime
  • belt drives are maintenance-free and clean (no oil or lubrication needed)

**Who Should Ride Billy? **

Both new and experienced riders

**Where to Buy? **Local distributors or ships from the USA.

Genze 200 series e-Bike

genze-best-electric-bikes-scooters https://www.genze.com/fleet/

Featuring a sleek and lightweight aluminum frame design, the 200-Series ebike takes your riding experience to greater heights. Available in both black and white this ebike comes with a connected app, which allows you to plan activities, map distances and routes while also allowing connections with fellow riders.

Price: $2099.00

Available countries

The Genze 200 series e-Bike is available at GenZe retail locations across the U.S or online via GenZe.com website. Customers from outside the US can ship the product while incurring the relevant charges.

Features

  • 2 Frame Options
  • 2 Sizes
  • Integrated/Removable Battery
  • Throttle and Pedal Assist Ride Modes
  • Integrated LCD Display
  • Connected App
  • 24 month warranty
  • GPS navigation
  • Bluetooth connectivity

Specifications

  • Maximum speed: 20 mph with throttle
  • Range per charge: 15-18 miles w/ throttle and 30-50 miles w/ pedal assist
  • Charging time: 3.5 hours
  • Motor type: Brushless Rear Hub Motor
  • Gears: Microshift Thumb Shifter
  • Battery type: Removable Samsung 36V, 9.6AH Li-Ion battery pack
  • Battery capacity: 36V and 350 Wh
  • Weight: 46 pounds
  • Derailleur: 8-speed Shimano
  • Brakes: Dual classic
  • Wheels: 26 x 20 inches
  • Frame: 16, and 18 inches
  • Operating Mode: Analog mode 5 levels of Pedal Assist Thrott­le Mode

Norco from eBikestore

norco-best-electric-bikes-scooters https://ebikestore.com/shop/norco-vlt-s2/

The Norco VLT S2 is a front suspension e-Bike with solid components alongside the reliable Bosch Performance Line Power systems that offer precise pedal assistance during any riding situation.

Price: $2,699.00

Available countries

This item is available via the various Norco bikes international distributors.

Features

  • VLT aluminum frame- for stiffness and wheel security.
  • Bosch e-bike system – for their reliability and performance.
  • E-bike components – for added durability.
  • Hydraulic disc brakes – offer riders more stopping power for safety and control at higher speeds.
  • Practical design features – to add convenience and versatility.

Specifications

  • Maximum speed: KMC X9 9spd
  • Motor type: Bosch Active Line
  • Gears: Shimano Altus RD-M2000, SGS, 9 Speed
  • Battery type: Power Pack 400
  • Battery capacity: 396Wh
  • Suspension: SR Suntour suspension fork
  • Frame: Norco VLT, Aluminum, 12x142mm TA Dropouts

Bodo EV

bodo-best-electric-bikes-scootershttp://www.bodoevs.com/bodoev/products_show.asp?product_id=13

Manufactured by Bodo Vehicle Group Limited, the Bodo EV is specially designed for strong power and extraordinary long service to facilitate super amazing rides. The Bodo Vehicle Company is a striking top in electric vehicles brand field in China and across the globe. Their Bodo EV will no doubt provide your riders with high-level riding satisfaction owing to its high-quality design, strength, breaking stability and speed.

Price: $799

Available countries

This item ships from China with buyers bearing the shipping costs and other variables prior to delivery.

Features

  • Reliable
  • Environment friendly
  • Comfortable riding
  • Fashionable
  • Economical
  • Durable – long service life
  • Braking stability
  • LED lighting technology

Specifications

  • Maximum speed: 45km/h
  • Range per charge: 50km per person
  • Charging time: 8 hours
  • Maximum Power: 3000W
  • Motor type: Brushless DC Motor
  • Load capacity: 100kg
  • Battery type: Lead-acid battery
  • Battery capacity: 60V 20AH
  • Weight: w/o battery 47kg

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Carmen  Grimes

Carmen Grimes

1595494844

How to start an electric scooter facility/fleet in a university campus/IT park

Are you leading an organization that has a large campus, e.g., a large university? You are probably thinking of introducing an electric scooter/bicycle fleet on the campus, and why wouldn’t you?

Introducing micro-mobility in your campus with the help of such a fleet would help the people on the campus significantly. People would save money since they don’t need to use a car for a short distance. Your campus will see a drastic reduction in congestion, moreover, its carbon footprint will reduce.

Micro-mobility is relatively new though and you would need help. You would need to select an appropriate fleet of vehicles. The people on your campus would need to find electric scooters or electric bikes for commuting, and you need to provide a solution for this.

To be more specific, you need a short-term electric bike rental app. With such an app, you will be able to easily offer micro-mobility to the people on the campus. We at Devathon have built Autorent exactly for this.

What does Autorent do and how can it help you? How does it enable you to introduce micro-mobility on your campus? We explain these in this article, however, we will touch upon a few basics first.

Micro-mobility: What it is

micro-mobility

You are probably thinking about micro-mobility relatively recently, aren’t you? A few relevant insights about it could help you to better appreciate its importance.

Micro-mobility is a new trend in transportation, and it uses vehicles that are considerably smaller than cars. Electric scooters (e-scooters) and electric bikes (e-bikes) are the most popular forms of micro-mobility, however, there are also e-unicycles and e-skateboards.

You might have already seen e-scooters, which are kick scooters that come with a motor. Thanks to its motor, an e-scooter can achieve a speed of up to 20 km/h. On the other hand, e-bikes are popular in China and Japan, and they come with a motor, and you can reach a speed of 40 km/h.

You obviously can’t use these vehicles for very long commutes, however, what if you need to travel a short distance? Even if you have a reasonable public transport facility in the city, it might not cover the route you need to take. Take the example of a large university campus. Such a campus is often at a considerable distance from the central business district of the city where it’s located. While public transport facilities may serve the central business district, they wouldn’t serve this large campus. Currently, many people drive their cars even for short distances.

As you know, that brings its own set of challenges. Vehicular traffic adds significantly to pollution, moreover, finding a parking spot can be hard in crowded urban districts.

Well, you can reduce your carbon footprint if you use an electric car. However, electric cars are still new, and many countries are still building the necessary infrastructure for them. Your large campus might not have the necessary infrastructure for them either. Presently, electric cars don’t represent a viable option in most geographies.

As a result, you need to buy and maintain a car even if your commute is short. In addition to dealing with parking problems, you need to spend significantly on your car.

All of these factors have combined to make people sit up and think seriously about cars. Many people are now seriously considering whether a car is really the best option even if they have to commute only a short distance.

This is where micro-mobility enters the picture. When you commute a short distance regularly, e-scooters or e-bikes are viable options. You limit your carbon footprints and you cut costs!

Businesses have seen this shift in thinking, and e-scooter companies like Lime and Bird have entered this field in a big way. They let you rent e-scooters by the minute. On the other hand, start-ups like Jump and Lyft have entered the e-bike market.

Think of your campus now! The people there might need to travel short distances within the campus, and e-scooters can really help them.

How micro-mobility can benefit you

benefits-micromobility

What advantages can you get from micro-mobility? Let’s take a deeper look into this question.

Micro-mobility can offer several advantages to the people on your campus, e.g.:

  • Affordability: Shared e-scooters are cheaper than other mass transportation options. Remember that the people on your campus will use them on a shared basis, and they will pay for their short commutes only. Well, depending on your operating model, you might even let them use shared e-scooters or e-bikes for free!
  • Convenience: Users don’t need to worry about finding parking spots for shared e-scooters since these are small. They can easily travel from point A to point B on your campus with the help of these e-scooters.
  • Environmentally sustainable: Shared e-scooters reduce the carbon footprint, moreover, they decongest the roads. Statistics from the pilot programs in cities like Portland and Denver showimpressive gains around this key aspect.
  • Safety: This one’s obvious, isn’t it? When people on your campus use small e-scooters or e-bikes instead of cars, the problem of overspeeding will disappear. you will see fewer accidents.

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Fredy  Larson

Fredy Larson

1595059664

How long does it take to develop/build an app?

With more of us using smartphones, the popularity of mobile applications has exploded. In the digital era, the number of people looking for products and services online is growing rapidly. Smartphone owners look for mobile applications that give them quick access to companies’ products and services. As a result, mobile apps provide customers with a lot of benefits in just one device.

Likewise, companies use mobile apps to increase customer loyalty and improve their services. Mobile Developers are in high demand as companies use apps not only to create brand awareness but also to gather information. For that reason, mobile apps are used as tools to collect valuable data from customers to help companies improve their offer.

There are many types of mobile applications, each with its own advantages. For example, native apps perform better, while web apps don’t need to be customized for the platform or operating system (OS). Likewise, hybrid apps provide users with comfortable user experience. However, you may be wondering how long it takes to develop an app.

To give you an idea of how long the app development process takes, here’s a short guide.

App Idea & Research

app-idea-research

_Average time spent: two to five weeks _

This is the initial stage and a crucial step in setting the project in the right direction. In this stage, you brainstorm ideas and select the best one. Apart from that, you’ll need to do some research to see if your idea is viable. Remember that coming up with an idea is easy; the hard part is to make it a reality.

All your ideas may seem viable, but you still have to run some tests to keep it as real as possible. For that reason, when Web Developers are building a web app, they analyze the available ideas to see which one is the best match for the targeted audience.

Targeting the right audience is crucial when you are developing an app. It saves time when shaping the app in the right direction as you have a clear set of objectives. Likewise, analyzing how the app affects the market is essential. During the research process, App Developers must gather information about potential competitors and threats. This helps the app owners develop strategies to tackle difficulties that come up after the launch.

The research process can take several weeks, but it determines how successful your app can be. For that reason, you must take your time to know all the weaknesses and strengths of the competitors, possible app strategies, and targeted audience.

The outcomes of this stage are app prototypes and the minimum feasible product.

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