?>

Integrate Canny with ShipRocket

Appy Pie Connect allows you to automate multiple workflows between Canny and ShipRocket

  • No code
  • No Credit Card
  • Lightning Fast Setup
20 Million man hours saved

Award Winning App Integration Platform

About Canny

Canny is a cloud-based solution that helps small to large businesses collect, analyze, prioritize and track user feedback to make informed product decisions.

About ShipRocket

Shiprocket is a technologically advanced logistics platform that connects retailers, consumers, and supply chain partners to create great shipping experiences.

ShipRocket Integrations

Best ways to Integrate Canny + ShipRocket

  • Canny Integration ShipRocket Integration

    Canny + ShipRocket

    Add New Product in shiprocket when New Post is created in Canny Read More...
    Close
    When this happens...
    Canny Integration New Post
     
    Then do this...
    ShipRocket Integration Add New Product
  • Canny Integration ShipRocket Integration

    Canny + ShipRocket

    Create Custom Order to shiprocket from New Post in Canny Read More...
    Close
    When this happens...
    Canny Integration New Post
     
    Then do this...
    ShipRocket Integration Create Custom Order
  • Canny Integration ShipRocket Integration

    Canny + ShipRocket

    Create a Return Order to shiprocket from New Post in Canny Read More...
    Close
    When this happens...
    Canny Integration New Post
     
    Then do this...
    ShipRocket Integration Create a Return Order
  • Canny Integration ShipRocket Integration

    Canny + ShipRocket

    Cancel an Order in shiprocket when New Post is created in Canny Read More...
    Close
    When this happens...
    Canny Integration New Post
     
    Then do this...
    ShipRocket Integration Cancel an Order
  • Canny Integration ShipRocket Integration

    Canny + ShipRocket

    Update Order in shiprocket when New Post is created in Canny Read More...
    Close
    When this happens...
    Canny Integration New Post
     
    Then do this...
    ShipRocket Integration Update Order
  • Canny Integration {{item.actionAppName}} Integration

    Canny + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} Integration {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} Integration {{item.actionTitle}}
Connect Canny + ShipRocket in easier way

It's easy to connect Canny + ShipRocket without coding knowledge. Start creating your own business flow.

    Triggers
  • New Comment

    Triggers when a new comment is created.

  • New Post

    Triggers when a new post is created.

  • New Vote

    Triggers when a new vote is created.

  • Post Status Change

    Triggers when a post's status is changed.

  • New Order

    Triggers when a new order is created.

  • New Product

    Triggers when a new product is created.

  • New Shipment

    Triggers when a new shipment is created.

    Actions
  • Change Post Status

    Changes a post's status.

  • Add New Product

    Creates a new product.

  • Cancel an Order

    Cancel an order

  • Create Custom Order

    Creates a custom order.

  • Create a Return Order

    Create a return order

  • Update Order

    Update an existing order.

Compliance Certifications and Memberships

Highly rated by thousands of customers all over the world

We’ve been featured on

featuredon
Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How Canny & ShipRocket Integrations Work

  1. Step 1: Choose Canny as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick ShipRocket as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Canny to ShipRocket.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Canny and ShipRocket

Canny?

Canny is a software intended for scanning and recognition of the objects. It has three different types such as edge, corner or blob detector. It can recognize the object which is closed or not closed. Canny is mainly used in the image processing and pattern recognition.

ShipRocket?

Shiprocket is an open source project that is a combination of a number of libraries. The main objective of this project is to create a single library which will combine Canny, libsvm and Liblinear. This gives a chance to use these tops in a single topbox. It also includes the MALLET library for machine learning, OpenCV library for computer vision, and TrieMap for trie based indexing.

Integration of Canny and ShipRocket

In this part, we will discuss about the integration of Canny and ShipRocket. Shiprocket provides an easy way to use several algorithms from different libraries in a single place. We can integrate Canny and Shiprocket easily using a topbox shipped with Shiprocket.

We have to require “Canny” and “Shiprocket” in our script to start using them.

#!/usr/bin/env python # -- coding. utf-8 -- # Using Canny package from Shiprocket import * from PIL import Image from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix from sklearn.metrics import precision_recall_curve import urllib2 import os import sys import time class myModel(object). def __init__(self). pass def load_model(self, model_file_name). model = load(model_file_name. return model def train(self, data). return self.train_model(data. def train_model(self, data). X = [] y = [] X = [i[0] for i in data] y = [i[1] for i in data] self.model = myModel(. i = 0 for j in range(len(X)). i += 1 x = X[j] y = y[j] score = self.model.predict(x. if score > 0. X.append(x. y.append(1. else. X.append(x. y.append(0. print 'Accuracy is ', self.model.accuracy(. def predict(self, x). return self.model.predict(x. def get_canny_threshpd(self, img). threshpdArray = cv2.threshpd(img, 0, 255, cv2.THRESH_BINARY); return threshpdArray def get_canny_features(self, img). outputs = cv2.Canny(img, 100, 745. return outputs def run(). args = sys.argv[1:] if args == ''. args = ['--help'] if len(args. < 2. print 'Usage. {} <image file>'.format(args[0]. exit(-1. filename = args[1] img = Image.open(filename. img = img.resize((480, 480). threshpdArray = get_canny_threshpd(img. features = get_canny_features(img. print classification_report(features, labels=['spotted','not spotted']. preds = self.predict(features. predictedLabelSet = [] for pred in preds. predictedLabelSet += ['spotted'] + pred predictedLabelSet += ['not spotted'] + pred print print 'Classification Zone:' print classification_report(predictedLabelSet, labels=['spotted','not spotted']. predictedLabelSet = [] for pred in preds. predictedLabelSet += ['spotted'] + pred predictedLabelSet += ['not spotted'] + pred blur = cv2.GaussianBlur(preds, (20, 20), 0.astype('uint8'. print classification_report(predictedLabelSet, labels=['spotted','not spotted']. rawPreds = [] for pred in predictedLabelSet. rawPreds += [pred] features = get_canny_features(blur. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. probabilites = get_probabilities(features, rawPreds. print predictionScoresScatterplot(. print precisionRecallCurve(. print confusionMatrix(. def predictionScoresScatterplot(). predResultScatterplotResults=[ ] for i in range (0 , len (predictedLabelSet )). xscore , yscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore , zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ,zscore ] print 'Score distribution:' print 'The relative score for each class:' for i in range (0 , len (predictedLabelSet )). poslabel , neglabel = labels [ i ] poslabel += "+" neglabel += "-".join("+" if p == 1 else "-" if p == 0 else " " . xypospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospospos posnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegnegneg neg xy xy xy xy xy xy xy xy xy xy xy xy xy xy xy xy x

The process to integrate Canny and ShipRocket may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.