trash_recog.py

'''
Created on Apr 24, 2016

@author: tisham
'''
import time 
import requests
import cv2
import operator
import numpy as np
# from grove_rgb_lcd import *

_url = 'https://api.projectoxford.ai/vision/v1.0/analyze?visualFeatures=Description,Tags'
_key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'  # Here you have to paste your primary key
_maxNumRetries = 10

# Import library to display results
import matplotlib.pyplot as plt
from pylab import show

def processRequest(json, data, headers, params=None):

    """
    Helper function to process the request to Project Oxford

    Parameters:
    json: Used when processing images from its URL. See API Documentation
    data: Used when processing image read from disk. See API Documentation
    headers: Used to pass the key information and the data type request
    """

    retries = 0
    result = None

    while True:

        response = requests.request('post', _url, json=json, data=data, headers=headers, params=params)

        if response.status_code == 429: 

            print "Message: %s" % (response.json()['error']['message'])

            if retries <= _maxNumRetries: 
                time.sleep(1) 
                retries += 1
                continue
            else: 
                print 'Error: failed after retrying!'
                break

        elif response.status_code == 200 or response.status_code == 201:

            if 'content-length' in response.headers and int(response.headers['content-length']) == 0: 
                result = None 
            elif 'content-type' in response.headers and isinstance(response.headers['content-type'], str): 
                if 'application/json' in response.headers['content-type'].lower(): 
                    result = response.json() if response.content else None 
                elif 'image' in response.headers['content-type'].lower(): 
                    result = response.content
        else:
            print "Error code: %d" % (response.status_code)
            print "Message: %s" % (response.json()['error']['message'])

        break
        
    return result


def renderResultOnImage(result, img):
    
    """Display the obtained results onto the input image"""

    R = int(result['color']['accentColor'][:2], 16)
    G = int(result['color']['accentColor'][2:4], 16)
    B = int(result['color']['accentColor'][4:], 16)

    cv2.rectangle(img, (0, 0), (img.shape[1], img.shape[0]), color=(R, G, B), thickness=25)

    if 'categories' in result:
        categoryName = sorted(result['categories'], key=lambda x: x['score'])[0]['name']
        cv2.putText(img, categoryName, (30, 70), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 3)

def showResultOnLCD(result):
    """Display recognition result on Grove-LCD"""
    
    if 'description' in result:
        caption = result['description']['captions'][0]['text']
        print(caption)
        
    # setText(categoryName)
    # setRGB(0,0,64)
   


if __name__ == '__main__':
    
    # URL direction to image
    # urlImage = 'https://oxfordportal.blob.core.windows.net/vision/Analysis/3.jpg'

    # Capture image using opencv
    cap = cv2.VideoCapture(0)
    # set the width and height, and UNSUCCESSFULLY set the exposure time
    cap.set(3, 1280)
    cap.set(4, 1024)
    cap.set(15, 0.1)
    ret, img = cap.read()
    cv2.imwrite('temp_cap.jpg', img)

    pathToFileInDisk = r'temp_cap.jpg'
    with open(pathToFileInDisk, 'rb') as f:
        data = f.read()
    
    # Computer Vision parameters
    params = { 'visualFeatures' : 'Color,Categories'} 
    
    headers = dict()
    headers['Ocp-Apim-Subscription-Key'] = _key
    headers['Content-Type'] = 'application/octet-stream'
    
    # json = { 'url': urlImage } 
    json = None
    # data = None
    
    result = processRequest(json, data, headers, params)
    print(result)
    
    # Load the original image, fetched from the URL
    # arr = np.asarray( bytearray( requests.get( urlImage ).content ), dtype=np.uint8 )
    # img = cv2.cvtColor( cv2.imdecode( arr, -1 ), cv2.COLOR_BGR2RGB )
    
    showResultOnLCD(result)
    
    # ig, ax = plt.subplots(figsize=(15, 20))
    # ax.imshow( img )
    # show()