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Diff for: ‎HogDescriptor.py

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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from skimage.feature import hog
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from skimage import data, color, exposure
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class HogDescriptor:
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def describe_hog(self, image):
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image = color.rgb2gray(image)
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fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
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cells_per_block=(1, 1), visualise=True)
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return fd
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Diff for: ‎HogDescriptor.pyc

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Diff for: ‎app.py

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# http://flask.pocoo.org/docs/patterns/fileuploads/
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import os, glob
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import flask_search
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from flask import Flask, request, redirect, url_for, send_from_directory, jsonify, send_file, render_template
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from werkzeug import secure_filename
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import shutil
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UPLOAD_FOLDER = 'uploads'
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ALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif','ppm'])
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app = Flask(__name__)
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app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
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basedir = os.path.abspath(os.path.dirname(__file__))
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def allowed_file(filename):
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# this has changed from the original example because the original did not work for me
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return filename[-3:].lower() in ALLOWED_EXTENSIONS
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@app.route('/query', methods=['POST'])
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def upload_file():
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if request.method == 'POST':
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# check if the post request has the file part
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if 'file' not in request.files:
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print ('No file part')
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return redirect(request.url)
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print ("POST request", request.files)
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file = request.files['file']
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# if file and allowed_file(file.filename):
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if True:
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print '**found file', file.filename
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filename = secure_filename(file.filename)
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filepath = os.path.join(basedir, app.config['UPLOAD_FOLDER'], filename)
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file.save(filepath)
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result_from_search = flask_search.flask_search(filepath)
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shutil.rmtree('query_images')
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os.mkdir('query_images')
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i = 9
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for _, res in result_from_search:
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shutil.copyfile('original1/'+ res, 'query_images/' +str(i)+ res)
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i = i-1
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# result_path = cd.function(file)
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# for browser, add 'redirect' function on top of 'url_for'
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# response = send_file(result_from_search[1])
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return jsonify(result_from_search)
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# return '''
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# <!doctype html>
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# <title>Upload new File</title>
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# <h1>Upload new File</h1>
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# <form action="" method=post enctype=multipart/form-data>
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# <p><input type=file name=file>
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# <input type=submit value=Upload>
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# </form>
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# '''
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return "Error in request processing"
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@app.route('/')
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def home_page():
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files = [os.path.basename(x) for x in glob.glob('./query_images/*.png')]
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return render_template("home.html", urls=files)
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@app.route('/result/<filename>')
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def uploaded_file(filename):
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return send_from_directory("query_images", filename)
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@app.route('/hello', methods=['GET', 'POST'])
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def hello_world():
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print (request.method)
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if request.method == 'GET':
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var = "somethig"
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msg = "Hi!! you sent GET Req"
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return render_template('response.html', msg=msg)
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if request.method == 'POST':
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args = request.json
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print (args)
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msg = "Hi!! you sent POST Req"
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return msg
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if __name__ == '__main__':
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app.run(debug=True, host='0.0.0.0')

Diff for: ‎colordescriptor.py

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# import the necessary packages
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import numpy as np
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import cv2
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class ColorDescriptor:
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def __init__(self, bins):
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# store the number of bins for the 3D histogram
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self.bins = bins
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def describe(self, image):
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# convert the image to the HSV color space and initialize
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# the features used to quantify the image
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image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
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features = []
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# grab the dimensions and compute the center of the image
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(h, w) = image.shape[:2]
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(cX, cY) = (int(w * 0.5), int(h * 0.5))
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# divide the image into four rectangles/segments (top-left,
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# top-right, bottom-right, bottom-left)
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segments = [(0, cX, 0, cY), (cX, w, 0, cY), (cX, w, cY, h),
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(0, cX, cY, h)]
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# construct an elliptical mask representing the center of the
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# image
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(axesX, axesY) = (int(w * 0.75) / 2, int(h * 0.75) / 2)
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ellipMask = np.zeros(image.shape[:2], dtype = "uint8")
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cv2.ellipse(ellipMask, (cX, cY), (int(axesX), int(axesY)), 0, 0, 360, 255, -1)
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# loop over the segments
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for (startX, endX, startY, endY) in segments:
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# construct a mask for each corner of the image, subtracting
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# the elliptical center from it
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cornerMask = np.zeros(image.shape[:2], dtype = "uint8")
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cv2.rectangle(cornerMask, (startX, startY), (endX, endY), 255, -1)
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cornerMask = cv2.subtract(cornerMask, ellipMask)
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# extract a color histogram from the image, then update the
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# feature vector
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hist = self.histogram(image, cornerMask)
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features.extend(hist)
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# extract a color histogram from the elliptical region and
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# update the feature vector
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hist = self.histogram(image, ellipMask)
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features.extend(hist)
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# return the feature vector
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return features
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def histogram(self, image, mask):
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# extract a 3D color histogram from the masked region of the
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# image, using the supplied number of bins per channel; then
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# normalize the histogram
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hist = cv2.calcHist([image], [0, 1, 2], mask, self.bins,
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[0, 180, 0, 256, 0, 256])
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hist = cv2.normalize(hist, hist).flatten()
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# return the histogram
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return hist

Diff for: ‎colordescriptor.pyc

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Diff for: ‎flask_search.py

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from colordescriptor import ColorDescriptor
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import numpy as np
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from pyimagesearch.zernikemoments import ZernikeMoments
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from HogDescriptor import HogDescriptor
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from hu import ShapeDescriptor
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from searcher import Searcher
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import argparse
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import cv2
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def flask_search(image1):
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cd = HogDescriptor()
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# load the query image and describe it
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print ("before ", image1)
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image = cv2.imread(image1)
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print ("something ", image)
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image = (255 - image)
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image = cv2.resize(image, (256,256))
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query = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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features = cd.describe_hog(query)
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# perform the search
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# args['index'] is not defined
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searcher = Searcher('hog_index.csv')
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results = searcher.search(features)
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print (results)
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return results
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# display the query
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# cv2.imshow("Query", query)
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# print ("Reached here")
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# loop over the results
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# for (score, resultID) in results:
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# # load the result image and display it
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# result = cv2.imread(args["result_path"] + "/" + resultID)
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# path.extend(resultID)
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# #cv2.imshow("Result", result)
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# #cv2.waitKey(0)
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# return path

Diff for: ‎flask_search.pyc

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Diff for: ‎hog_cropped.csv

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Diff for: ‎hog_index.py

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# import the necessary packages
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from colordescriptor import ColorDescriptor
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from HogDescriptor import HogDescriptor
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import argparse
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import glob
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import cv2
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# construct the argument parser and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-d", "--dataset", required = True,
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help = "Path to the directory that contains the images to be indexed")
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ap.add_argument("-i", "--index", required = True,
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help = "Path to where the computed index will be stored")
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args = vars(ap.parse_args())
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# initialize the Shape descriptor
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cd = HogDescriptor()
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# open the output index file for writing
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output = open(args["index"], "w")
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# use glob to grab the image paths and loop over them
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for imagePath in glob.glob(args["dataset"] + "/*.png"):
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# extract the image ID (i.e. the unique filename) from the image
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# path and load the image itself
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print ("Saving for image ", imagePath)
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imageID = imagePath[imagePath.rfind("/") + 1:]
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image = cv2.imread(imagePath)
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image = cv2.resize(image, (256, 256))
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# describe the image
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features = cd.describe_hog(image)
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# write the features to file
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features = [str(f) for f in features]
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output.write("%s,%s\n" % (imageID, ",".join(features)))
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# close the index file
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output.close()

Diff for: ‎hu.py

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# import the necessary packages
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import numpy as np
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import cv2
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import argparse
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class ShapeDescriptor:
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def describe1(self, image):
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# convert it to grayscale, and display it
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#gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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#cv2.imshow("image", image)
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# extract Hu Moments from the image -- this list of numbers
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# is our 'feature vector' used to quantify the shape of the
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# object in our image
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features = cv2.HuMoments(cv2.moments(image)).flatten()
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# return the feature vector
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return features
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Diff for: ‎hu.pyc

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Diff for: ‎plot.py

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import sys
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import cv2
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import numpy as np
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import pylab as pl
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from sklearn.metrics import precision_recall_curve
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recall=[0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
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precision=[1,1,1,1,1,1,1,1,1,1,1]
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def main(recall,precision,pr):
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pl.clf()
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#for precision, recall, label in lines:
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f=pl.plot(recall, precision)
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pl.xlabel('Recall')
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pl.ylabel('Precision')
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pl.ylim([0.0, 2.5])
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pl.xlim([0.0, 1.0])
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pl.title('Precision-Recall')
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pl.legend(loc="upper right")
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#pl.show()
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pl.savefig(pr)
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img=cv2.imread(pr)
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cv2.imshow('pr',img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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main(recall,precision,'pr_curve.tiff')

Diff for: ‎pyimagesearch/__init__.pyc

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Diff for: ‎pyimagesearch/zernikemoments.py

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# import the necessary packages
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import mahotas
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class ZernikeMoments:
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def __init__(self, radius):
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# store the size of the radius that will be
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# used when computing moments
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self.radius = radius
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def describe(self, image):
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# return the Zernike moments for the image
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return mahotas.features.zernike_moments(image, self.radius)

Diff for: ‎pyimagesearch/zernikemoments.pyc

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Diff for: ‎search.py

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# import the necessary packages
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from colordescriptor import ColorDescriptor
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import numpy as np
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from pyimagesearch.zernikemoments import ZernikeMoments
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from HogDescriptor import HogDescriptor
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from hu import ShapeDescriptor
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from searcher import Searcher
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import argparse
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import cv2
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# construct the argument parser and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-i", "--index", required=True,
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help="Path to where the computed index will be stored")
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ap.add_argument("-q", "--query", required=True,
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help="Path to the query image")
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ap.add_argument("-r", "--result-path", required=True,
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help="Path to the result path")
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args = vars(ap.parse_args())
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# initialize the image descriptor
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#cd = ShapeDescriptor()
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#cd = ZernikeMoments(50)
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cd = HogDescriptor()
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#cd = ColorDescriptor()
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# load the query image and describe it
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query = cv2.imread(args["query"])
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query = cv2.cvtColor(query, cv2.COLOR_BGR2GRAY)
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# pad the image with extra white pixels to ensure the
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# edges of the pokemon are not up against the borders
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# of the image
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#query = cv2.copyMakeBorder(query, 15, 15, 15, 15,
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# cv2.BORDER_CONSTANT, value = 255)
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# invert the image and threshold it
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#thresh = cv2.bitwise_not(query)
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#thresh[thresh > 0] = 255
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# initialize the outline image, find the outermost
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# contours (the outline) of the pokemone, then draw
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# it
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#outline = np.zeros(query.shape, dtype = "uint8")
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#(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
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# cv2.CHAIN_APPROX_SIMPLE)
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#cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
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#cv2.drawContours(outline, [cnts], -1, 255, -1)
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features = cd.describe_hog(query)
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# perform the search
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searcher = Searcher(args["index"])
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results = searcher.search(features)
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# display the query
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cv2.imshow("Query", query)
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print ("Reached here")
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# loop over the results
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for (score, resultID) in results:
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# load the result image and display it
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print (score)
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result = cv2.imread(args["result_path"] + "/" + resultID)
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cv2.imshow("Result", result)
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cv2.waitKey(0)

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