{"action":"created","comment":{"url":"https://api.github.com/repos/parseq/digital-phenotyping/pulls/comments/68371251","id":68371251,"diff_hunk":"@@ -0,0 +1,237 @@\n+#!/usr/bin/env python3\n+# -*- coding: utf-8 -*-\n+\n+# import the necessary packages\n+import numpy as np\n+import cv2\n+import math\n+import argparse\n+from matplotlib import pyplot as plt\n+import matplotlib.mlab as mlab\n+from scipy.signal import argrelextrema\n+from scipy.stats import norm\n+import sys\n+\n+# Contours selection facilitating function enhanced with the threshold\n+def contours_selection_threshold(image):\n+    image = cv2.GaussianBlur(image, (5, 5), 0)  # blur is added to denoise the image\n+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n+    ret2, th2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n+    tresh = np.copy(th2)\n+    im, cnts, hier = cv2.findContours(th2, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)\n+\n+    return cnts,tresh\n+\n+def square_selection(contours, image):\n+    areas = []\n+    for c in contours:\n+        areas.append(cv2.contourArea(c))\n+\n+    c_areas = np.asarray(areas)\n+    top = c_areas.argsort()[-2:][::-1]\n+    a = int(image.shape[0])\n+    b = int(image.shape[1])\n+    c = cv2.contourArea(contours[top[0]])\n+\n+    if c >= 0.97*a*b:\n+        print('The biggest contour is the picture border, the second biggest contour was selected.')\n+        top_regime = True\n+    else:\n+        print('The biggest contour is not defined properly. The result might be unreliable.')\n+        top_regime = False\n+\n+    #selection of second large contour on the picture that is the square\n+    return top, top_regime\n+\n+'''def shrink_the_mask(square_contour, image):\n+    perimeter = cv2.arcLength(square_contour, True)  # finds closed contour\n+    if perimeter == 0:\n+        print('The square is not recognized.')\n+    epsilon = 0.1 * perimeter\n+    approx = cv2.approxPolyDP(square_contour, epsilon, True)\n+\n+    if len(approx) > 4:\n+        print('The square is not recognized.')\n+\n+    component = np.zeros_like(image)\n+    x, y, z, h = approx\n+    x = x[0]\n+    z = z[0]\n+    cv2.rectangle(component, (x[0], x[1]), (z[0], z[1]), (255, 255, 255), -1)\n+\n+    kernel = np.ones((5, 5), np.uint8)\n+    component = cv2.erode(component, kernel, iterations=6)\n+    component = cv2.cvtColor(component,cv2.COLOR_BGR2GRAY)\n+    im, cnts, hier = cv2.findContours(component, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n+    if len(cnts) == 1:\n+        print('The square is shrinked properly.')\n+\n+    perimeter = cv2.arcLength(cnts[0], True)  # finds closed contour\n+    if perimeter == 0:\n+        print('The square is not recognized.')\n+    epsilon = 0.1 * perimeter\n+    approx = cv2.approxPolyDP(cnts[0], epsilon, True)\n+\n+    if len(approx) > 4:\n+        print('The square is not recognized.')\n+\n+    x, y, z, h = approx\n+    x = tuple(x[0])\n+    y = tuple(y[0])\n+    z = tuple(z[0])\n+    h = tuple(h[0])\n+\n+    print('There are new coordinates of corners lying inside the contour of the square.')\n+\n+    return x,y,z,h, component'''\n+\n+def shrink_the_mask(square_contour, image):\n+    perimeter = cv2.arcLength(square_contour, True)  # finds closed contour\n+    if perimeter == 0:\n+        print('The square is not recognized.')\n+    epsilon = 0.1 * perimeter\n+    approx = cv2.approxPolyDP(square_contour, epsilon, True)\n+\n+    if len(approx) > 4:\n+        print('The square is not recognized.')\n+\n+\n+    component = np.zeros_like(image)\n+    mask = np.zeros_like(image)\n+\n+    if len(approx) == 2:\n+        x, z = approx\n+        x = x[0]\n+        z = z[0]\n+        cv2.rectangle(component, (x[0], x[1]), (z[0], z[1]), (255, 255, 255), -1)\n+        component = cv2.cvtColor(component, cv2.COLOR_BGR2GRAY)\n+        cv2.drawContours(mask, square_contour, -1, (255, 255, 255), -1)\n+        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\n+        for x in np.ndenumerate(component):\n+            print(component[x,y])\n+            if component[x,y] == mask[x,y]:\n+                print('Your square fits perfectly.')\n+            else:\n+                print('The square is rotated or distorted.')\n+\n+    else:\n+        x,z,y,h = approx\n+        x = tuple(x[0])\n+        y = tuple(y[0])\n+        z = tuple(z[0])\n+        h = tuple(h[0])\n+\n+        cv2.rectangle(component, (x[0], x[1]), (z[0], z[1]), (255, 255, 255), -1)\n+        cv2.drawContours(mask, square_contour, -1, (255, 255, 255), -1)\n+        component = cv2.cvtColor(component, cv2.COLOR_BGR2GRAY)\n+        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\n+\n+        print('There are new coordinates of corners lying inside the contour of the square.')\n+\n+        for (x,y) in np.ndenumerate(component):\n+            print(component[x,y])\n+            if component[x,y] == mask[x,y]:\n+                print('Your square fits perfectly.')\n+            else:\n+                print('The square is rotated or distorted.')\n+\n+\n+    return x, z, mask, component\n+\n+\n+# Angle-checking function\n+def is_on_line(x1, y1, x2, y2, x3, y3):\n+    # integer class variables expected","path":"image-processing/recoshape.py","position":144,"original_position":144,"commit_id":"5772bd384e32c057af7c0679961404ad79928d81","original_commit_id":"5772bd384e32c057af7c0679961404ad79928d81","user":{"login":"npilshchikova","id":5954158,"avatar_url":"https://avatars.githubusercontent.com/u/5954158?v=3","gravatar_id":"","url":"https://api.github.com/users/npilshchikova","html_url":"https://github.com/npilshchikova","followers_url":"https://api.github.com/users/npilshchikova/followers","following_url":"https://api.github.com/users/npilshchikova/following{/other_user}","gists_url":"https://api.github.com/users/npilshchikova/gists{/gist_id}","starred_url":"https://api.github.com/users/npilshchikova/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/npilshchikova/subscriptions","organizations_url":"https://api.github.com/users/npilshchikova/orgs","repos_url":"https://api.github.com/users/npilshchikova/repos","events_url":"https://api.github.com/users/npilshchikova/events{/privacy}","received_events_url":"https://api.github.com/users/npilshchikova/received_events","type":"User","site_admin":false},"body":"Для описания параметров функции существуют стандартные форматы, в python могут использоваться ([разные варианты](http://daouzli.com/blog/docstring.html)), но рекомендуемым [PEP287 ](https://www.python.org/dev/peps/pep-0287/) является [reStructuredText](http://thomas-cokelaer.info/tutorials/sphinx/docstring_python.html) (reST).\r\nВ PyCharm он используется по умолчанию, при этом, если указать типы переменных, интерпретатор начнёт их проверять и подсвечивать, если где-то они некорректны.","created_at":"2016-06-24T09:01:42Z","updated_at":"2016-06-24T09:01:42Z","html_url":"https://github.com/parseq/digital-phenotyping/pull/6#discussion_r68371251","pull_request_url":"https://api.github.com/repos/parseq/digital-phenotyping/pulls/6","_links":{"self":{"href":"https://api.github.com/repos/parseq/digital-phenotyping/pulls/comments/68371251"},"html":{"href":"https://github.com/parseq/digital-phenotyping/pull/6#discussion_r68371251"},"pull_request":{"href":"https://api.github.com/repos/parseq/digital-phenotyping/pulls/6"}}},"pull_request":{"url":"https://api.github.com/repos/parseq/digital-phenotyping/pulls/6","id":74398326,"html_url":"https://github.com/parseq/digital-phenotyping/pull/6","diff_url":"https://github.com/parseq/digital-phenotyping/pull/6.diff","patch_url":"https://github.com/parseq/digital-phenotyping/pull/6.patch","issue_url":"https://api.github.com/repos/parseq/digital-phenotyping/issues/6","number":6,"state":"open","locked":false,"title":"Image processing/issue 3","user":{"login":"miryad","id":13433463,"avatar_url":"https://avatars.githubusercontent.com/u/13433463?v=3","gravatar_id":"","url":"https://api.github.com/users/miryad","html_url":"https://github.com/miryad","followers_url":"https://api.github.com/users/miryad/followers","following_url":"https://api.github.com/users/miryad/following{/other_user}","gists_url":"https://api.github.com/users/miryad/gists{/gist_id}","starred_url":"https://api.github.com/users/miryad/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/miryad/subscriptions","organizations_url":"https://api.github.com/users/miryad/orgs","repos_url":"https://api.github.com/users/miryad/repos","events_url":"https://api.github.com/users/miryad/events{/privacy}","received_events_url":"https://api.github.com/users/miryad/received_events","type":"User","site_admin":false},"body":"Recognition algorithm code was added.\r\nUnnecessary files (photos, palette versions) were removed.","created_at":"2016-06-20T07:13:53Z","updated_at":"2016-06-24T09:01:42Z","closed_at":null,"merged_at":null,"merge_commit_sha":"ab9285a87145e1494f6cbe561dc447482fcfb229","assignee":{"login":"agbragin","id":2486050,"avatar_url":"https://avatars.githubusercontent.com/u/2486050?v=3","gravatar_id":"","url":"https://api.github.com/users/agbragin","html_url":"https://github.com/agbragin","followers_url":"https://api.github.com/users/agbragin/followers","following_url":"https://api.github.com/users/agbragin/following{/other_user}","gists_url":"https://api.github.com/users/agbragin/gists{/gist_id}","starred_url":"https://api.github.com/users/agbragin/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/agbragin/subscriptions","organizations_url":"https://api.github.com/users/agbragin/orgs","repos_url":"https://api.github.com/users/agbragin/repos","events_url":"https://api.github.com/users/agbragin/events{/privacy}","received_events_url":"https://api.github.com/users/agbragin/received_events","type":"User","site_admin":false},"assignees":[{"login":"agbragin","id":2486050,"avatar_url":"https://avatars.githubusercontent.com/u/2486050?v=3","gravatar_id":"","url":"https://api.github.com/users/agbragin","html_url":"https://github.com/agbragin","followers_url":"https://api.github.com/users/agbragin/followers","following_url":"https://api.github.com/users/agbragin/following{/other_user}","gists_url":"https://api.github.com/users/agbragin/gists{/gist_id}","starred_url":"https://api.github.com/users/agbragin/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/agbragin/subscriptions","organizations_url":"https://api.github.com/users/agbragin/orgs","repos_url":"https://api.github.com/users/agbragin/repos","events_url":"https://api.github.com/users/agbragin/events{/privacy}","received_events_url":"https://api.github.com/users/agbragin/received_events","type":"User","site_admin":false},{"login":"npilshchikova","id":5954158,"avatar_url":"https://avatars.githubusercontent.com/u/5954158?v=3","gravatar_id":"","url":"https://api.github.com/users/npilshchikova","html_url":"https://github.com/npilshchikova","followers_url":"https://api.github.com/users/npilshchikova/followers","following_url":"https://api.github.com/users/npilshchikova/following{/other_user}","gists_url":"https://api.github.com/users/npilshchikova/gists{/gist_id}","starred_url":"https://api.github.com/users/npilshchikova/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/npilshchikova/subscriptions","organizations_url":"https://api.github.com/users/npilshchikova/orgs","repos_url":"https://api.github.com/users/npilshchikova/repos","events_url":"https://api.github.com/users/npilshchikova/events{/privacy}","received_events_url":"https://api.github.com/users/npilshchikova/received_events","type":"User","site_admin":false}],"milestone":null,"commits_url":"https://api.github.com/repos/parseq/digital-phenotyping/pulls/6/commits","review_comments_url":"https://api.github.com/repos/parseq/digital-phenotyping/pulls/6/comments","review_comment_url":"https://api.github.com/repos/parseq/digital-phenotyping/pulls/comments{/number}","comments_url":"https://api.github.com/repos/parseq/digital-phenotyping/issues/6/comments","statuses_url":"https://api.github.com/repos/parseq/digital-phenotyping/statuses/5772bd384e32c057af7c0679961404ad79928d81","head":{"label":"parseq:image-processing/issue-3","ref":"image-processing/issue-3","sha":"5772bd384e32c057af7c0679961404ad79928d81","user":{"login":"parseq","id":7878537,"avatar_url":"https://avatars.githubusercontent.com/u/7878537?v=3","gravatar_id":"","url":"https://api.github.com/users/parseq","html_url":"https://github.com/parseq","followers_url":"https://api.github.com/users/parseq/followers","following_url":"https://api.github.com/users/parseq/following{/other_user}","gists_url":"https://api.github.com/users/parseq/gists{/gist_id}","starred_url":"https://api.github.com/users/parseq/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/parseq/subscriptions","organizations_url":"https://api.github.com/users/parseq/orgs","repos_url":"https://api.github.com/users/parseq/repos","events_url":"https://api.github.com/users/parseq/events{/privacy}","received_events_url":"https://api.github.com/users/parseq/received_events","type":"Organization","site_admin":false},"repo":{"id":59024369,"name":"digital-phenotyping","full_name":"parseq/digital-phenotyping","owner":{"login":"parseq","id":7878537,"avatar_url":"https://avatars.githubusercontent.com/u/7878537?v=3","gravatar_id":"","url":"https://api.github.com/users/parseq","html_url":"https://github.com/parseq","followers_url":"https://api.github.com/users/parseq/followers","following_url":"https://api.github.com/users/parseq/following{/other_user}","gists_url":"https://api.github.com/users/parseq/gists{/gist_id}","starred_url":"https://api.github.com/users/parseq/starred{/owner}{/repo}","subscriptions_url":"ht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