Merge pull request #1 from gkswjdzz/dev

create word cloud

function.py

@@ -0,0 +1,109 @@
+from os import path
+from PIL import Image
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from wordcloud import WordCloud, STOPWORDS
+
+import os
+import requests
+
+detectron2_url = 'http://localhost/predictions'
+stanfordnlp_url = 'http://localhost:81/analyze'
+
+mpl.use('agg')
+
+def postStanfordnlp(filepath, lang) :
+    """
+        input : txt file
+        lang : ko => model_version : ko_kaist
+    """
+
+    files = { 
+        'file' : open(filepath, 'rb')
+    }
+    data = {
+        'model_version' : lang
+    }
+    predictions = requests.post(stanfordnlp_url, data= data, files=files)
+
+    text = []
+
+    ret = predictions.json()
+
+    for key in ret :
+        for word in ret[key] :
+            if word['upos'] == 'NOUN':
+                text.append(word['lemma'])
+
+    txt = ' '.join(text)
+
+    print(txt)
+    print('end post stanfordnlp')
+
+    return txt
+
+def generateWordCloud(mask_path, text):
+    # get data directory (using getcwd() is needed to support running example in generated IPython notebook)
+    d = path.dirname(__file__) if "__file__" in locals() else os.getcwd()
+
+    # Read the whole text.
+    #text = open(path.join(d, txt_path)).read()
+
+    # read the mask image
+    # taken from
+    # http://www.stencilry.org/stencils/movies/alice%20in%20wonderland/255fk.jpg
+    alice_mask = np.array(Image.open(path.join(d, mask_path)))
+
+    stopwords = set(STOPWORDS)
+    stopwords.add("said")
+
+    wc = WordCloud(background_color="white", max_words=2000, mask=alice_mask,
+                stopwords=stopwords, contour_width=3, contour_color='steelblue')
+
+    # generate word cloud
+    wc.generate(text)
+
+    # store to file
+    wc.to_file(path.join(d, "word.png"))
+
+    return "word.png"
+def generate_mask(jpg_path):
+    print('in function generate_mask')
+    predictions = postDetectron2(jpg_path)
+    polygons = getPolygons(predictions, None)
+
+    img = Image.open(jpg_path)
+    img_np = np.array(img)
+
+    print(img_np.shape)
+    height, width, _ = img_np.shape
+
+    img_np = np.array([255] * (height * width * 3), dtype=np.uint8)
+    img_np = img_np.reshape(height, width, 3)
+
+    xy = polygons[0]['0']
+    print(xy)
+    fig = plt.figure()
+    ax = plt.subplot()
+    ax.imshow(img_np)
+    ax.axis('off')
+    ax.add_patch(mpl.patches.Polygon(xy, fill=True, facecolor='k', edgecolor='none', alpha=1.0))
+
+    fig.savefig('mask.png')
+
+    return 'mask.png'
+
+def postDetectron2(filepath) :
+    files = { 'file' : open(filepath, 'rb')}
+    predictions = requests.post(detectron2_url, files=files)
+
+    return predictions.json()
+
+def getPolygons(predictions, className) :
+    polygons = []
+
+    for idx in predictions.keys() :
+        polygons.append(predictions[idx]['polygons'])
+
+    return polygons