{"cells":[{"metadata":{},"cell_type":"markdown","source":"The Wheat is of no use to me. The wheat fields have nothing to say to me. And that is sad. But you have hair that is the color of gold. \n\n#The grain, which is also golden, will bring me back the thought of you. And I shall love to listen to the wind in the wheat...\nAntoine de Saint-Exupéry, The Little Prince"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"#codes from Rodrigo Lima  @rodrigolima82\nfrom IPython.display import Image\nImage(url = 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',width=400,height=400)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"pinterest.com"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-output":true,"collapsed":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\nimport plotly.graph_objs as go\nimport plotly.offline as py\nimport plotly.express as px\nimport imageio\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom PIL import Image, ImageDraw\nfrom ast import literal_eval\nimport cv2\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"#codes from Rodrigo Lima  @rodrigolima82\nfrom IPython.display import Image\nImage(url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQ2QArJDnDSQDbeiASstxs8TIkSZGWoI5K3hh5OXidJ9IImPiL4&usqp=CAU',width=400,height=400)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"facebook.com"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"PATH = '/kaggle/input/global-wheat-detection/'\nfox_df = pd.read_csv('../input/global-wheat-detection/train.csv', encoding='ISO-8859-2')\nfox_df.tail()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fox_images = pd.Series(os.listdir(PATH + '/train/')).sort_values(ascending=True).reset_index(drop=True)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"#codes from Rodrigo Lima  @rodrigolima82\nfrom IPython.display import Image\nImage(url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcTF10jqcjvSd8aRvhvqoAZe7Ouk4N4rdOOHegAUOBZ0pJXpyH07&usqp=CAU',width=400,height=400)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"study.eu"},{"metadata":{},"cell_type":"markdown","source":"#Codes from Kuldeep Dhadhwal  https://www.kaggle.com/kuldeepdhadhwal/global-wheat-detection-eda"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"fig, ax = plt.subplots(nrows=2, ncols=4, figsize=(10, 6))\nk=0\nfor i, row in enumerate(ax):\n    for j, col in enumerate(row):\n        img = mpimg.imread(PATH + '/train/' + fox_images[k])\n        col.imshow(img)\n        col.set_title(fox_images[k])\n        k=k+1\nplt.suptitle('Samples from wheat Images', fontsize=14)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"BBox"},{"metadata":{"trusted":true},"cell_type":"code","source":"display_fox_image = fox_df.loc[fox_df['image_id'] == '5e0747034']","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"#codes from Rodrigo Lima  @rodrigolima82\nfrom IPython.display import Image\nImage(url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcR2R51BcLDXTcDPsMSrlcxDTyiJ7YSQa7H7u9q6-dvoUIuItK_a&usqp=CAU',width=400,height=400)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"inrae.fr"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"display_fox_image.head()      ","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-output":false,"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"#codes from Rodrigo Lima  @rodrigolima82\nfrom IPython.display import Image\nImage(url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcRueMgBHT-80TLvCZVVndwGT-7eJoYwvp3OB3NwFGwpErIFS5qU&usqp=CAU',width=400,height=400)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"albertawheat.com"},{"metadata":{"_kg_hide-output":true,"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"import sqlite3\nfrom tkinter import Tk, Button, Canvas\nfrom PIL import Image, ImageFont\n\nfrom ast import literal_eval\nimage_id = 'train/5e0747034'\nimage_path = os.path.join(PATH + image_id + \".jpg\")\nimage = Image.open(image_path)\n\nboxes = [literal_eval(box) for box in fox_df.loc[fox_df['image_id'] == '5e0747034']['bbox']]\n\nprint(boxes)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"image = Image.open(image_path)\n# visualize them\ndraw = ImageDraw.Draw(image)\nfor box in boxes:    \n    draw.rectangle([box[0], box[1], box[0] + box[2], box[1] + box[3]], width=3)\n\nplt.figure(figsize = (4,4))\nplt.imshow(image)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.countplot(fox_df[\"source\"])\nplt.xticks(rotation=90)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = px.line(fox_df, x=\"source\", y=\"height\", \n              title=\"Wheat Fields and Foxes\")\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"#codes from Rodrigo Lima  @rodrigolima82\nfrom IPython.display import Image\nImage(url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcR_UyVG8ThfElh45vJFWj41t0cAa7vHFRFehSqW1wv_YQBdY725&usqp=CAU',width=400,height=400)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"jesterkingbrewery.com - Fresh Le Petit Prince on Today!"},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"fig = px.histogram(fox_df[fox_df.source.notna()],x=\"source\",marginal=\"box\",nbins=10)\nfig.update_layout(\n    title = \"Wheat Fields and Foxes\",\n    xaxis_title=\"source\",\n    yaxis_title=\"height\",\n    barmode=\"group\",\n    bargap=0.1,\n    xaxis = dict(\n        tickmode = 'linear',\n        tick0 = 0,\n        dtick = 10),\n    font=dict(\n        family=\"Arial, monospace\",\n        size=15,\n        color=\"#7f7f7f\"\n    )\n    )\npy.iplot(fig)","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"from wordcloud import WordCloud, STOPWORDS\nstopwords = set(STOPWORDS)\n\ndef show_wordcloud(data, title = None):\n    wordcloud = WordCloud(\n        background_color='black',\n        stopwords=stopwords,\n        max_words=200,\n        colormap='Set3',\n        max_font_size=40, \n        scale=3,\n        random_state=1 # chosen at random by flipping a coin; it was heads\n).generate(str(data))\n\n    fig = plt.figure(1, figsize=(15, 15))\n    plt.axis('off')\n    if title: \n        fig.suptitle(title, fontsize=20)\n        fig.subplots_adjust(top=2.3)\n\n    plt.imshow(wordcloud)\n    plt.show()\n\nshow_wordcloud(fox_df['source'])","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"fig = px.scatter(fox_df, x=\"width\", y=\"source\", color=\"height\", marginal_y=\"rug\", marginal_x=\"histogram\")\nfig","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"fig = px.density_contour(fox_df, x=\"width\", y=\"source\", color_discrete_sequence=['purple'])\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"#codes from Rodrigo Lima  @rodrigolima82\nfrom IPython.display import Image\nImage(url = 'https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcRvVWWidXA8FgutFxsnJVcM-95dacIFfBdtZ5Ntfh3co5HEc_zw&usqp=CAU',width=400,height=400)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"That's a perfect combination image to BBox: Bread, Beer (my plan A is Wine), wheat? No it's Rye.  "},{"metadata":{},"cell_type":"markdown","source":"#My new version of taming the fox:\n\nThe wheat fields take on new meaning for the fox as their colour reminds her of his friend's golden curls. \n\n'And so the little prince tamed NO one. Since everyone is responsible for their own (except children, ancient or those that can't take care of themselves).\n\n#Computer Vision can show (and hide) what is invisible to the eye.\n\n“Then it has done you no good!”Said the little Prince. “It has done me good,” said the fox, “because of the colour of the wheat fields.(with bread, beer and wheat golden curls).\n\nThe grain, which is also golden, will bring me back the thought of you.\n\n#And I shall love to listen to the wind in the wheat... \n\nAntoine de Saint-Exupéry, The Little Prince\n"},{"metadata":{},"cell_type":"markdown","source":"The Global Wheat Head Dataset is led by nine research institutes from seven countries: the University of Tokyo, Institut national de recherche pour l’agriculture, l’alimentation et l’environnement, Arvalis, ETHZ, University of Saskatchewan, University of Queensland, Nanjing Agricultural University, and Rothamsted Research. These institutions are joined by many in their pursuit of accurate wheat head detection, including the Global Institute for Food Security, DigitAg, Kubota, and Hiphen."},{"metadata":{},"cell_type":"markdown","source":"Kaggle Notebook Runner: Marília Prata  @mpwolke"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}