{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"\n# 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 in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../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\n'''\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n'''\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator\nfrom keras.applications.inception_v3 import preprocess_input\nfrom keras.utils.data_utils import GeneratorEnqueuer\nimport matplotlib.pyplot as plt\nimport math, os","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_path = '/kaggle/input/open-images-2019-object-detection/'\n\nbatch_size = 100\nimg_generator = ImageDataGenerator().flow_from_directory(image_path, shuffle=False, target_size = (416,416), batch_size = batch_size)\nn_rounds = math.ceil(img_generator.samples / img_generator.batch_size)\nfilenames = img_generator.filenames\n\nimg_generator = GeneratorEnqueuer(img_generator)\nimg_generator.start()\nimg_generator = img_generator.get()\nprint(img_generator)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class_descriptions_boxable = pd.read_csv('/kaggle/input/open-images-classes/class-descriptions-boxable.csv',header=None)\nrev = class_descriptions_boxable.set_index(1).T.to_dict('list')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!cp -r ../input/imageaireport/imageai/imageai imageai","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from imageai.Detection import ObjectDetection\nmodel_weight_path = \"../input/modelyolov3/model_yolov3/model_yolov3.h5\"\n\nexecution_path = os.getcwd()\ndetector = ObjectDetection()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"detector.setModelTypeAsYOLOv3()\ndetector.setModelPath(model_weight_path)\ndetector.loadModel()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(n_rounds):\n    batch = next(img_generator)\n    for j, prediction in enumerate(batch):\n        image = filenames[i * batch_size + j]\n        detections = detector.detectObjectsFromImage(input_image=image_path+image, output_image_path=\"image_with_box.png\", minimum_percentage_probability = 75)        \n        pred_str = \"\"\n        labels = \"\"\n        for eachObject in detections:\n            if eachObject[\"name\"].capitalize() in rev:\n                pred_str += rev[eachObject[\"name\"].capitalize()][0] + \" \" + str(float(eachObject[\"percentage_probability\"])/100) + \" 0.1 0.1 0.9 0.9\"\n                pred_str += \" \"\n                labels += eachObject['name'] + \", \" + str(round(float(eachObject['percentage_probability'])/100, 1)) \n                labels += \" | \"\n        if labels != \"\":\n            plt.figure(figsize=(12,12))\n            plt.imshow(plt.imread(\"image_with_box.png\"))\n            plt.show()\n\n            print (\"Labels Detected: \")\n            print (labels)\n            print ()\n            print (\"Prediction String: \")\n            print (pred_str)\n\n    if i == 10:\n        break","execution_count":null,"outputs":[]}],"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":1}