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File size: 9,690 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "fb70944c",
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'simplejson'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23568/1068728291.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mflask\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFlask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mResponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0msimplejson\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mvisualization_utils\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mvis_util\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mPIL\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'simplejson'"
]
}
],
"source": [
"from flask import Flask, request, Response\n",
"import simplejson\n",
"import tensorflow\n",
"import visualization_utils as vis_util\n",
"from PIL import Image\n",
"import numpy as np\n",
"from PIL import Image\n",
"import numpy as np\n",
"import label_map_util\n",
"import tensorflow as tf\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"import time\n",
"import cv2\n",
"from numpy import asarray\n",
"\n",
"# Creation of the Flask app\n",
"app = Flask(__name__)\n",
"# Flask route for Liveness checks\n",
"\n",
"\n",
"@app.route(\"/isalive\")\n",
"def isalive():\n",
" print(\"/isalive request\")\n",
" status_code = Response(status=200)\n",
" return status_code\n",
"\n",
"\n",
"# Flask route for predictions\n",
"\n",
"\n",
"@app.route('/predict', methods=['GET', 'POST'])\n",
"def prediction():\n",
" total_time_start = time.time()\n",
"\n",
"\n",
" def loadImageIntoNumpyArray(image):\n",
" (im_width, im_height) = image.size\n",
" if image.getdata().mode == \"RGBA\":\n",
" image = image.convert('RGB')\n",
" return asarray(image).reshape((im_height, im_width, 3)).astype(np.uint8)\n",
"\n",
" def main(image_path,model_path,model_PATH_TO_CKPT,path_to_labels):\n",
" image = Image.open(image_path)\n",
" image_np = loadImageIntoNumpyArray(image)\n",
" image_np_expanded = np.expand_dims(image_np, axis=0)\n",
" label_map = label_map_util.load_labelmap(path_to_labels)\n",
" # print(\"label_map------->\",type(label_map))\n",
" categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=100, use_display_name=True)\n",
" category_index = label_map_util.create_category_index(categories)\n",
" # print(\"category index-->\",category_index)\n",
"\n",
" detection_graph = tf.Graph()\n",
" with detection_graph.as_default():\n",
" od_graph_def = tf.compat.v1.GraphDef()\n",
" with tf.compat.v2.io.gfile.GFile(model_PATH_TO_CKPT, 'rb') as fid:\n",
" serialized_graph = fid.read()\n",
" od_graph_def.ParseFromString(serialized_graph)\n",
" tf.import_graph_def(od_graph_def, name='')\n",
" sess = tf.compat.v1.Session(graph=detection_graph)\n",
" # Input tensor is the image\n",
" image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
" # Output tensors are the detection boxes, scores, and classes\n",
" # Each box represents a part of the image where a particular object was detected\n",
" detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
" # Each score represents level of confidence for each of the objects.\n",
" # The score is shown on the result image, together with the class label.\n",
" detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
" detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
" # Number of objects detected\n",
" num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
" (boxes, scores, classes, num) = sess.run(\n",
" [detection_boxes, detection_scores, detection_classes, num_detections],\n",
" feed_dict={image_tensor: image_np_expanded})\n",
" vis_util.visualize_boxes_and_labels_on_image_array(\n",
" image_np,\n",
" np.squeeze(boxes),\n",
" np.squeeze(classes).astype(np.int32),\n",
" np.squeeze(scores),\n",
" category_index,\n",
" use_normalized_coordinates=True,\n",
" line_thickness=8,\n",
" min_score_thresh=0.1)\n",
" %matplotlib inline\n",
" from matplotlib import pyplot as plt\n",
" # print(\"boxes:\",boxes)\n",
" # print(\"class:\",classes)\n",
" objects = []\n",
" threshold = 0.5\n",
" # print(\"category:\",category_index)\n",
" boxes = boxes[0]\n",
" for index, value in enumerate(classes[0]):\n",
" object_dict = {}\n",
" if scores[0, index] > threshold:\n",
" object_dict[\"class\"] = (category_index.get(value)).get('name')\n",
" object_dict[\"score\"] = round(scores[0, index] * 100,2)\n",
" box = tuple(boxes[index].tolist())\n",
" ymin, xmin, ymax, xmax= box\n",
" im_width,im_height = 360,360\n",
" left, right, top, bottom = (xmin * im_width, xmax * im_width, \n",
" ymin * im_height, ymax * im_height)\n",
" object_dict[\"box\"] = (int(left), int(right), int(top), int(bottom))\n",
" objects.append(object_dict)\n",
"\n",
" image_orignal = Image.open(image_path)\n",
" image_np_orignal = loadImageIntoNumpyArray(image_orignal)\n",
"\n",
"\n",
" fig, ax = plt.subplots(1,2)\n",
"\n",
" fig.suptitle('Tag Deciphering')\n",
"\n",
" ax[0].imshow(image_np_orignal,aspect='auto');\n",
" ax[1].imshow(image_np,aspect='auto');\n",
"\n",
"\n",
" return objects\n",
"\n",
" image_path = \"C://Users//thirdeye//Documents//ytag_gcp//test_images//33102340_20221005_1.JPG\"\n",
" model_path = \"C://Users//thirdeye//Documents//ytag_gcp//ytag//yellow-black-28-may-22-inc-30-april-21\"\n",
" model_PATH_TO_CKPT = model_path+\"//inference//frozen_inference_graph.pb\"\n",
" path_to_labels = \"C://Users//thirdeye//Documents//ytag_gcp//ytag//tf_label_map.pbtxt\"\n",
"\n",
" result = main(image_path,model_path,model_PATH_TO_CKPT,path_to_labels)\n",
" # print(\"result-\",result)\n",
" # list_to_be_sorted= [{'class': 'Y', 'score': 99.97, 'box': (157, 191, 269, 288)}, {'class': '6', 'score': 99.93, 'box': (158, 191, 247, 267)}, {'class': '9', 'score': 99.88, 'box': (156, 190, 179, 196)}, {'class': '4', 'score': 99.8, 'box': (156, 189, 198, 219)}, {'class': '1', 'score': 99.65, 'box': (157, 189, 222, 244)}, {'class': 'F', 'score': 63.4, 'box': (155, 185, 157, 175)}]\n",
" newlist = sorted(result, key=lambda k: k['box'][3],reverse=False)\n",
"\n",
" text =''\n",
" for each in newlist:\n",
" if(each['score']>65):\n",
" text += each['class']\n",
" # print(\"text:\",text)\n",
" if(text!=\"\"):\n",
" text = text.replace(\"yellowTag\", \"\") \n",
" result = text\n",
" else:\n",
" result = \"No Vertical Tag Detected\"\n",
" response = {\"predictions\": [result]}\n",
" total_time_end = time.time()\n",
" print(\"total time : \",round((total_time_end-total_time_start),2))\n",
" return simplejson.dumps(response)\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" app.run(debug=True, host='0.0.0.0', port=8087)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7 (tags/v3.9.7:1016ef3, Aug 30 2021, 20:19:38) [MSC v.1929 64 bit (AMD64)]"
},
"vscode": {
"interpreter": {
"hash": "c58a6b68d966fd9b37abe1a881a7bc4a5fe187b07fe812e6c998975c787534e1"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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