2020-02-25 22:18:56 -03:00
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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2020-02-26 16:05:21 -03:00
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"# Example of load model for detections\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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2020-06-15 18:48:44 -04:00
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"execution_count": 1,
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2020-02-26 16:05:21 -03:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n",
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"import os\n",
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"import argparse\n",
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"import json\n",
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"import cv2\n",
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"import sys\n",
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2020-08-26 00:39:38 -04:00
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"sys.path += [os.path.abspath('../keras-yolo3-master')]\n",
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2020-02-26 16:05:21 -03:00
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"\n",
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"from utils.utils import get_yolo_boxes, makedirs\n",
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"from utils.bbox import draw_boxes\n",
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2020-04-16 14:11:33 -04:00
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"from tensorflow.keras.models import load_model\n",
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2020-02-26 16:05:21 -03:00
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"from tqdm import tqdm\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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2020-04-16 18:29:03 -04:00
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"from panel_disconnect import disconnect\n",
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2020-02-26 16:05:21 -03:00
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Trained Model Soiling Fault"
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]
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},
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{
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"cell_type": "code",
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2020-06-15 18:48:44 -04:00
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"execution_count": 2,
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2020-02-26 16:05:21 -03:00
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"metadata": {},
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"outputs": [
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{
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2020-06-15 18:48:44 -04:00
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"name": "stdout",
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2020-02-26 16:05:21 -03:00
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"output_type": "stream",
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"text": [
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2020-06-15 18:48:44 -04:00
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"WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
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2020-02-26 16:05:21 -03:00
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]
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}
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],
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"source": [
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"## Config of trained model, change this for use different trained model\n",
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"config_path = 'config_full_yolo_fault_1_infer.json' \n",
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"\n",
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"with open(config_path) as config_buffer:\n",
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" config = json.load(config_buffer)\n",
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" \n",
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"\n",
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"###############################\n",
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"##### Load the model ######\n",
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"###############################\n",
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"os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']\n",
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"infer_model = load_model(config['train']['saved_weights_name'])\n",
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"\n",
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"## Parameters of detection\n",
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"net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster\n",
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"obj_thresh, nms_thresh = 0.5, 0.45\n",
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"\n",
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"\n",
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"#infer_model.summary()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Detection Soling Fault"
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]
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},
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{
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"cell_type": "code",
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2020-06-15 18:48:44 -04:00
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"execution_count": 3,
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2020-02-26 16:05:21 -03:00
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2020-06-15 18:48:44 -04:00
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"Elapsed time = 4.298295259475708\n"
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2020-02-26 16:05:21 -03:00
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]
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},
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{
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"data": {
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"text/plain": [
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"(-0.5, 639.5, 511.5, -0.5)"
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]
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},
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2020-06-15 18:48:44 -04:00
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"execution_count": 3,
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2020-02-26 16:05:21 -03:00
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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2020-06-15 18:48:44 -04:00
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"image/png": "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2020-02-26 16:05:21 -03:00
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"text/plain": [
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"<Figure size 1152x1152 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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2020-06-15 18:48:44 -04:00
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"image/png": "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2020-02-26 16:05:21 -03:00
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"text/plain": [
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"<Figure size 1152x1152 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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2020-08-26 00:39:38 -04:00
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"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
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"\n",
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"\n",
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"image = cv2.imread(image_path)\n",
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"\n",
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"## Show original image\n",
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"fig, ax = plt.subplots(figsize=(16, 16))\n",
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"ax.set_title('Original Image')\n",
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"plt.imshow(image, cmap='gray')\n",
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"ax.axis('off') \n",
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"\n",
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"start = time.time()\n",
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"## predict the bounding boxes\n",
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"boxes = get_yolo_boxes(infer_model, [image], net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
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"print('Elapsed time = {}'.format(time.time() - start))\n",
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"## draw bounding boxes on the image using labels\n",
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"draw_boxes(image, boxes, config['model']['labels'], obj_thresh)\n",
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"\n",
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"\n",
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"## Show Detection Fault\n",
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"fig, ax = plt.subplots(figsize=(16, 16))\n",
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2020-06-15 18:48:44 -04:00
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"ax.set_title('Detection Soling Fault')\n",
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2020-02-26 16:05:21 -03:00
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"plt.imshow(image, cmap='gray')\n",
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"ax.axis('off') \n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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|
|
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"## Load Trained Model Diode Fault"
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]
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},
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{
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"cell_type": "code",
|
2020-06-15 18:48:44 -04:00
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"execution_count": 4,
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2020-02-26 16:05:21 -03:00
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"metadata": {},
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"outputs": [
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{
|
2020-06-15 18:48:44 -04:00
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"name": "stdout",
|
2020-02-26 16:05:21 -03:00
|
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"output_type": "stream",
|
|
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|
"text": [
|
2020-06-15 18:48:44 -04:00
|
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|
"WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
|
2020-02-26 16:05:21 -03:00
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]
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}
|
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],
|
|
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"source": [
|
|
|
|
|
"## Config of trained model, change this for use different trained model\n",
|
|
|
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|
"config_path = 'config_full_yolo_fault_4_infer.json' \n",
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"\n",
|
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|
|
|
"with open(config_path) as config_buffer:\n",
|
|
|
|
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" config = json.load(config_buffer)\n",
|
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" \n",
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"\n",
|
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"###############################\n",
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|
|
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"##### Load the model ######\n",
|
|
|
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|
"###############################\n",
|
|
|
|
|
"os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']\n",
|
|
|
|
|
"infer_model = load_model(config['train']['saved_weights_name'])\n",
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|
"\n",
|
|
|
|
|
"#infer_model.summary()\n",
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"\n",
|
|
|
|
|
"## Parameters of detection\n",
|
|
|
|
|
"net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster\n",
|
|
|
|
|
"obj_thresh, nms_thresh = 0.5, 0.45"
|
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|
|
]
|
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|
},
|
|
|
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|
{
|
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|
"cell_type": "markdown",
|
|
|
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|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"## Detection Diode Fault"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
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|
"cell_type": "code",
|
2020-06-15 18:48:44 -04:00
|
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|
"execution_count": 5,
|
2020-02-26 16:05:21 -03:00
|
|
|
"metadata": {},
|
|
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|
"outputs": [
|
|
|
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|
{
|
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|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
2020-06-15 18:48:44 -04:00
|
|
|
"Elapsed time = 2.4191977977752686\n"
|
2020-02-26 16:05:21 -03:00
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]
|
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|
|
},
|
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{
|
|
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"data": {
|
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"text/plain": [
|
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|
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"(-0.5, 639.5, 511.5, -0.5)"
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]
|
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},
|
2020-06-15 18:48:44 -04:00
|
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|
"execution_count": 5,
|
2020-02-26 16:05:21 -03:00
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"metadata": {},
|
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"output_type": "execute_result"
|
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},
|
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{
|
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"data": {
|
2020-06-15 18:48:44 -04:00
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAA4sAAALoCAYAAADRBGAjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9a6i025bfNZ5aq2qtWu9l78M5NsYIHdR4iXgBQUxQFOI10CSIETGiQYSgkg/SKnSLwf5gxBAaohKV+M0WIzaJoKISREWxI+iHoE2MKLYaPDGc03vv865LXVbV44f1/p/1e/41xqx6t+f06fbMAYtVVc+8jDnmnOM+5zOM4xgdOnTo0KFDhw4dOnTo0KEDYfHDRqBDhw4dOnTo0KFDhw4dOvzqg24sdujQoUOHDh06dOjQoUOHE+jGYocOHTp06NChQ4cOHTp0OIFuLHbo0KFDhw4dOnTo0KFDhxPoxmKHDh06dOjQoUOHDh06dDiBbix26NChQ4cOHTp06NChQ4cT6MZihw4dOnT4VQnDMPz0MAz/1ve77AVtjcMw/GXfj7Y6dOjQoUOHX8sw9PcsdujQoUOHHzQMw/C7I+InI+IvjYjvRcQfj4ifGsfxyx8mXhkMwzBGxG8cx/F/TZ79lxHxc+M4fl8M0w4dOnTo0OFXM/TIYocOHTp0+IHCMAw/GRH/ckT8MxHxWUT8TRHx4xHxJ4ZhWBV1rn/lMOzQoUOHDh06ZNCNxQ4dOnTo8AODYRjeR8TPRMTvHcfxPx3HcT+O4y9FxN8fLwbjP/Sx3L8wDMPPD8Pwc8MwfC8ifvfH334Obf3DwzD8H8MwfHcYhn9+GIZfGobhb0f9n/v4+Td8TCX9R4Zh+D+HYfjOMAz/HNr5G4dh+IVhGL4chuHbwzD8a5XRemZsf9swDH92GIZ/dhiGP/+xrd8xDMNvG4bhfxmG4ZeHYfjpS/sdhuHvHIbhzwzD8NUwDH94GIb/ahiGfwzP/9FhGP70MAxfDMPwnw3D8OOfinOHDh06dOjwKdCNxQ4dOnTo8IOE3xIRtxHxx/jjOI73EfGfRMTfgZ9/e0T8fER8HhH/DssPw/CbIuIPR8TviohfFy8Ryl9/pu+/OSL+ioj4rRHx+4Zh+Ks+/n6IiH8qIr4VEb/54/N/4hPHJfgL42V8vz4ifl9E/JF4MYD/hoj4Wz72+5ec63cYhm/Fy9h/KiK+GRF/Jl5oFx+f/46I+OmI+Hsj4i+IiP86Iv7dr4lzhw4dOnTocBF0Y7FDhw4dOvwg4VsR8Z1xHJ+TZ9/++FzwC+M4/gfjOB7HcXyysn9fRPyH4zj+N+M47uLFMDt36P5nxnF8GsfxT0XEn4qIvy4iYhzH/2Ecxz85juPzxyjnvxkRf+unDy0iIvYR8S+O47iPiD/6cTx/aBzHD+M4/mJE/GJE/LUX9PvbIuIXx3H8Yx9p9a9ExJ9DP78nIv6lcRz/9Mfnvz8i/voeXezQoUOHDj9I6MZihw4dOnT4QcJ3IuJbxRnEX/fxueD/arTzF/H5OI6PEfHdM33T2HqMiLcREcMw/OXDMPxHwzD8uY8pr78/5kbrp8B3x3E8fPwsA/f/wfOnC/v18Y0R8WfRzo9HxB/6mML6ZUT8ckQMcT662qFDhw4dOnxt6MZihw4dOnT4QcIvRMQ2XtInJxiG4U1E/D0R8Z/j51ak8NsR8Rej/jpe0jW/DvzrEfE/x8uNp+/jJb1z+Jptfb/69fEN/B4vhuTvGcfxc/ytx3H8b38F8O7QoUOHDj+i0I3FDh06dOjwA4NxHL+Klwtu/tVhGP7uYRiWwzD8hoj49+MlcvZvX9jUz0fETwzD8Fs+XgrzM/H1Dbx38fL6jvthGP7KiPjHv2Y7389+/+OI+Gs+XpBzHRH/ZLychxT8GxHxU8Mw/NUREcMwfDYMw+/8FcK7Q4cOHTr8iEI3Fjt06NChww8UxnH8A/ESRfuD8WIs/XfxEin7reM4bi9s4xcj4vfGy7nAb0fEh4j48/EStfxU+Kcj4h/82MYfiYh/72u08XWg7Hccx+9ExO+MiD8QL+m1vyki/vv4OL5xHP94vLx+5I9+TGH9n+IlMtuhQ4cOHTr8wGB4ORbRoUOHDh06/NqBYRjeRsSX8ZLS+b//sPH5fsMwDIt4ibz+rnEc/4sfNj4dOnTo0OFHE3pksUOHDh06/JqAYRh+YhiGu4/nHf9gRPyPEfFLP1ysvn8wDMPfNQzD58Mw3MTrecY/+UNGq0OHDh06/AhDNxY7dOjQocOvFfjtEfF/f/z7jRHxD4z//0qP+c0R8b/Fyw2xPxERvyN5hUiHDh06dOjwKwY9DbVDhw4dOnTo0KFDhw4dOpxAjyx26NChQ4cOHTp06NChQ4cT6MZihw4dOnTo0KFDhw4dOnQ4gevWw5/8yZ8cX94LHKH/ERGHwyH2+30cDoeIiBjHMZbL5UuD19dxdXU1K6/P4zgG016Px+P0eRzHGIYhFovF7Lu3wc9sl8B+hmE4qct659pinayMj8nxYN8cs8aq8V5dXUVExH6/j4iIzWYzfWadiJjVY1/V+IiDyjneh8Nh9p11jsdj+cxxGMdxwreipZcnrlm9m5ubePv27fR5HMf44osvIiLiw4cPsdvtJrrw//X19QmdCCzPcldXVzO8qrL6rDKkk+ZX87pYLGZzcTwep/0zDENcXV1N7Q7DENfXr1vzcDjE8/PzDD/9555ZLBZTuV/+5V+O7373u/H4+Dg9Ew3v7u5itVpNfWjPqu/D4RAPDw8R8boeSU/h7etiu93OvmvMAj67urqK1Wo1tavPx+Mx9vv9tIa22+2Eg/AU3svlcsL7+vp6xk/UP/8TD5ZdLBbTd/6uz6S3aKR6/hvB8eF3rnX278D55boUnTS/+h/xSpfb29sJT/K5c/zK92HGvyteWLXr37N64zjO1rlwr9ohDtfX13FzcxMRL3RSO5vNJjabzYw+PldcQ6RPNV+XjK+SXVn91rqo6FTJHV/bXD83Nzdxd3cXb968mfq9v7+Pr776KiIinp+fp731/Pwcj4+PE29dLpcnsrmSA1xvGgfHlukUzm/Plb9ExmTy18t5vywvWtzd3cXd3d20vp6enuL+/j4iIh4eHmb8Kuuf/OIcPpeUI74Rp3zJy1W6k5ev9n2mQxFEl7dv38bt7e1Ei/v7+/jw4UNEvMiw7XY77Uvu85ubmxMZ1KIF+TrL6nOmU/ga9PFRlnmfzhMl/1TWdQTV8f0rHVn/1c5+v5/oIZ4u+Xs8Hie63NzcxGq1muqLbuxDslJtav8+Pz9Pz56fn09kN3Wm1Wo1zelyuSz1zozvOW35e7Y+pXeyTLWepSNlc+H7mDZI1m/rGcv4eKu9nclVt2+8DX2v5KbbHWynpde2ZFWGD+Fnf/ZnS4L0yGKHDh06dOjQoUOHDh06dDiBZmTxeDyWFji9JvQe6rug5cn2djOLO7OSM2v7Eg+3g9erPG1Zu5WXm+P1yJN7uuTRinjxsNCTsVqtSi8zPTzqL/PeqL3MM5F5lqqo0PF4nHly5fnxMQkPenuy6DHLst3K28EIkjxGiprQW+ljyuaXfTBiy7KM4LCu/leeTMfdy/oe4dg5p+P4GqnP6OieVXnA6XW8vb2Nu7u7mSeXXsfD4TDzJjICGhGzqKfGdDgcTtYCcWSEzPcvxy/vofC+ubmZ8N5sNrHb7WZrSOXcs8j5zmjk6ynDWzhnkUWOTePnOLjWM97BPdnyzld7JBsX+cdqtZqNiZHQzMvN+hUurYgDaeF8ucWfKUccWnPo+Lb68Ogpo+aMortMu7q6mmXFZNEmp4GeXSrXhJ//rr4YqajmgrJC6zXjyWqTvE1tcM8JJ4/oKKKg/awIx+3t7RT9z+oJDofDTD5w7Gr3Es+3P/c1VMkxr+88qAK1U0UhqOOs1+vZGB4fH6c5zDz+VTTLy3gUrFqHLV3L14nzgYyGGZ6EbK1nOo1kDfee+meUmmNUG4p2OR7OO/1zha/jlvG1DJcMt0xvqeryd84FM2kk65k9w0weymZmfCnDSLh45tTz8/OMl5AWzme
|
2020-02-26 16:05:21 -03:00
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|
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"text/plain": [
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"<Figure size 1152x1152 with 1 Axes>"
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]
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|
},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
|
2020-06-15 18:48:44 -04:00
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAA4sAAALoCAYAAADRBGAjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9e6y133bXNeba9/2+7++c01MqtmkbleAFI4ixqNGUKFGRiwaNxaCxRpH+oQTThBqLBA0QMaQWhaSIGIzFKl7ANpZE+YNEAojQNBITTYoBegvQ09/vd959W2vtvR7/2Pv77M/zXWPMtd7j0Vo6R7Kz11rPvIw55pzjPufTpmmKAQMGDBgwYMCAAQMGDBgwgLD6qUZgwIABAwYMGDBgwIABAwb8/w+GsThgwIABAwYMGDBgwIABA/ZgGIsDBgwYMGDAgAEDBgwYMGAPhrE4YMCAAQMGDBgwYMCAAQP2YBiLAwYMGDBgwIABAwYMGDBgD4axOGDAgAEDBgwYMGDAgAED9mAYiwMGDBgw4KcNtNb+99baL/4p6vvfaq39J19i3T/QWvutX26cvlRorf2c1tp4d9aAAQMGDOjCMBYHDBgwYMAMrbW/2Fq7b629b6190lr7k621b2mtHSUvWmu/uLX2I18mXPYMrGmaft40TX/8y9G+9fXHW2sPL+P+Ymvtz7XW/s3W2gX6/u3TNP0rX+6+j8Btaq3dttZuXv4++X+hjz/RWvvmL3e7AwYMGDDgpzcMY3HAgAEDBjj8imma3kXE10fEvxcR3xYRv/+nFqX/T+Bfexn33xgR3xoRvzoivr+11n5q0YqIiJ8/TdPbl7/P/lQjM2DAgAEDfmbAMBYHDBgwYEAK0zR9Ok3T90bEN0XEv9ha+zsjIlprF62139la+8uttb/SWvuu1tpVa+1NRPzRiPhqRMG+urW2eonS/YXW2hdaa3+otfYV6qe19g++RDA/aa39cGvtm1tr/2pE/JqI+I0v7XzfS9m/2Fr7JcDjO1trP/by952KBCrC2Vr71tbaX22t/Xhr7V86cty3L9HLXxkRf39E/LKXNn9La+27gfevfEmL/eQlMvm349nf3Vr7gZdI5X8VEZfso7X2y1trP4jo7d/1gdMTrbXPt9a+v7X211prH7fWvq+19jV4/iNM2W2t/dbW2h9I2vkdL+P8rhdaf+eH4jJgwIABA/76hGEsDhgwYMCALkzT9Gci4kci4h96+el3RMTPjYhfEBE/JyK+JiJ+8zRNtxHxSyPixxAF+7GI+PUR8U9FxDdGxFdHxMcR8XsiIlprXxfPBuZ/FBE/66XNH5ym6T+OiD8YEf/+Szu/IkHt2yPi73up8/Mj4hsi4jfh+c+OiM+84PcvR8Tvaa197gPG/Zcj4s9i3DO01n5uRHxPRPyGF7y/PyK+r7V23lo7j4g/EhH/eUR8RUT81xHxT6PuL4yI/zQifl1EfD4ifm9EfC9TXo+EVUT8voj4uniOAm8j4nd9YBsxTdO3RcSfiohveaH1b/jQNgYMGDBgwF+fMIzFAQMGDBhwDPxYRHzFS0rmr42If2Oapp+cpul9RPz2eE7ZrODXRcS3T9P0I9M0rSPit0TEP9NaO43n6OEfm6bpe6Zp2k7T9IVpmn7wSJx+TUT8u9M0/dVpmv5aRPw7EfEv4Pn25fl2mqbvj4ibiPhbjx9yRLyMO/n9myLif5im6X+apmkbEb8zIq4i4h+IZwP2LCK+86Xv/yYi/lfU/bUR8Xunafpfpml6mqbpP4uI9Uu9Cn7gJQr5SWvtP4yImKbpr03T9IenabqfpumL8TwP3/iB4xswYMCAAQNKOP2pRmDAgAEDBvy0gK+JiJ+M5yjadUT8ORzlaxFx0qn79RHxh1trO/z2FBF/Q0R8bUT8hS8Rp6+OiL+E73/p5TfBF6ZpesT3u4h4+4F9fE1E/MlDfU/TtGut/fBL+aeI+NFpmnjbKPH8+nhO6/3X8du54e7wC6dp+iH+8JL2+7si4h+NCJ1jfNcfzoABAwYMGHA8jMjigAEDBgzoQmvt741nI+hPRMRPRMR9RPy8aZo++/L3mWmaZIRlr2P44Yj4pSj/2WmaLqdp+tGXZ39L0fWhVzv8WDwbXoKve/ntywKtta+NiL8nIv7nQ32/RFy/NiJ+NCJ+PCK+xi7G+Tp8/uGI+G1Gj+tpmr7nA1H8jRHxN0XEN0zT9FFE/MP2/DaeDXvBz+60NV6jMWDAgAED9mAYiwMGDBgwIIXW2kettV8eEf9lRHz3NE1/fpqmXTyfk/sPWmtf9VLua1pr/9hLtb8SEZ9vrX0GTX1XRPy21trXv5T/Wa21f/Ll2R+MiF/SWvtnW2unL5e2/AK09Td3UPyeiPhNL+19ZUT85oj47k75Y8d93Vr7xoj47yPiz8TzeUSHPxQRv6y19o+01s7i+fbUdTxHIf9URDxGxK9/GdOviufzlILfFxHf0lr7Re0Z3rTWfllr7UOjgu/iOVr6cWvt8/E8fsIPRsSvfsHhGyLiV3XaOkTrAQMGDBjwMxCGsThgwIABAxy+r7X2Pp4jYN8eEd8REbxJ9Nsi4oci4k+31r4YEX8sXs4CTtP0f8SzEfd/vZyv++p4TpX83oj4H1/a/dMR8Yteyv/liPgn4tnY+sl4NnB+/ks/vz8i/o6Xdv5IgudvjecLaP63iPjzEfEDL799qfC7X/D7KxHxnRHx30bEP/5iIC9gmqb/MyL++Xi+mOcnIuJXxPMrRzbTNG3i2TD75ni+zOebIuK/Q90/G8/nFn/3y/Mfein7ofAd8XyBzxfi2Uj9o/b82yPib4uITyLi346I/6LT1ndGxD/3Quvv+BJwGTBgwIABfx1CWx6pGDBgwIABAwYMGDBgwIABA0ZkccCAAQMGDBgwYMCAAQMGJDCMxQEDBgwYMGDAgAEDBgwYsAfDWBwwYMCAAQMGDBgwYMCAAXswjMUBAwYMGDBgwIABAwYMGLAHw1gcMGDAgAEDBgwYMGDAgAF7cNp7+K3f+q2T3inMdws/PT3FdruNp6eniIiYpinOzs6eGzw9jZOTk0V5fZ6mKXj76m73ehv5NE3RWovVarX47m3wM9slsJ/W2l5d1jvUFutkZXxMjsfyncyvY9ZYNd6Tk5OIiNhutxER8fDwMH9mnYhY1GNf1fiIg8o53k9PT4vvrLPb7cpnjsM0TTO+FS29PHHN6l1cXMTbt2/nz9M0xccffxwREe/fv4/NZjPThf9PT0/36ERgeZY7OTlZ4FWV1WeVIZ00v5rX1Wq1mIvdbjfvn9ZanJyczO221uL09HVrPj09xePj4wI//eeeWa1Wc7mf/MmfjC984Qtxd3c3PxMNr6+v4/z8fO5De1Z9Pz09xe3tbUS8rkfSU3j7uliv14vvGrOAz05OTuL8/HxuV593u11st9t5Da3X6xkH4Sm8z87OZrxPT08X/ET98z/xYNnVajV/5+/6THqLRqrnvxEcH37nWmf/DpxfrkvRSfOr/xGvdLm8vJzxJJ87xK98H2b8u+KFVbv+Pas3TdNinQv3qh3icHp6GhcXFxHxTCe18/DwEA8PDwv6+FxxDZE+1XwdM75KdmX1e+uiolMld3xtc/1cXFzE9fV1vHnzZu735uYmPv3004iIeHx8nPfW4+Nj3N3dzbz17OxsTzZXcoDrTePg2DKdwvntofLHyJhM/no575flRYvr6+u4vr6e19f9/X3c3NxERMTt7e2CX2X9k18cwueYcsQ3Yp8veblKd/Ly1b7PdCiC6PL27du4vLycaXFzcxPv37+PiGcZtl6v533JfX5xcbEng3q0IF9nWX3OdApfgz4+yjLv03mi5J/Kuo6gOr5/pSPrv9rZbrczPcTTJX93u91Ml4uLizg/P5/ri27sQ7JSbWr/Pj4+zs8eHx/3ZDd1pvPz83lOz87OSr0z43tOW/6erU/pnSxTrWfpSNlc+D6mDZL123vGMj7eam9nctXtG29D3yu56XYH2+nptT1ZleFD+I7v+I6SICOyOGDAgAEDBgwYMGDAgAED9qAbWdztdqUFTq8JvYf6Luh
|
2020-02-26 16:05:21 -03:00
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"text/plain": [
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|
|
"<Figure size 1152x1152 with 1 Axes>"
|
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|
|
]
|
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|
},
|
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|
"metadata": {
|
|
|
|
|
"needs_background": "light"
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},
|
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"output_type": "display_data"
|
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}
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],
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|
"source": [
|
2020-08-26 00:39:38 -04:00
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|
"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
|
2020-02-26 16:05:21 -03:00
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|
"\n",
|
|
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"\n",
|
|
|
|
|
"image = cv2.imread(image_path)\n",
|
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"\n",
|
|
|
|
|
"## Show original image\n",
|
|
|
|
|
"fig, ax = plt.subplots(figsize=(16, 16))\n",
|
|
|
|
|
"ax.set_title('Original Image')\n",
|
|
|
|
|
"plt.imshow(image, cmap='gray')\n",
|
|
|
|
|
"ax.axis('off') \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"start = time.time()\n",
|
|
|
|
|
"## predict the bounding boxes\n",
|
|
|
|
|
"boxes = get_yolo_boxes(infer_model, [image], net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
|
|
|
|
|
"print('Elapsed time = {}'.format(time.time() - start))\n",
|
|
|
|
|
"## draw bounding boxes on the image using labels\n",
|
|
|
|
|
"draw_boxes(image, boxes, config['model']['labels'], obj_thresh)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"## Show Detection Fault\n",
|
|
|
|
|
"fig, ax = plt.subplots(figsize=(16, 16))\n",
|
2020-06-15 18:48:44 -04:00
|
|
|
"ax.set_title('Detection Diode Fault')\n",
|
|
|
|
|
"plt.imshow(image, cmap='gray')\n",
|
|
|
|
|
"ax.axis('off') "
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"## Load Trained Model Cell Damage Fault"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 6,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"## Config of trained model, change this for use different trained model\n",
|
|
|
|
|
"config_path = 'config_full_yolo_fault_2_infer.json' \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"with open(config_path) as config_buffer:\n",
|
|
|
|
|
" config = json.load(config_buffer)\n",
|
|
|
|
|
" \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"###############################\n",
|
|
|
|
|
"##### Load the model ######\n",
|
|
|
|
|
"###############################\n",
|
|
|
|
|
"os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']\n",
|
|
|
|
|
"infer_model = load_model(config['train']['saved_weights_name'])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"## Parameters of detection\n",
|
|
|
|
|
"net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster\n",
|
|
|
|
|
"obj_thresh, nms_thresh = 0.5, 0.45\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"#infer_model.summary()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"## Detection Affected Cell Fault"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 17,
|
|
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|
|
"metadata": {},
|
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|
"outputs": [
|
|
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|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"Elapsed time = 5.986032247543335\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"(-0.5, 639.5, 511.5, -0.5)"
|
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|
|
]
|
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|
|
},
|
|
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|
|
"execution_count": 17,
|
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|
"metadata": {},
|
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|
|
|
"output_type": "execute_result"
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|
|
},
|
|
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|
{
|
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|
|
"data": {
|
|
|
|
|
"image/png": "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
|
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|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1152x1152 with 1 Axes>"
|
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|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1152x1152 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
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|
|
],
|
|
|
|
|
"source": [
|
2020-08-26 00:39:38 -04:00
|
|
|
"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
|
2020-06-15 18:48:44 -04:00
|
|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"image = cv2.imread(image_path)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"## Show original image\n",
|
|
|
|
|
"fig, ax = plt.subplots(figsize=(16, 16))\n",
|
|
|
|
|
"ax.set_title('Original Image')\n",
|
|
|
|
|
"plt.imshow(image, cmap='gray')\n",
|
|
|
|
|
"ax.axis('off') \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"start = time.time()\n",
|
|
|
|
|
"## predict the bounding boxes\n",
|
|
|
|
|
"boxes = get_yolo_boxes(infer_model, [image], net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
|
|
|
|
|
"print('Elapsed time = {}'.format(time.time() - start))\n",
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|
|
"## draw bounding boxes on the image using labels\n",
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"draw_boxes(image, boxes, config['model']['labels'], obj_thresh)\n",
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"\n",
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"\n",
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|
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|
|
"## Show Detection Fault\n",
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|
|
"fig, ax = plt.subplots(figsize=(16, 16))\n",
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|
|
"ax.set_title('Detection Affected Cell Fault')\n",
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|
"plt.imshow(image, cmap='gray')\n",
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"ax.axis('off') "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
|
|
|
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"source": [
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|
|
"## Load Trained Model Panel Detection"
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|
]
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|
},
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|
{
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|
"cell_type": "code",
|
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|
|
|
"execution_count": 14,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
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|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"## Config of trained model, change this for use different trained model\n",
|
|
|
|
|
"config_path = 'config_full_yolo_panel_infer.json' \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"with open(config_path) as config_buffer:\n",
|
|
|
|
|
" config_panel = json.load(config_buffer)\n",
|
|
|
|
|
" \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"###############################\n",
|
|
|
|
|
"##### Load the model ######\n",
|
|
|
|
|
"###############################\n",
|
|
|
|
|
"os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']\n",
|
|
|
|
|
"infer_model_panel = load_model(config_panel['train']['saved_weights_name'])\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"#infer_model.summary()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"## Parameters of detection\n",
|
|
|
|
|
"net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster\n",
|
|
|
|
|
"obj_thresh, nms_thresh, nms_thresh_panel = 0.5, 0.45, 0.3"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"## Panel Detection"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 15,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"(-0.5, 639.5, 511.5, -0.5)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 15,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA4sAAALoCAYAAADRBGAjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9a6i025bfNZ5aq2qtWu9l78M5NsYIHdR4iXgBQUxQFOI10CSIETGiQYSgkg/SKnSLwf5gxBAaohKV+M0WIzaJoKISREWxI+iHoE2MKLYaPDGc03vv865LXVbV44f1/p/1e/41xqx6t+f06fbMAYtVVc+8jDnmnOM+5zOM4xgdOnTo0KFDhw4dOnTo0KEDYfHDRqBDhw4dOnTo0KFDhw4dOvzqg24sdujQoUOHDh06dOjQoUOHE+jGYocOHTp06NChQ4cOHTp0OIFuLHbo0KFDhw4dOnTo0KFDhxPoxmKHDh06dOjQoUOHDh06dDiBbix26NChQ4cOHTp06NChQ4cT6MZihw4dOnT4VQnDMPz0MAz/1ve77AVtjcMw/GXfj7Y6dOjQoUOHX8sw9PcsdujQoUOHHzQMw/C7I+InI+IvjYjvRcQfj4ifGsfxyx8mXhkMwzBGxG8cx/F/TZ79lxHxc+M4fl8M0w4dOnTo0OFXM/TIYocOHTp0+IHCMAw/GRH/ckT8MxHxWUT8TRHx4xHxJ4ZhWBV1rn/lMOzQoUOHDh06ZNCNxQ4dOnTo8AODYRjeR8TPRMTvHcfxPx3HcT+O4y9FxN8fLwbjP/Sx3L8wDMPPD8Pwc8MwfC8ifvfH334Obf3DwzD8H8MwfHcYhn9+GIZfGobhb0f9n/v4+Td8TCX9R4Zh+D+HYfjOMAz/HNr5G4dh+IVhGL4chuHbwzD8a5XRemZsf9swDH92GIZ/dhiGP/+xrd8xDMNvG4bhfxmG4ZeHYfjpS/sdhuHvHIbhzwzD8NUwDH94GIb/ahiGfwzP/9FhGP70MAxfDMPwnw3D8OOfinOHDh06dOjwKdCNxQ4dOnTo8IOE3xIRtxHxx/jjOI73EfGfRMTfgZ9/e0T8fER8HhH/DssPw/CbIuIPR8TviohfFy8Ryl9/pu+/OSL+ioj4rRHx+4Zh+Ks+/n6IiH8qIr4VEb/54/N/4hPHJfgL42V8vz4ifl9E/JF4MYD/hoj4Wz72+5ec63cYhm/Fy9h/KiK+GRF/Jl5oFx+f/46I+OmI+Hsj4i+IiP86Iv7dr4lzhw4dOnTocBF0Y7FDhw4dOvwg4VsR8Z1xHJ+TZ9/++FzwC+M4/gfjOB7HcXyysn9fRPyH4zj+N+M47uLFMDt36P5nxnF8GsfxT0XEn4qIvy4iYhzH/2Ecxz85juPzxyjnvxkRf+unDy0iIvYR8S+O47iPiD/6cTx/aBzHD+M4/mJE/GJE/LUX9PvbIuIXx3H8Yx9p9a9ExJ9DP78nIv6lcRz/9Mfnvz8i/voeXezQoUOHDj9I6MZihw4dOnT4QcJ3IuJbxRnEX/fxueD/arTzF/H5OI6PEfHdM33T2HqMiLcREcMw/OXDMPxHwzD8uY8pr78/5kbrp8B3x3E8fPwsA/f/wfOnC/v18Y0R8WfRzo9HxB/6mML6ZUT8ckQMcT662qFDhw4dOnxt6MZihw4dOnT4QcIvRMQ2XtInJxiG4U1E/D0R8Z/j51ak8NsR8Rej/jpe0jW/DvzrEfE/x8uNp+/jJb1z+Jptfb/69fEN/B4vhuTvGcfxc/ytx3H8b38F8O7QoUOHDj+i0I3FDh06dOjwA4NxHL+Klwtu/tVhGP7uYRiWwzD8hoj49+MlcvZvX9jUz0fETwzD8Fs+XgrzM/H1Dbx38fL6jvthGP7KiPjHv2Y7389+/+OI+Gs+XpBzHRH/ZLychxT8GxHxU8Mw/NUREcMwfDYMw+/8FcK7Q4cOHTr8iEI3Fjt06NChww8UxnH8A/ESRfuD8WIs/XfxEin7reM4bi9s4xcj4vfGy7nAb0fEh4j48/EStfxU+Kcj4h/82MYfiYh/72u08XWg7Hccx+9ExO+MiD8QL+m1vyki/vv4OL5xHP94vLx+5I9+TGH9n+IlMtuhQ4cOHTr8wGB4ORbRoUOHDh06/NqBYRjeRsSX8ZLS+b//sPH5fsMwDIt4ibz+rnEc/4sfNj4dOnTo0OFHE3pksUOHDh06/JqAYRh+YhiGu4/nHf9gRPyPEfFLP1ysvn8wDMPfNQzD58Mw3MTrecY/+UNGq0OHDh06/AhDNxY7dOjQocOvFfjtEfF/f/z7jRHxD4z//0qP+c0R8b/Fyw2xPxERvyN5hUiHDh06dOjwKwY9DbVDhw4dOnTo0KFDhw4dOpxAjyx26NChQ4cOHTp06NChQ4cT6MZihw4dOnTo0KFDhw4dOnQ4gevWw5/8yZ8cX94LHKH/ERGHwyH2+30cDoeIiBjHMZbL5UuD19dxdXU1K6/P4zgG016Px+P0eRzHGIYhFovF7Lu3wc9sl8B+hmE4qct659pinayMj8nxYN8cs8aq8V5dXUVExH6/j4iIzWYzfWadiJjVY1/V+IiDyjneh8Nh9p11jsdj+cxxGMdxwreipZcnrlm9m5ubePv27fR5HMf44osvIiLiw4cPsdvtJrrw//X19QmdCCzPcldXVzO8qrL6rDKkk+ZX87pYLGZzcTwep/0zDENcXV1N7Q7DENfXr1vzcDjE8/PzDD/9555ZLBZTuV/+5V+O7373u/H4+Dg9Ew3v7u5itVpNfWjPqu/D4RAPDw8R8boeSU/h7etiu93OvmvMAj67urqK1Wo1tavPx+Mx9vv9tIa22+2Eg/AU3svlcsL7+vp6xk/UP/8TD5ZdLBbTd/6uz6S3aKR6/hvB8eF3rnX278D55boUnTS/+h/xSpfb29sJT/K5c/zK92HGvyteWLXr37N64zjO1rlwr9ohDtfX13FzcxMRL3RSO5vNJjabzYw+PldcQ6RPNV+XjK+SXVn91rqo6FTJHV/bXD83Nzdxd3cXb968mfq9v7+Pr776KiIinp+fp731/Pwcj4+PE29dLpcnsrmSA1xvGgfHlukUzm/Plb9ExmTy18t5vywvWtzd3cXd3d20vp6enuL+/j4iIh4eHmb8Kuuf/OIcPpeUI74Rp3zJy1W6k5ev9n2mQxFEl7dv38bt7e1Ei/v7+/jw4UNEvMiw7XY77Uvu85ubmxMZ1KIF+TrL6nOmU/ga9PFRlnmfzhMl/1TWdQTV8f0rHVn/1c5+v5/oIZ4u+Xs8Hie63NzcxGq1muqLbuxDslJtav8+Pz9Pz56fn09kN3Wm1Wo1zelyuSz1zozvOW35e7Y+pXeyTLWepSNlc+H7mDZI1m/rGcv4eKu9nclVt2+8DX2v5KbbHWynpde2ZFWGD+Fnf/ZnS4L0yGKHDh06dOjQoUOHDh06dDiBZmTxeDyWFji9JvQe6rug5cn2djOLO7OSM2v7Eg+3g9erPG1Zu5WXm+P1yJN7uuTRinjxsNCTsVqtSi8zPTzqL/PeqL3MM5F5lqqo0PF4nHly5fnxMQkPenuy6DHLst3K28EIkjxGiprQW+ljyuaXfTBiy7KM4LCu/leeTMfdy/oe4dg5p+P4GqnP6OieVXnA6XW8vb2Nu7u7mSeXXsfD4TDzJjICGhGzqKfGdDgcTtYCcWSEzPcvxy/vofC+ubmZ8N5sNrHb7WZrSOXcs8j5zmjk6ynDWzhnkUWOTePnOLjWM97BPdnyzld7JBsX+cdqtZqNiZHQzMvN+hUurYgDaeF8ucWfKUccWnPo+Lb68Ogpo+aMortMu7q6mmXFZNEmp4GeXSrXhJ//rr4YqajmgrJC6zXjyWqTvE1tcM8JJ4/oKKKg/awIx+3t7RT9z+oJDofDTD5w7Gr3Es+3P/c1VMkxr+88qAK1U0UhqOOs1+vZGB4fH6c5zDz+VTTLy3gUrFqHLV3L14nzgYyGGZ6EbK1nOo1kDfee+meUmmNUG4p2OR7OO/1zha/jlvG1DJcMt0xvqeryd84FM2kk65k9w0weymZmfCnDSLh45tTz8/OMl5AWzme
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1152x1152 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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"text/plain": [
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"<Figure size 1152x1152 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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2020-08-26 00:39:38 -04:00
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"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
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2020-06-15 18:48:44 -04:00
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"\n",
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"\n",
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"image = cv2.imread(image_path)\n",
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"\n",
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"## Show original image\n",
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"fig, ax = plt.subplots(figsize=(16, 16))\n",
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"ax.set_title('Original Image')\n",
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"plt.imshow(image, cmap='gray')\n",
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"ax.axis('off') \n",
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"\n",
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"start = time.time()\n",
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"## predict the bounding boxes\n",
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"boxes_panel = get_yolo_boxes(infer_model_panel, [image], net_h, net_w, config_panel['model']['anchors'], obj_thresh, nms_thresh_panel)[0]\n",
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"boxes_panel = [box for box in boxes_panel if box.get_score() > obj_thresh]\n",
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"\n",
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"## draw bounding boxes on the image using labels\n",
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"draw_boxes(image, boxes_panel, config['model']['labels'], obj_thresh)\n",
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"\n",
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"\n",
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"## Show Detection Fault\n",
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"fig, ax = plt.subplots(figsize=(16, 16))\n",
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"ax.set_title('Panel Detection')\n",
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"plt.imshow(image, cmap='gray')\n",
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"ax.axis('off') "
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]
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},
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{
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|
"cell_type": "markdown",
|
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"metadata": {},
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|
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|
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"source": [
|
|
|
|
|
"## Detection Panel Disconect"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
|
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"name": "stdout",
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|
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|
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"output_type": "stream",
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|
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"text": [
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|
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|
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"Elapsed time = 0.003537893295288086\n"
|
|
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]
|
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},
|
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|
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{
|
|
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"data": {
|
|
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|
"text/plain": [
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|
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"(-0.5, 639.5, 511.5, -0.5)"
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]
|
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},
|
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"execution_count": 16,
|
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"metadata": {},
|
|
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|
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"output_type": "execute_result"
|
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|
},
|
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{
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"data": {
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"image/png": "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
|
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|
"text/plain": [
|
|
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|
|
"<Figure size 1152x1152 with 1 Axes>"
|
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|
]
|
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|
},
|
|
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|
"metadata": {
|
|
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1152x1152 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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2020-08-26 00:39:38 -04:00
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"image_path = '../images/Mision 23_DJI_0061.jpg' \n",
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2020-06-15 18:48:44 -04:00
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"\n",
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"\n",
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"image = cv2.imread(image_path)\n",
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"\n",
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"## Show original image\n",
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"fig, ax = plt.subplots(figsize=(16, 16))\n",
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"ax.set_title('Original Image')\n",
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"plt.imshow(image, cmap='gray')\n",
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"ax.axis('off') \n",
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"\n",
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"start = time.time()\n",
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"## predict the bounding boxes\n",
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"boxes_disc = disconnect(image, boxes_panel, z_thresh = 1.8)\n",
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"print('Elapsed time = {}'.format(time.time() - start))\n",
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"\n",
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"draw_boxes(image, boxes_disc, config['model']['labels'], obj_thresh)\n",
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"\n",
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"\n",
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"## Show Detection Fault\n",
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"fig, ax = plt.subplots(figsize=(16, 16))\n",
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"ax.set_title('Detection Panel Disconect')\n",
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2020-02-26 16:05:21 -03:00
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"plt.imshow(image, cmap='gray')\n",
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"ax.axis('off') "
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2020-02-25 22:18:56 -03:00
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
|
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
|
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"mimetype": "text/x-python",
|
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|
"name": "python",
|
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|
|
"nbconvert_exporter": "python",
|
|
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|
|
"pygments_lexer": "ipython3",
|
2020-04-16 14:11:33 -04:00
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|
"version": "3.7.7"
|
2020-02-25 22:18:56 -03:00
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}
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},
|
|
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|
"nbformat": 4,
|
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"nbformat_minor": 2
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}
|
