Merge pull request #29 from RentadroneCL/yolo3_tensorflow2
Creating Classifier
This commit is contained in:
Daniel Saavedra
@@ -39,8 +39,8 @@
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"outputs": [],
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"source": [
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"input_path = '../images/Mision 23_DJI_0061.jpg' \n",
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"#output_path = 'Result_Complete_Example/'\n",
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"#makedirs(output_path)"
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"output_path = 'Result_Complete_Example/'\n",
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"makedirs(output_path)"
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]
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},
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{
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@@ -195,16 +195,59 @@
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" draw_boxes(image, boxes_cellD, config_cellD['model']['labels'], obj_thresh, number_color = 4)\n",
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" draw_boxes(image, boxes_disc, ['disc'], obj_thresh, number_color = 5)\n",
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" \n",
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" cv2.imwrite(output_path + image_path.split('/')[-1], np.uint8(image))\n",
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" # write the image with bounding boxes to file\n",
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" plt.figure(figsize=(16, 16))\n",
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" plt.imshow(image)\n",
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" #plt.figure(figsize=(16, 16))\n",
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" #plt.imshow(image)\n",
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"\n",
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"#file = open(output_path + 'time.txt','w')\n",
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"#file.write('Tiempo promedio:' + str(np.mean(times)))\n",
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"#file.close()\n",
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"file = open(output_path + 'time.txt','w')\n",
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"file.write('Tiempo promedio:' + str(np.mean(times)))\n",
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"file.close()\n",
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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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"# Example One image"
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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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"image_path = image_paths[55]\n",
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"\n",
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"image = cv2.imread(image_path)\n",
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"plt.figure(figsize=(12, 10))\n",
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"plt.imshow(image)\n",
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"print(image_path)\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_soiling = get_yolo_boxes(infer_model_soiling, [image], net_h, net_w, config_soiling['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
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"boxes_diode = get_yolo_boxes(infer_model_diode, [image], net_h, net_w, config_diode['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
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"boxes_cellD = get_yolo_boxes(infer_model_cellD, [image], net_h, net_w, config_cellD['model']['anchors'], obj_thresh, nms_thresh)[0]\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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"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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"times.append(time.time() - start)\n",
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"# Draw boxes\n",
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"draw_boxes(image, boxes_soiling, config_soiling['model']['labels'], obj_thresh, number_color = 0)\n",
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"draw_boxes(image, boxes_diode, config_diode['model']['labels'], obj_thresh, number_color = 2)\n",
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"draw_boxes(image, boxes_cellD, config_cellD['model']['labels'], obj_thresh, number_color = 4)\n",
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"draw_boxes(image, boxes_disc, ['disc'], obj_thresh, number_color = 5)\n",
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"\n",
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"print('Elapsed time = {}'.format(time.time() - start))\n",
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"times.append(time.time() - start)\n",
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"plt.figure(figsize=(12, 10))\n",
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"plt.imshow(image)"
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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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@@ -39,8 +39,8 @@
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"outputs": [],
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"source": [
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"input_path = '../images/Mision 23_DJI_0061.jpg' \n",
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"#output_path = 'Result_Complete_Example/'\n",
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"#makedirs(output_path)"
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"output_path = 'Result_Complete_Example/'\n",
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"makedirs(output_path)"
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]
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},
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{
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@@ -195,16 +195,59 @@
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" draw_boxes(image, boxes_cellD, config_cellD['model']['labels'], obj_thresh, number_color = 4)\n",
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" draw_boxes(image, boxes_disc, ['disc'], obj_thresh, number_color = 5)\n",
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" \n",
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" cv2.imwrite(output_path + image_path.split('/')[-1], np.uint8(image))\n",
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" # write the image with bounding boxes to file\n",
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" plt.figure(figsize=(16, 16))\n",
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" plt.imshow(image)\n",
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" #plt.figure(figsize=(16, 16))\n",
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" #plt.imshow(image)\n",
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"\n",
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"#file = open(output_path + 'time.txt','w')\n",
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"#file.write('Tiempo promedio:' + str(np.mean(times)))\n",
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"#file.close()\n",
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"file = open(output_path + 'time.txt','w')\n",
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"file.write('Tiempo promedio:' + str(np.mean(times)))\n",
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"file.close()\n",
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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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"# Example One image"
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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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"image_path = image_paths[55]\n",
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"\n",
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"image = cv2.imread(image_path)\n",
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"plt.figure(figsize=(12, 10))\n",
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"plt.imshow(image)\n",
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"print(image_path)\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_soiling = get_yolo_boxes(infer_model_soiling, [image], net_h, net_w, config_soiling['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
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"boxes_diode = get_yolo_boxes(infer_model_diode, [image], net_h, net_w, config_diode['model']['anchors'], obj_thresh, nms_thresh)[0]\n",
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"boxes_cellD = get_yolo_boxes(infer_model_cellD, [image], net_h, net_w, config_cellD['model']['anchors'], obj_thresh, nms_thresh)[0]\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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"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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"times.append(time.time() - start)\n",
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"# Draw boxes\n",
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"draw_boxes(image, boxes_soiling, config_soiling['model']['labels'], obj_thresh, number_color = 0)\n",
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"draw_boxes(image, boxes_diode, config_diode['model']['labels'], obj_thresh, number_color = 2)\n",
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"draw_boxes(image, boxes_cellD, config_cellD['model']['labels'], obj_thresh, number_color = 4)\n",
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"draw_boxes(image, boxes_disc, ['disc'], obj_thresh, number_color = 5)\n",
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"\n",
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"print('Elapsed time = {}'.format(time.time() - start))\n",
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"times.append(time.time() - start)\n",
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"plt.figure(figsize=(12, 10))\n",
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"plt.imshow(image)"
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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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2
GPS_Panel/.gitignore
vendored
2
GPS_Panel/.gitignore
vendored
@@ -3,4 +3,6 @@
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/Data_set_Segmentation
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/El_Aguila
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/El_Aguila_2020
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/Pampa
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File diff suppressed because one or more lines are too long
431
GPS_Panel/.ipynb_checkpoints/Enumeration_KML-checkpoint.ipynb
Normal file
431
GPS_Panel/.ipynb_checkpoints/Enumeration_KML-checkpoint.ipynb
Normal file
File diff suppressed because one or more lines are too long
259
GPS_Panel/ClassifierPanel_KML.ipynb
Normal file
259
GPS_Panel/ClassifierPanel_KML.ipynb
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File diff suppressed because one or more lines are too long
@@ -2,18 +2,9 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/dlsaavedra/anaconda3/envs/new/lib/python3.7/site-packages/pysal/__init__.py:65: VisibleDeprecationWarning: PySAL's API will be changed on 2018-12-31. The last release made with this API is version 1.14.4. A preview of the next API version is provided in the `pysal` 2.0 prelease candidate. The API changes and a guide on how to change imports is provided at https://migrating.pysal.org\n",
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" ), VisibleDeprecationWarning)\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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@@ -70,11 +61,13 @@
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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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"source": [
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"# Parameters"
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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": 2,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -86,6 +79,7 @@
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"ZonaPV = 'BP-B-5'\n",
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"path_kml_panel = 'Pampa/KML/Paneles_' + ZonaPV +'.kml'\n",
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"path_kml_mesa ='Pampa/KML/Mesa_' + ZonaPV +'.kml'\n",
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"path_dict = 'Pampa/KML/Mesa_' + ZonaPV + '.pickle'\n",
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"\n",
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"panel_size = (29 , 50)\n",
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"#panel_size = (37 , 17) #weight, height size#\n",
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@@ -95,8 +89,22 @@
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"\n",
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"\n",
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"GR_String = gr.from_file(path_String)\n",
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"GR_T = gr.from_file(path_T)\n",
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"\n",
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"GR_T = gr.from_file(path_T)\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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"# Enumerate Tables and Panels"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"label_im, nb_labels = ndimage.label(GR_String.raster.data > 0)#, structure= np.ones((2,2))) ## Label each connect region\n",
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"\n",
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@@ -154,11 +162,13 @@
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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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"source": [
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"# Save dictionary with gps data"
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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": 3,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -190,7 +200,10 @@
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" string_coord['panels'][panel_key] = panel_coord\n",
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" \n",
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" L_strings_coord[string_key] = string_coord\n",
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"\n"
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"\n",
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"## Save List in coordinate latitud and longitude ###\n",
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"with open(path_dict, 'wb') as handle:\n",
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" pickle.dump(L_strings_coord, handle, protocol=pickle.HIGHEST_PROTOCOL)"
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]
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},
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{
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@@ -202,7 +215,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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@@ -308,7 +321,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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@@ -320,6 +333,10 @@
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}
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],
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"source": [
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"\n",
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"## Load List in coordinate latitud and longitude ###\n",
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"with open(path_dict, \"rb\") as fp:\n",
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" L_strings_coord = pickle.load(fp)\n",
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"\n",
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"kml=simplekml.Kml()\n",
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"\n",
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File diff suppressed because one or more lines are too long
@@ -1024,7 +1024,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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"version": "3.7.7"
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}
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},
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"nbformat": 4,
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@@ -1,661 +1,661 @@
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File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user