Merge pull request #40 from dfguerrerom/master

Included new reclassify tab

acc_ui.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "honey-roller",
    "metadata": {},
    "outputs": [],
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "associate-terry",
    "metadata": {},
    "outputs": [],
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "seven-terry",
    "metadata": {},
    "outputs": [],
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "tested-pension",
    "metadata": {},
    "outputs": [],
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "early-tiger",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "metropolitan-great",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "de9343ce2ea3452f98ae2b52e5e84480",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tile(align_center=True, children=[Card(children=[Html(children=['ACC: Accounting of image objects and areas'],…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "acc_title"
    ]
@@ -83,41 +98,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "accessible-folder",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74e80d8770164d9a96234386e6483da6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "ConvertByte(align_center=True, children=[Card(children=[Html(children=['Select input map classes'], tag='h2'),…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "acc_convert"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "beginning-engagement",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "237453fa868b4ab08e7c631396787fbc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "AccTile(align_center=True, children=[Card(children=[Html(children=['Run Process'], tag='h2'), Flex(children=[S…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "acc_process"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "declared-pound",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "acc_io.__dict__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "sexual-playing",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

component/message/en.json

@@ -13,6 +13,7 @@
             "parc": "PARC",
             "rss": "RSS",
             "spa": "SPA",
+            "rcl":"Reclassify",
             "about": "About",
             "licence": "Licence"
         },
@@ -21,6 +22,10 @@
             "btn": "Accept the term of licence" 
         }
     },
+    "reclassify": {
+        "title": "Reclassify local rasters",
+        "description": "This module will allow you to reclassify your local raster values into a new custom codes. <h2>Usage</h2></br>Navigate over your sepal storage environment and select the input `.tif` image, in the next dropdown use a manual classification if you don't have a classification `.csv` table, otherwise select the corresponding file. </br> Go to the 'Customize Classification' tab in order to create or modify existing classification files, and use them after in the workflow.<br><br>Once you have updated your files, click over the 'Get reclassify table' and choose the target values for each class."
+    },
     "acc": {
         "title":  "ACC: Accounting of image objects and areas",
         "description": "This module will conduct the Accounting analysis. Accounting will label and calculate the area of all foreground objects (coded with 2 bytes). The results are spatially explicit maps and tabular summary statistics. Details on the methodology can be found in the (mac: right+click or windows: cmd+click to open in new tab) [Accounting product sheet](https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Objects-Accounting.pdf).",
@@ -102,6 +107,8 @@
         "btn": "Convert the input image classes",
         "title": "Select input map classes",
         "file_exist": "the file {} already exists, no new bin maps have been created",
+        "reclassify_exist": "The file {} already exists.",
+        "no_exists" : "The provided {} file doesn't exists.",
         "running": "The bin map is being processed",
         "finished": "the bin map is ready to be used",
         "no_file": "Please provide a file to be reclassified",

component/scripts/pre_processing.py

@@ -36,6 +36,10 @@ def set_byte_map(class_list, raster, process, output):
             each nested list is the value of the class that need to be included into each byte value.
         raster (pathlib.Path): the path to the original image
     """
+    print('class_list',class_list)
+    print('raster',raster)
+    print('process',process)
+    print('output',output)
     # check that the inputs are all separated
     if is_overlap(class_list):
         raise Exception(cm.bin.overlap)
@@ -78,7 +82,6 @@ def set_byte_map(class_list, raster, process, output):
                 data_value = (bool_data * (index + 1)).astype(np.uint8)
                 data = data + data_value
 
-
 
             data = data.astype(out_meta['dtype'])
 
@@ -108,4 +111,52 @@ def unique(raster):
         features = np.where(count!=0)[0]
         features = features.tolist()
 
-    return features
+    return features
+
+def reclassify_from_map(in_raster, map_values, dst_raster=None, overwrite=False):
+    """ Remap raster values from map_values dictionary. If the 
+    are missing values in the dictionary 0 value will be returned
+    
+    Args:
+        in_raster (path to raster): Input raster to reclassify
+        map_values (dict): Dictionary with origin:target values
+    """
+
+    # Get reclassify path raster
+    filename = Path(in_raster).stem
+    dst_raster = Path('~').expanduser()/f'downloads/{filename}_reclassified.tif' if not dst_raster else dst_raster
+
+    if not overwrite:
+        if dst_raster.is_file():
+            raise Warning(cm.bin.reclassify_exist.format(dst_raster))
+        else:
+            raise Exception(cm.bin.no_exists.format(dst_raster))
+
+    if not all(list(map_values.values())):
+        raise Exception('All new values has to be filled, try it again.')
+
+    # Cast to integer map_values
+
+    map_values = {k: int(v) for k, v in map_values.items()}
+
+    with rio.open(in_raster) as src:
+
+        raw_data = src.read()
+        profile = src.profile
+        profile.update(compress='lzw', dtype=np.uint8)
+
+        data = np.zeros_like(raw_data, dtype=np.uint8)
+
+        for origin, target in map_values.items():
+
+            bool_data = np.zeros_like(raw_data, dtype=np.bool_)
+            bool_data = bool_data + (raw_data == origin)
+
+            data_value = (bool_data * target).astype(np.uint8)
+
+            data += data_value
+
+            with rio.open(dst_raster, 'w', **profile) as dst:
+                dst.write(data)
+
+    return data

component/tile/reclassify/__init__.py

@@ -0,0 +1,2 @@
+from .reclassify_tile import *
+from .datatable import *