basic firebrand recognition

Co-authored-by: Alex <AlexanderMcDowell@users.noreply.github.com>

Pipfile

@@ -14,6 +14,7 @@ matplotlib = "*"
 plotly = "*"
 pandas = "*"
 scipy = "==1.8.1"
+scikit-image = "*"
 
 [dev-packages]
 

Pipfile.lock

@@ -1,7 +1,7 @@
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         "requires": {
@@ -131,6 +131,14 @@
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@@ -348,6 +356,14 @@
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@@ -565,6 +581,37 @@
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@@ -611,6 +658,67 @@
             "index": "pypi",
             "version": "==6.0"
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         "setuptools": {
             "hashes": [
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@@ -635,6 +743,14 @@
             "markers": "python_version >= '3.6'",
             "version": "==8.1.0"
         },
+        "tifffile": {
+            "hashes": [
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+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==2022.10.10"
+        },
         "werkzeug": {
             "hashes": [
                 "sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f",

README.md

@@ -1,4 +1,4 @@
-# Pyrometry image processing
+# Fire Lab Work
 
 ## Interface Screenshots
 
@@ -6,13 +6,26 @@
 | --- | --- | --- |
 | ![](screenshots/pyro_input.png) | ![](screenshots/pyro_results.png) | ![](screenshots/temp_dist_plot.png) 
 
+| Labeling Input | Labeling Results | 
+| --- | --- |
+| ![](screenshots/projected_area_in.png) | ![](screenshots/projected_area_out.png) |
+
 ## Using the web version 
 
+### Ratio Pyrometry
+
 1. Go to [pyro.turtlebasket.ml](https://pyro.turtlebasket.ml).
 2. Select an input image.
 3. Enter your DSLR camera settings.
 4. Click "Generate Heatmap".
 
+### Projected Object Area
+
+1. Go to [pyro.turtlebasket.ml](https://pyro.turtlebasket.ml).
+2. Navigate to "Object Area".
+3. Select an input image.
+4. Click "Generate Projected Sizes".
+
 ## Using the local (batch) version
 
 Create a new config file:

examples/projected-area/projected_area.py

@@ -0,0 +1,80 @@
+import cv2 as cv
+import numpy as np
+from skimage import measure, morphology, color, segmentation
+import matplotlib.pyplot as plt
+
+file = 'proj-area-3.jpg'
+
+original = cv.imread(file)
+original = cv.cvtColor(original, cv.COLOR_BGR2RGB)
+img = original
+
+img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+
+retval, thresh_gray = cv.threshold(img, 200, 255, cv.THRESH_BINARY)
+
+def remove_dirt(image):
+    image = morphology.area_closing(image, area_threshold=250, connectivity=1)
+    # image = morphology.opening(image, morphology.square(5))
+
+    return image
+
+def calculate_area(countour):
+    c = np.expand_dims(countour.astype(np.float32), 1)
+    c = cv.UMat(c)
+    
+    return cv.contourArea(c)
+
+def center_of_mass(X):
+    x = X[:,0]
+    y = X[:,1]
+    g = (x[:-1]*y[1:] - x[1:]*y[:-1])
+    A = 0.5*g.sum()
+    cx = ((x[:-1] + x[1:])*g).sum()
+    cy = ((y[:-1] + y[1:])*g).sum()
+
+    return 1./(6*A)*np.array([cx,cy])
+
+img = remove_dirt(thresh_gray)
+
+
+# alpha = 1 # Contrast control (1.0-3.0)
+# beta = 1 # Brightness control (0-100)
+
+# img = cv.convertScaleAbs(img, alpha=alpha, beta=beta)
+
+# img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+
+# img = cv.medianBlur(img, 5)
+
+# retval, thresh_gray = cv.threshold(img, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
+
+
+# img = cv.medianBlur(img, 5)
+
+# img = cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 51, 15)
+
+contours = measure.find_contours(array=img, level=100)
+
+fig, ax = plt.subplots()
+ax.imshow(img, cmap=plt.cm.gray, alpha=0)
+
+index = 1
+
+for contour in contours:
+    if calculate_area(contour) > 300:
+        ax.plot(contour[:, 1], contour[:, 0], linewidth=0.5, color='orangered')
+
+        cX, cY = center_of_mass(contour)
+
+        plt.text(cY, cX, index, color='black', fontsize=6)
+
+        index += 1
+
+ax.axis('image')
+ax.set_xticks([])
+ax.set_yticks([])
+
+plt.savefig('output.png', dpi=300)
+
+# cv.imwrite('proj-area-1-processed.jpg', remove_dirt(img))

examples/pyrometry/edge_detection.py

@@ -2,26 +2,77 @@
 
 import cv2 as cv
 import numpy as np
+from skimage import measure, morphology, color, segmentation
+import matplotlib.pyplot as plt
 
-# edge-detection kernel amplification
-AMPLIFIER=9
-
-MIN_INTENSITY=100
-
-# file = '01-0001-cropped.png'
-file = 'streaktest.png'
-file_name = file.split(".")[0]
-file_ext = file.split(".")[1]
-
+file = 'streaktest2.png'
 img = cv.imread(file)
 
-img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+# blurred = cv.GaussianBlur(img, (8, 8), 0)
 
-kernel = np.array([
-    [-1, -1, -1],
-    [-1, AMPLIFIER, -1],
-    [-1, -1, -1],
-])
-img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)
+retval, thresh_gray = cv.threshold(img, 120, 255, cv.THRESH_BINARY)
 
-cv.imwrite(f'{file_name}-edge-detection.{file_ext}', img)
+kernel = np.ones((7, 7), np.uint8)
+image = cv.morphologyEx(thresh_gray, cv.MORPH_CLOSE, kernel, iterations=1)
+gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+
+retval, gray = cv.threshold(gray, 0, 255, cv.THRESH_BINARY)
+
+gray = cv.copyMakeBorder(
+    gray, 
+    20, 
+    20, 
+    20, 
+    20, 
+    cv.BORDER_CONSTANT, 
+    value=0
+)
+
+# cv.imshow('gray', gray)
+# cv.waitKey(0)
+
+# contours = measure.find_contours(array=gray, level=100)
+_img, contours = cv.findContours(gray, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)[0]
+
+fig, ax = plt.subplots()
+ax.imshow(gray, cmap=plt.cm.gray, alpha=1)
+
+def calculate_area(countour):
+    c = np.expand_dims(countour.astype(np.float32), 1)
+    c = cv.UMat(c)
+
+    return cv.contourArea(c)
+
+def center_of_mass(X):
+    x = X[:,0]
+    y = X[:,1]
+    g = (x[:-1]*y[1:] - x[1:]*y[:-1])
+    A = 0.5*g.sum()
+    cx = ((x[:-1] + x[1:])*g).sum()
+    cy = ((y[:-1] + y[1:])*g).sum()
+
+    return 1./(6*A)*np.array([cx,cy])
+
+
+img_new = cv.cvtColor(gray, cv.COLOR_GRAY2BGR)
+
+for contour in contours:
+    area = calculate_area(contour)
+
+    # if area > 250:
+        # cnt = np.array(contour).reshape((-1, 1, 2)).astype(np.int32)
+        # cv.drawContours(img_new, [cnt], -1, (0, 200, 255), thickness=10)
+
+    cv.drawContours(img_new, [contour], -1, (0, 200, 255), thickness=3)
+
+        # ax.plot(contour[:, 1], contour[:, 0], linewidth=0.5, color='orangered')
+
+# cv.imshow('contours', img_new)
+# cv.waitKey(0)
+
+cv.imwrite("firebrand_contours_opencv.png", img_new)
+
+ax.axis('image')
+ax.set_xticks([])
+ax.set_yticks([])
+plt.savefig("edge_detection_figure.png", dpi=500)

flask_frontend.py

@@ -1,10 +1,10 @@
-from flask import Flask, render_template, request
+from flask import Flask, render_template, request, send_file
 import numpy as np
+from plotly_util import generate_plotly_temperature_pdf
 from ratio_pyrometry import ratio_pyrometry_pipeline
+from size_projection import get_projected_area
 import base64
 import cv2 as cv
-import plotly.figure_factory as ff
-from scipy import stats
 
 app = Flask(
     __name__, 
@@ -14,13 +14,13 @@ app = Flask(
 
 @app.route('/', methods=['GET'])
 def index():
-    return render_template('index.jinja2')
+    return render_template('index.html')
 
 @app.route('/ratio_pyro', methods=['POST'])
 def ratio_pyro():
     f = request.files['file']
     f_bytes = np.fromstring(f.read(), np.uint8)
-    img_orig, img_res, key, ptemps = ratio_pyrometry_pipeline(
+    img_orig, img_res, key, ptemps, indiv_firebrands = ratio_pyrometry_pipeline(
         f_bytes,
         ISO=float(request.form['iso']),
         I_Darkcurrent=float(request.form['i_darkcurrent']),
@@ -30,37 +30,64 @@ def ratio_pyro():
         MIN_TEMP=float(request.form['min_temp']),
         smoothing_radius=int(request.form['smoothing_radius']),
         key_entries=int(request.form['legend_entries']),
-        eqn_scaling_factor=float(request.form['equation_scaling_factor'])
+        eqn_scaling_factor=float(request.form['equation_scaling_factor']),
+        firebrand_min_intensity_threshold=float(request.form['intensity_threshold']),
+        firebrand_min_area=float(request.form['min_area']),
     )
 
     # get base64 encoded images
     img_orig_b64 = base64.b64encode(cv.imencode('.png', img_orig)[1]).decode(encoding='utf-8')
     img_res_b64 = base64.b64encode(cv.imencode('.png', img_res)[1]).decode(encoding='utf-8')
 
-    # generate prob. distribution histogram & return embed
-    fig = ff.create_distplot(
-        [ptemps], 
-        group_labels=[f.filename], 
-        show_rug=False,
-        show_hist=False,
-    )
-    fig.update_layout(
-        autosize=False,
-        width=800,
-        height=600,
-    )
-    fig.update_xaxes(
-        title_text="Temperature (°C)",
-    )
-    fig.update_yaxes(
-        title_text="Probability (1/°C)",
-    )
-    freq_plot = fig.to_html()
+    ptemps_list = [ptemps]
+
+    for i in range(len(indiv_firebrands)):
+        # base64 encode image data
+        brand_data = indiv_firebrands[i]
+        unencoded = brand_data["img_data"]
+        brand_data["img_data"] = base64.b64encode(cv.imencode('.png', unencoded)[1]).decode(encoding='utf-8')
+        indiv_firebrands[i] = brand_data
+        
+        # add ptemp data to list
+        ptemps_list.append(brand_data["ptemps"])
+
+    freq_plot, csvstrs = generate_plotly_temperature_pdf(ptemps_list)
 
     return render_template(
-        'results.jinja2',
+        'pyrometry-results.html',
         img_orig_b64=img_orig_b64,
         img_res_b64=img_res_b64,
         legend=key,
-        freq_plot=freq_plot
+        freq_plot=freq_plot,
+        csv_data=csvstrs[0],
+        individual_firebrands=indiv_firebrands,
     )
+
+
+@app.route('/projected_area')
+def projected_area():
+    return render_template('projected-area.html')
+
+
+@app.route('/projected_area_results', methods=['POST'])
+def projected_area_results():
+    f = request.files['file']
+    f_bytes = np.fromstring(f.read(), np.uint8)
+
+    img, dtable = get_projected_area(
+        f_bytes,
+        int(request.form['area_threshold']),
+        int(request.form['min_display_threshold']),
+        float(request.form['paper_width']),
+        float(request.form['paper_width'])
+    )
+
+    return render_template(
+        'projected-area-results.html',
+        img_b64=img,
+        dtable=dtable
+    )
+
+# @app.route("/download_pyrometry_temps")
+# def download_pyrometry_temps():
+#     return send_file()

plotly_util.py

@@ -0,0 +1,64 @@
+from typing import List
+import plotly.figure_factory as ff
+import pandas as pd
+
+def generate_plotly_temperature_pdf(ptemps_list: List[list]):
+    """
+    Generate plotly graph HTML & raw CSV data for temperature pdf
+
+    ptemps: pixel temperature LIST in order of:
+
+    - Ptemps of firebrands "overview" image
+    - Ptemps list for each individual firebrand
+
+    plotname: what to call the plot
+
+    Returns result in form (plot_html, csv_data)
+    """
+
+    # generate prob. distribution histogram & return embed
+    labels = ["Full Image"]
+    for i in range(len(ptemps_list[1:])):
+        labels.append(f"Firebrand {i+1}")
+    labels.reverse()
+
+    fig = ff.create_distplot(
+        ptemps_list,
+        group_labels=labels, 
+        show_rug=False,
+        show_hist=False,
+    )
+    fig.update_layout(
+        autosize=False,
+        width=800,
+        height=600,
+    )
+    fig.update_xaxes(
+        title_text="Temperature (°C)",
+    )
+    fig.update_yaxes(
+        title_text="Probability (1/°C)",
+    )
+    freq_plot = fig.to_html()
+
+    # create csv-formatted stuff
+    csvstrs = []
+    plot_data=fig.to_dict()
+    for i in range(len(plot_data["data"])):
+        x_data = plot_data["data"][i]["x"]
+        y_data = plot_data["data"][i]["y"]
+
+        tdata = [["Temperature", "Frequency"]]
+        for i in range(len(x_data)):
+            r = []
+            r.append(x_data[i])
+            r.append(y_data[i])
+            tdata.append(r)
+
+        csvstr = pd.DataFrame(tdata).to_csv(index=False, header=False)
+        csvstrs.append(csvstr)
+
+    return (
+        freq_plot,
+        csvstrs
+    )

ratio_pyrometry.py

@@ -3,6 +3,7 @@ from multiprocessing.sharedctypes import Value
 import cv2 as cv
 import numpy as np
 from numba import jit
+from skimage import measure
 
 @jit(nopython=True)
 def rg_ratio_normalize(
@@ -85,11 +86,104 @@ def ratio_pyrometry_pipeline(
     smoothing_radius: int,
     key_entries: int,
     eqn_scaling_factor: float,
+    # firebrand detection
+    firebrand_min_intensity_threshold: float,
+    firebrand_min_area: float
 ):
-
     # read image & crop
     img_orig = cv.imdecode(file_bytes, cv.IMREAD_UNCHANGED)
 
+    # ---------------------------------------------------------
+    # -- Firebrand detection
+    # ---------------------------------------------------------
+
+    img = cv.copyMakeBorder(
+        img_orig, 
+        20, 
+        20, 
+        20, 
+        20, 
+        cv.BORDER_CONSTANT, 
+        value=0
+    )
+
+    retval, thresh_gray = cv.threshold(img, firebrand_min_intensity_threshold, 255, cv.THRESH_BINARY)
+
+    kernel = np.ones((7, 7), np.uint8)
+    image = cv.morphologyEx(thresh_gray, cv.MORPH_CLOSE, kernel, iterations=1)
+    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
+
+    retval, gray = cv.threshold(gray, 0, 255, cv.THRESH_BINARY)
+
+    contours = measure.find_contours(array=gray, level=100)
+
+    def calculate_area(countour):
+        c = np.expand_dims(countour.astype(np.float32), 1)
+        c = cv.UMat(c)
+        
+        return cv.contourArea(c)
+
+    individual_firebrands = []
+
+    for contour in contours:
+        if calculate_area(contour) > firebrand_min_area:
+            mask = np.zeros(img.shape[0:2], dtype='uint8')
+            cv.fillPoly(mask, pts=np.int32([np.flip(contour, 1)]), color=(255,255,255))
+
+            retval, mask = cv.threshold(mask, 0, 255, cv.THRESH_BINARY)
+
+            #apply the mask to the img
+            masked = cv.bitwise_and(img, img, mask=mask)
+
+            masked_ratio_rg, ptemps_indiv = rg_ratio_normalize(
+                masked,
+                I_Darkcurrent,
+                f_stop,
+                exposure_time,
+                ISO,
+                MIN_TEMP,
+                MAX_TEMP,
+                eqn_scaling_factor,
+            )
+
+            # build & apply smoothing conv kernel
+            k = []
+            for i in range(smoothing_radius):
+                k.append([1/(smoothing_radius**2) for i in range(smoothing_radius)])
+            kernel = np.array(k)
+
+            masked_ratio_rg = cv.filter2D(src=masked_ratio_rg, ddepth=-1, kernel=kernel)
+
+            # write colormapped image
+            masked_ratio_rg_jet = cv.applyColorMap(masked_ratio_rg, cv.COLORMAP_JET)
+
+            # Generate key
+            step = (MAX_TEMP - MIN_TEMP) / (key_entries-1)
+            temps = []
+            key_img_arr = [[]]
+
+            for i in range(key_entries):
+                res_temp = MIN_TEMP + (i * step)
+                res_color = scale_temp(res_temp, MIN_TEMP, MAX_TEMP)
+                temps.append(math.floor(res_temp))
+                key_img_arr[0].append([res_color, res_color, res_color])
+
+            key_img = np.array(key_img_arr).astype(np.uint8)
+            key_img_jet = cv.applyColorMap(key_img, cv.COLORMAP_JET)
+
+            tempkey = {}
+
+            for i in range(len(temps)):
+                c = key_img_jet[0][i]
+                tempkey[temps[i]] = f"rgb({c[2]}, {c[1]}, {c[0]})"
+
+            individual_firebrands.append({
+                "img_data": masked_ratio_rg_jet,
+                "legend": tempkey,
+                "ptemps": ptemps_indiv
+            })
+
+
     img, ptemps = rg_ratio_normalize(
         img_orig,
         I_Darkcurrent,
@@ -112,7 +206,9 @@ def ratio_pyrometry_pipeline(
     # write colormapped image
     img_jet = cv.applyColorMap(img, cv.COLORMAP_JET)
 
-    # --- Generate temperature key ---
+    # ---------------------------------------------------------
+    # -- Generate temperature key
+    # ---------------------------------------------------------
 
     # Generate key
     step = (MAX_TEMP - MIN_TEMP) / (key_entries-1)
@@ -133,4 +229,4 @@ def ratio_pyrometry_pipeline(
         tempkey[temps[i]] = f"rgb({c[2]}, {c[1]}, {c[0]})"
 
     # original, transformed, legend
-    return img_orig, img_jet, tempkey, ptemps
+    return img_orig, img_jet, tempkey, ptemps, individual_firebrands

size_projection.py

@@ -0,0 +1,75 @@
+# use headless backend
+import matplotlib
+matplotlib.use("Agg")
+
+import base64
+import cv2 as cv
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage import measure, morphology, color, segmentation
+import io
+
+def get_projected_area(image, area_threshold, display_threshold, paper_width, paper_height):
+    total_px = image.size
+    total_mm = paper_width * paper_height * 25.4
+
+    output = []
+    original = cv.imdecode(image, cv.IMREAD_UNCHANGED)
+    original = cv.cvtColor(original, cv.COLOR_BGR2RGB)
+
+    img = cv.cvtColor(original, cv.COLOR_BGR2GRAY)
+    _retval, thresh_gray = cv.threshold(img, 200, 255, cv.THRESH_BINARY)
+
+    img = morphology.area_closing(thresh_gray, area_threshold=area_threshold, connectivity=1)
+
+    contours = measure.find_contours(image=img, level=100)
+
+    fig, ax = plt.subplots()
+    ax.imshow(original, cmap=plt.cm.gray, alpha=0.3)
+
+    index = 1
+
+    for contour in contours:
+        area = calculate_area(contour)
+        
+        if calculate_area(contour) > display_threshold:
+            ax.plot(contour[:, 1], contour[:, 0], linewidth=0.5, color='orangered')
+
+            cX, cY = center_of_mass(contour)
+            plt.text(cY, cX, index, color='black', fontsize=6)
+            
+            output.append((index, round(area / total_px * total_mm, 2)))
+
+            # print(area, total_px)
+
+
+            index += 1
+
+    ax.axis('image')
+    ax.set_xticks([])
+    ax.set_yticks([])
+    
+    ax.margins(0)
+
+    my_stringIObytes = io.BytesIO()
+    plt.savefig(my_stringIObytes, format='png', dpi=500, bbox_inches='tight')
+    my_stringIObytes.seek(0)
+    image_arr = base64.b64encode(my_stringIObytes.read()).decode(encoding='utf-8')
+
+    return image_arr, output
+
+def calculate_area(countour):
+    c = np.expand_dims(countour.astype(np.float32), 1)
+    c = cv.UMat(c)
+    
+    return cv.contourArea(c)
+
+def center_of_mass(X):
+    x = X[:,0]
+    y = X[:,1]
+    g = (x[:-1]*y[1:] - x[1:]*y[:-1])
+    A = 0.5*g.sum()
+    cx = ((x[:-1] + x[1:])*g).sum()
+    cy = ((y[:-1] + y[1:])*g).sum()
+
+    return 1./(6*A)*np.array([cx,cy])

static/app.css

@@ -0,0 +1,77 @@
+* {
+    font-family: sans-serif;
+}
+
+html {
+    margin: 0px;
+    padding: 0px;
+}
+
+.navbar {
+    display: flex;
+    flex-direction: row;
+    align-items: center;
+    padding: 0px 2rem;
+    background-color: #e0e0e0;
+    border-radius: 0.8rem;
+}
+
+.navbar-links {
+    display: flex;
+    flex-direction: row;
+    align-items: center;
+    margin-left: auto;
+}
+
+.navbar-link {
+    display: flex;
+    margin-left: 3rem;
+}
+
+.content {
+    display: flex;
+    margin: 0rem 1rem;
+}
+
+.form {
+    display: flex;
+    flex-direction: column;
+}
+
+#img-preview {
+    width: 18rem;
+}
+
+#pyro-eqn {
+    width: 800px;
+}
+
+.img-table-heading {
+    padding: 4px 10px;
+}
+
+.img-table-cell {
+    padding: 10px 20px;
+}
+
+.img-out {
+    width: 32rem;
+}
+
+.image-out-pa {
+    width: 60rem;
+}
+
+.legend {
+    border-spacing: 0px;
+    border-collapse: collapse;
+}
+
+.legend-heading {
+    padding: 4px;
+}
+
+.legend-cell {
+    border-width: 0px;
+    padding: 0px 20px;
+}

static/js/csv_download.js

@@ -0,0 +1,28 @@
+function saveData(csvStr, filename) {
+    // string rep
+    // var csvStr = "";
+    // for (let r = 0; r < csvData.length; r++) {
+    //     let row = csvData[r]
+    //     for (let c = 0; c < row.length; c++) {
+    //         let item = row[c]
+    //         csvStr += item;
+    //         if (c < row.length - 1)
+    //             csvStr += ",";
+    //     }
+    //     if (r < csvStr.length - 1)
+    //         csvStr += "\r\n";
+    // }
+
+    // define data blob
+    var data = new Blob([csvStr]);
+
+    // create & click temp link
+    // slightly modded https://stackoverflow.com/a/15832662
+    var link = document.createElement("a");
+    link.download = filename;
+    link.href = URL.createObjectURL(data);
+    document.body.appendChild(link);
+    link.click();
+    document.body.removeChild(link);
+    delete link;
+}

static/js/img_preview.js

@@ -0,0 +1,9 @@
+let imgPreview = document.getElementById('img-preview');
+let imgUpload = document.getElementById('img-upload');
+imgUpload.onchange = event => {
+    const [file] = imgUpload.files;
+    if (file) {
+        console.log(file)
+        imgPreview.src = URL.createObjectURL(file);
+    }
+};

templates/app.css

@@ -1,4 +0,0 @@
-.form {
-    display: flex;
-    flex-direction: column;
-}

templates/base.html

@@ -0,0 +1,22 @@
+<!DOCTYPE html>
+<head>
+    <title>Pyrometry Application</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='app.css') }}">
+    {% block head %}
+    {% endblock %}
+</head>
+<body>
+    <div class="navbar">
+        <h3>Pyrometry Toolkit</h3>
+        <div class="navbar-links">
+            <a class="navbar-link" href="/">Ratio Pyrometry</a>
+            <a class="navbar-link" href="/projected_area">Projected Area</a>
+        </div>
+    </div>
+    <br>
+    <div class="content">
+    {% block content required %}
+    {% endblock %}
+    </div>
+    <br><br><br>
+</body>

templates/base.jinja2

@@ -1,15 +0,0 @@
-<!DOCTYPE html>
-<head>
-    <title>Pyrometry Application</title>
-    <link rel="app.css">
-</head>
-<body>
-    {% block content required %}
-    {% endblock %}
-</body>
-
-<style>
-* {
-    font-family: sans-serif;
-}
-</style>

templates/index.html

@@ -1,7 +1,11 @@
-{% extends "base.jinja2" %}
+{% extends "base.html" %}
+
+{% block head %}
+{% endblock %}
+
 {% block content %} 
 <form action="/ratio_pyro" method="POST" enctype="multipart/form-data">
-    <h2>Simple Ratio Pyrometry Interface</h2>
+    <h2>Ratio Pyrometry Interface</h2>
 
     <img src="#" id="img-preview" type="file"/>
     <br>
@@ -44,6 +48,16 @@
     <input type="number" name="equation_scaling_factor" value="0.55" step="0.001"/>
     <br>
 
+    <h4>Firebrand Detection Settings</h4>
+
+    <label for="intensity_threshold">intensity threshold (0-255)</label>
+    <input type="number" name="intensity_threshold" value="250", step="0.1"/>
+    <br>
+
+    <label for="min_area">min area (removes small particles)</label>
+    <input type="number" name="min_area" value="115", step="0.1"/>
+    <br>
+
     <h4>Output Settings</h4>
 
     <label for="smoothing_radius">Smoothing Radius (px)</label>
@@ -58,25 +72,7 @@
     <input type="submit" value="Generate Heatmap"/>
 </form>
 
-<script>
-let imgPreview = document.getElementById('img-preview');
-let imgUpload = document.getElementById('img-upload');
-imgUpload.onchange = event => {
-    const [file] = imgUpload.files;
-    if (file) {
-        console.log(file)
-        imgPreview.src = URL.createObjectURL(file);
-    }
-};
-</script>
-
-<style>
-#img-preview {
-    width: 18rem;
-}
-#pyro-eqn {
-    width: 800px;
-}
-</style>
+<!-- Image Preview -->
+<script src="/s/js/img_preview.js"></script>
 
 {% endblock %} 

templates/projected-area-results.html

@@ -0,0 +1,30 @@
+{% extends "base.html" %}
+{% block content %} 
+
+<h2>Results</h2>
+<a href="/projected_area">Process another image</a>
+
+<div style="display: flex; align-items: start;">
+    <img class="image-out-pa" src="data:image/png;base64,{{ img_b64 }}" alt="original image">
+
+    <div style="overflow-y: scroll;">
+    <table style="background: #f1f1f1">
+        <tr>
+            <th class="legend-heading">Index</th>
+            <th class="legend-heading">Area (mm²)</th>
+            <th class="legend-heading">Copy</th>
+        </tr>
+        {% for item in dtable %}
+        <tr>
+            <td class="legend-cell">{{ item.0 }}</td>
+            <td class="legend-cell">{{ item.1 }}</td>
+            <td>
+                <button onclick="() => navigator.clipboard.writeText('{{ item.1 }}')">Copy</button>
+            </td>
+        </tr>
+        {% endfor %}
+    </table>
+    </div>
+</div>
+
+{% endblock %}

templates/projected-area.html

@@ -0,0 +1,38 @@
+{% extends "base.html" %}
+{% block content %} 
+
+<form action="/projected_area_results" method="POST" enctype="multipart/form-data">
+    <h2>Projected Area Interface</h2>
+
+    <img src="#" id="img-preview" type="file"/>
+    <br>
+
+    <input id="img-upload" type="file" name="file" accept=".png,.jpg,.jpeg,.tiff" value="Choose Image"/>
+
+    <h4>Settings</h4>
+
+    <label for="area_threshold">Area threshold (to remove dust particles) in px</label>
+    <input type="number" name="area_threshold" value="250"/>
+    <br>
+    <br>
+
+    <label for="min_display_threshold">Minimum display threshold (in px)</label>
+    <input type="number" name="min_display_threshold" value="300"/>
+    <br>
+    <br>
+
+    <label for="min_display_threshold">Page size</label>
+    <input type="number" name="paper_width" value="8.5"/>
+    X
+    <input type="number" name="paper_height" value="11"/> inches
+    <br>
+    <br>
+
+    <br>
+    <input type="submit" value="Generate Projected Sizes"/>
+</form>
+
+<!-- Image Preview -->
+<script src="/s/js/img_preview.js"></script>
+
+{% endblock %}

templates/pyrometry-results.html

@@ -0,0 +1,99 @@
+{% extends "base.html" %}
+
+{% block head %}
+<script src="/s/js/csv_download.js"></script>
+{% endblock %}
+
+{% block content %} 
+
+<div style="display:flex; flex-direction: column;">
+
+<h2>General Results</h2>
+
+<table class="img-table">
+    <tr>
+        <th class="img-table-heading">Input Image</th>
+        <th class="img-table-heading">Output Heatmap</th>
+        <th class="img-table-heading">Legend</th>
+    </tr>
+    <tr>
+        {# Original image #}
+        <td class="img-table-cell" id="orig-cell">
+            <img class="img-out" src="data:image/png;base64,{{ img_orig_b64 }}" alt="original image">
+        </td>
+        {# Result image #}
+        <td class="img-table-cell" id="res-cell">
+            <img class="img-out" src="data:image/png;base64,{{ img_res_b64 }}" alt="result image">
+        </td>
+        {# Legend #}
+        <td>
+            <table class="legend" id="legend">
+                <tr>
+                    <th class="legend-heading">Color</th>
+                    <th class="legend-heading">Temperature</th>
+                </tr>
+                {% for temp, color in legend.items() %}
+                <tr>
+                    <td class="legend-cell"><div style="width:30px;height:20px;background-color:{{ color }};"></div></td>
+                    <td class="legend-cell">{{ temp }}°C</td>
+                </tr>
+                {% endfor %}
+            </table>
+        </td>
+    </tr>
+</table>
+
+
+<h2>Individual Firebrands</h2>
+
+<table>
+    <tr>
+        <th>Output Heatmap</th>
+        <th>Legend</th>
+    </tr>
+
+    {% for item in individual_firebrands %}
+    <tr>
+        {# output heatmap #}
+        <td>
+            <img class="img-out" src="data:image/png;base64,{{ item['img_data'] }}" alt="result image">
+        </td>
+
+        </td>
+        {# legend #}
+        <td>
+            <h3>Firebrand {{ loop.index }}</h3>
+            <table class="legend" id="legend">
+                <tr>
+                    <th class="legend-heading">Color</th>
+                    <th class="legend-heading">Temperature</th>
+                </tr>
+                {% for temp, color in item["legend"].items() %}
+                <tr>
+                    <td class="legend-cell"><div style="width:30px;height:20px;background-color:{{ color }};"></div></td>
+                    <td class="legend-cell">{{ temp }}°C</td>
+                </tr>
+                {% endfor %}
+            </table>
+        </td>
+    </tr>
+    {% endfor %}
+
+</table>
+
+<br>
+{# Temperature Frequency Plot #}
+<div style="display: flex; flex-direction: row; align-items: center;">
+<strong>Temperature Distribution</strong>
+<button 
+style="width: 10rem; height: 2rem; margin-left: 1rem;" 
+onclick="saveData(`{{csv_data}}`, 'temperature-data.csv')">Download Data as CSV</button>
+</div>
+{{ freq_plot | safe }}
+
+
+<!-- Firebrands: {{ individual_firebrands }} -->
+
+</div>
+
+{% endblock %}

templates/results.jinja2

@@ -1,70 +0,0 @@
-{% extends "base.jinja2" %}
-{% block content %} 
-
-<table class="img-table">
-    <tr>
-        <th class="img-table-heading">Input Image</th>
-        <th class="img-table-heading">Output Heatmap</th>
-    </tr>
-    <tr>
-        {# Original image #}
-        <td class="img-table-cell" id="orig-cell">
-            <img class="img-out" src="data:image/png;base64,{{ img_orig_b64 }}" alt="original image">
-        </td>
-        {# Result image #}
-        <td class="img-table-cell" id="res-cell">
-            <img class="img-out" src="data:image/png;base64,{{ img_res_b64 }}" alt="result image">
-        </td>
-    </tr>
-    {# Legend #}
-    <tr>
-        <td class="img-table-cell"></td>
-        <td class="img-table-cell">
-            <table class="legend" id="legend">
-                <tr>
-                    <th class="legend-heading">Color</th>
-                    <th class="legend-heading">Temperature</th>
-                </tr>
-                {% for temp, color in legend.items() %}
-                <tr>
-                    <td class="legend-cell"><div style="width:30px;height:20px;background-color:{{ color }};"></div></td>
-                    <td class="legend-cell">{{ temp }}°C</td>
-                </tr>
-                {% endfor %}
-            </table>
-        </td>
-    </tr>
-</table>
-{# Temperature Frequency Plot #}
-<strong>Temperature Distribution</strong>
-{{freq_plot}}
-
-<style>
-    .img-table-heading {
-        padding: 4px 10px;
-    }
-
-    .img-table-cell {
-        padding: 10px 20px;
-    }
-
-    .img-out {
-        width: 32rem;
-    }
-
-    .legend {
-        border-spacing: 0px;
-        border-collapse: collapse;
-    }
-
-    .legend-heading {
-        padding: 4px;
-    }
-
-    .legend-cell {
-        border-width: 0px;
-        padding: 0px 20px;
-    }
-</style>
-
-{% endblock %}