basic firebrand recognition

Co-authored-by: Alex <AlexanderMcDowell@users.noreply.github.com>
firebrand edge detection demo
2022-11-14 22:47:43 -08:00 · 2022-11-13 17:57:41 -08:00 · 2022-11-11 19:39:19 -08:00 · 2022-11-04 18:58:35 -07:00 · 2022-11-04 18:42:22 -07:00 · 2022-11-04 17:55:25 -07:00
60 changed files with 1346 additions and 195 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,10 @@
 # CUSTOM

+images-input/*
+!images-input/.gitkeep
+images-output/*
+!images-output/.gitkeep
+config.yaml
 .vscode/
 *.swp

--- a/4
+++ b/4
@@ -10,3 +10,7 @@ dev:
 clean:
 	rm -rf static/*
 	touch static/.gitkeep
+
+batch:
+	pipenv run python3 batch-process.py
+
--- a/5
+++ b/5
@@ -10,6 +10,11 @@ flask = "*"
 gunicorn = "*"
 werkzeug = "*"
 pyyaml = "*"
+matplotlib = "*"
+plotly = "*"
+pandas = "*"
+scipy = "==1.8.1"
+scikit-image = "*"

 [dev-packages]

--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
    "_meta": {
        "hash": {
-            "sha256": "0cfa12a983973f0699999da1f8adcf4662998d4d37158ae1eed984cb7b773fc3"
+            "sha256": "4ce52e44137325bfa984f7d78467dce4463d471bfc344ecd116c2beb2af52d60"
        },
        "pipfile-spec": 6,
        "requires": {
@@ -24,6 +24,89 @@
            "markers": "python_version >= '3.7'",
            "version": "==8.1.3"
        },
+        "contourpy": {
+            "hashes": [
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+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==1.0.5"
+        },
+        "cycler": {
+            "hashes": [
+                "sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3",
+                "sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==0.11.0"
+        },
        "flask": {
            "hashes": [
                "sha256:642c450d19c4ad482f96729bd2a8f6d32554aa1e231f4f6b4e7e5264b16cca2b",
@@ -32,6 +115,14 @@
            "index": "pypi",
            "version": "==2.2.2"
        },
+        "fonttools": {
+            "hashes": [
+                "sha256:2bb244009f9bf3fa100fc3ead6aeb99febe5985fa20afbfbaa2f8946c2fbdaf1",
+                "sha256:820466f43c8be8c3009aef8b87e785014133508f0de64ec469e4efb643ae54fb"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==4.38.0"
+        },
        "gunicorn": {
            "hashes": [
                "sha256:9dcc4547dbb1cb284accfb15ab5667a0e5d1881cc443e0677b4882a4067a807e",
@@ -40,6 +131,14 @@
            "index": "pypi",
            "version": "==20.1.0"
        },
+        "imageio": {
+            "hashes": [
+                "sha256:9bdafe9c5a3d336a187f3f554f3e30bcdbf8a1d7d46f0e4d94e4a535adfb64c7",
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+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==2.22.2"
+        },
        "itsdangerous": {
            "hashes": [
                "sha256:2c2349112351b88699d8d4b6b075022c0808887cb7ad10069318a8b0bc88db44",
@@ -56,6 +155,80 @@
            "markers": "python_version >= '3.7'",
            "version": "==3.1.2"
        },
+        "kiwisolver": {
+            "hashes": [
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+            "markers": "python_version >= '3.7'",
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        "llvmlite": {
            "hashes": [
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@@ -136,6 +309,61 @@
            "markers": "python_version >= '3.7'",
            "version": "==2.1.1"
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@@ -217,6 +445,173 @@
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+                "sha256:d0e56cd7a53aed3cceca91a04d62feb3a0aca6725b1912d29546c26f6ea90426",
+                "sha256:d854411eb5ee9cb4bc5d0e66e3634aeb8f594210f6a1bed96dbed57ec70f181c",
+                "sha256:da7b9c006171be1f9ddb12cc6e0d3d703b95f7f43cb5e2c6f5f15d3233fcf202",
+                "sha256:daf0aa79842b571308d7c31a9c43bc99a30b6328e6aea3f50388cd8f69ba7dbc",
+                "sha256:de7cd61a88a982edfec01ea755b0740e94766e00a1ceceeafef3ed4c85c605cd"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==1.4.1"
+        },
        "pyyaml": {
            "hashes": [
                "sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf",
@@ -263,6 +658,67 @@
            "index": "pypi",
            "version": "==6.0"
        },
+        "scikit-image": {
+            "hashes": [
+                "sha256:03779a7e1736fdf89d83c0ba67d44110496edd736a3bfce61a2b5177a1c8a099",
+                "sha256:0b0a199157ce8487c77de4fde0edc0b42d6d42818881c11f459262351d678b2d",
+                "sha256:19a21a101a20c587a3b611a2cf6f86c35aae9f8d9563279b987e83ee1c9a9790",
+                "sha256:24b5367de1762da6ee126dd8f30cc4e7efda474e0d7d70685433f0e3aa2ec450",
+                "sha256:2a02d1bd0e2b53e36b952bd5fd6118d9ccc3ee51de35705d63d8eb1f2e86adef",
+                "sha256:2f50b923f8099c1045fcde7418d86b206c87e333e43da980f41d8577b9605245",
+                "sha256:32fb88cc36203b99c9672fb972c9ef98635deaa5fc889fe969f3e11c44f22919",
+                "sha256:33dfd463ee6cc509defa279b963829f2230c9e0639ccd3931045be055878eea6",
+                "sha256:3a01372ae4bca223873304b0bff79b9d92446ac6d6177f73d89b45561e2d09d8",
+                "sha256:651de1c2ce1fbee834753b46b8e7d81cb12a5594898babba63ac82b30ddad49d",
+                "sha256:6b6a8f98f2ac9bb73706461fd1dec875f6a5141759ed526850a5a49e90003d19",
+                "sha256:7f9f8a1387afc6c70f2bed007c3854a2d7489f9f7713c242f16f32ee05934bc2",
+                "sha256:84baa3179f3ae983c3a5d81c1e404bc92dcf7daeb41bfe9369badcda3fb22b92",
+                "sha256:8d8917fcf85b987b1f287f823f3a1a7dac38b70aaca759bc0200f3bc292d5ced",
+                "sha256:9439e5294de3f18d6e82ec8eee2c46590231cf9c690da80545e83a0733b7a69e",
+                "sha256:9fb0923a3bfa99457c5e17888f27b3b8a83a3600b4fef317992e7b7234764732",
+                "sha256:a7c3985c68bfe05f7571167ee021d14f5b8d1a4a250c91f0b13be7fb07e6af34",
+                "sha256:a8714348ddd671f819457a797c97d4c672166f093def66d66c3254cbd1d43f83",
+                "sha256:ad5d8000207a264d1a55681a9276e6a739d3f05cf4429004ad00d61d1892235f",
+                "sha256:cc24177de3fdceca5d04807ad9c87d665f0bf01032ed94a9055cd1ed2b3f33e9",
+                "sha256:ce3d2207f253b8eb2c824e30d145a9f07a34a14212d57f3beca9f7e03c383cbe",
+                "sha256:cfbb073f23deb48e0e60c47f8741d8089121d89cc78629ea8c5b51096efc5be7",
+                "sha256:e207c6ce5ce121d7d9b9d2b61b9adca57d1abed112c902d8ffbfdc20fb42c12b",
+                "sha256:fd9dd3994bb6f9f7a35f228323f3c4dc44b3cf2ff15fd72d895216e9333550c6",
+                "sha256:fdf48d9b1f13af69e4e2c78e05067e322e9c8c97463c315cd0ecb47a94e259fc",
+                "sha256:ff3b1025356508d41f4fe48528e509d95f9e4015e90cf158cd58c56dc63e0ac5"
+            ],
+            "index": "pypi",
+            "version": "==0.19.3"
+        },
+        "scipy": {
+            "hashes": [
+                "sha256:02b567e722d62bddd4ac253dafb01ce7ed8742cf8031aea030a41414b86c1125",
+                "sha256:1166514aa3bbf04cb5941027c6e294a000bba0cf00f5cdac6c77f2dad479b434",
+                "sha256:1da52b45ce1a24a4a22db6c157c38b39885a990a566748fc904ec9f03ed8c6ba",
+                "sha256:23b22fbeef3807966ea42d8163322366dd89da9bebdc075da7034cee3a1441ca",
+                "sha256:28d2cab0c6ac5aa131cc5071a3a1d8e1366dad82288d9ec2ca44df78fb50e649",
+                "sha256:2ef0fbc8bcf102c1998c1f16f15befe7cffba90895d6e84861cd6c6a33fb54f6",
+                "sha256:3b69b90c9419884efeffaac2c38376d6ef566e6e730a231e15722b0ab58f0328",
+                "sha256:4b93ec6f4c3c4d041b26b5f179a6aab8f5045423117ae7a45ba9710301d7e462",
+                "sha256:4e53a55f6a4f22de01ffe1d2f016e30adedb67a699a310cdcac312806807ca81",
+                "sha256:6311e3ae9cc75f77c33076cb2794fb0606f14c8f1b1c9ff8ce6005ba2c283621",
+                "sha256:65b77f20202599c51eb2771d11a6b899b97989159b7975e9b5259594f1d35ef4",
+                "sha256:6cc6b33139eb63f30725d5f7fa175763dc2df6a8f38ddf8df971f7c345b652dc",
+                "sha256:70de2f11bf64ca9921fda018864c78af7147025e467ce9f4a11bc877266900a6",
+                "sha256:70ebc84134cf0c504ce6a5f12d6db92cb2a8a53a49437a6bb4edca0bc101f11c",
+                "sha256:83606129247e7610b58d0e1e93d2c5133959e9cf93555d3c27e536892f1ba1f2",
+                "sha256:93d07494a8900d55492401917a119948ed330b8c3f1d700e0b904a578f10ead4",
+                "sha256:9c4e3ae8a716c8b3151e16c05edb1daf4cb4d866caa385e861556aff41300c14",
+                "sha256:9dd4012ac599a1e7eb63c114d1eee1bcfc6dc75a29b589ff0ad0bb3d9412034f",
+                "sha256:9e3fb1b0e896f14a85aa9a28d5f755daaeeb54c897b746df7a55ccb02b340f33",
+                "sha256:a0aa8220b89b2e3748a2836fbfa116194378910f1a6e78e4675a095bcd2c762d",
+                "sha256:d3b3c8924252caaffc54d4a99f1360aeec001e61267595561089f8b5900821bb",
+                "sha256:e013aed00ed776d790be4cb32826adb72799c61e318676172495383ba4570aa4",
+                "sha256:f3e7a8867f307e3359cc0ed2c63b61a1e33a19080f92fe377bc7d49f646f2ec1"
+            ],
+            "index": "pypi",
+            "version": "==1.8.1"
+        },
        "setuptools": {
            "hashes": [
                "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17",
@@ -271,6 +727,30 @@
            "markers": "python_version >= '3.7'",
            "version": "==65.5.0"
        },
+        "six": {
+            "hashes": [
+                "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926",
+                "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==1.16.0"
+        },
+        "tenacity": {
+            "hashes": [
+                "sha256:35525cd47f82830069f0d6b73f7eb83bc5b73ee2fff0437952cedf98b27653ac",
+                "sha256:e48c437fdf9340f5666b92cd7990e96bc5fc955e1298baf4a907e3972067a445"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==8.1.0"
+        },
+        "tifffile": {
+            "hashes": [
+                "sha256:50b61ba943b866d191295bc38a00191c9fdab23ece063544c7f1a264e3f6aa8e",
+                "sha256:87f3aee8a0d06b74655269a105de75c1958a24653e1930d523eb516100043503"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==2022.10.10"
+        },
        "werkzeug": {
            "hashes": [
                "sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f",
--- a/README.md
+++ b/README.md
@@ -1,18 +1,31 @@
-# Pyrometry image processing
+# Fire Lab Work

 ## Interface Screenshots

-| Input View | Results | 
+| Input View | Results | Temp. Distribution | 
+| --- | --- | --- |
+| ![](screenshots/pyro_input.png) | ![](screenshots/pyro_results.png) | ![](screenshots/temp_dist_plot.png) 
+
+| Labeling Input | Labeling Results | 
 | --- | --- |
-| ![](screenshots/pyro_input.png) | ![](screenshots/pyro_results.png)
+| ![](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:
@@ -67,23 +80,3 @@ To autoreload on source file changes:
 ```
 gunicorn flask_frontend:app --reload
 ```
-
-## Temperature maps
-
-**Grayscale pyrometry:** currently basic; uses grayscale opencv import, then just applies a jet filter. Doesn't yet copy the full impl in the paper.
-
-**Ratio pyrometry:** pretty damn close to what's in the paper but it's very broken atm
-
-**Test image:**
-
-![](examples/01-0001-cropped.png)
-
-**Ratio pyrometry result (with convolutional smoothing):**
-
-According to general researcher consensus, ratio pyrometry is supposed to be more accurate.
-
-![](examples/01-0001-cropped-transformed-ratio.png)
-
-**Grayscale pyrometry result:**
-
-![](examples/01-0001-transformed-grayscale.png)
--- a/batch-process.py
+++ b/batch-process.py
@@ -2,14 +2,15 @@ import yaml
 import cv2 as cv
 import numpy as np
 import os
+from matplotlib import pyplot as plt, image as mpimg
 from ratio_pyrometry import rg_ratio_normalize

 config = {}
 with open("./config.yaml", "r") as yaml_stream:
    config = yaml.safe_load(yaml_stream)

-img_in_dir = "./images-input"
-img_out_dir = "./images-output"
+img_input_dir = "images-input"
+img_out_dir = "images-output"
 accepted_formats = [
    ".jpg",
    ".jpeg",
@@ -19,44 +20,64 @@ accepted_formats = [

 files = []

-for file in os.listdir(img_in_dir):
+for file in os.listdir(img_input_dir):
    filename = os.fsdecode(file)
    valid = False
    for fmt in accepted_formats:
        if filename.endswith(fmt):
-            files.append(os.path.join(img_in_dir, filename))
+            files.append(filename)
            valid = True
            break
-    if not valid:
+    if not valid and filename != ".gitkeep":
        print(f"Invalid file extension for {filename}.")
        exit
-    
+
 for filename in files:
-    with open(filename) as imgfile:
-        # read image & crop
-        img_orig = cv.imread(imgfile, cv.IMREAD_UNCHANGED)
+    # read image & crop
+    img_orig = cv.imread(f'{img_input_dir}/{filename}', cv.IMREAD_UNCHANGED)

-        img = rg_ratio_normalize(
-            img_orig,
-            config['i-darkcurrent'],
-            config['f-stop'],
-            config['exposure-time'],
-            config['iso'],
-            config['min-temp'],
-            config['max-temp'],
-            config['scaling-factor'],
-        )
+    img = rg_ratio_normalize(
+        img_orig,
+        config['i-darkcurrent'],
+        config['f-stop'],
+        config['exposure-time'],
+        config['iso'],
+        config['min-temp'],
+        config['max-temp'],
+        config['scaling-factor'],
+        img_out=False
+    )

-        # build & apply smoothing conv kernel
-        k = []
-        smoothing_radius = config['smoothing-radius']
-        for i in range(smoothing_radius):
-            k.append([1/(smoothing_radius**2) for i in range(smoothing_radius)])
-        kernel = np.array(k)
+    # build & apply smoothing conv kernel
+    k = []
+    smoothing_radius = config['smoothing-radius']
+    for i in range(smoothing_radius):
+        k.append([1/(smoothing_radius**2) for i in range(smoothing_radius)])
+    kernel = np.array(k)

-        img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)
+    img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)

-        # write colormapped image
-        img_jet = cv.applyColorMap(img, cv.COLORMAP_JET)
+    # chop off alphas & reverse bgr
+    img_orig = img_orig[:,:,:3]
+    img_orig = img_orig[:,:,::-1]

-        # TODO: GENERTE TEMP KEY & OUTPUT MATPLOTLIB
+    fig = plt.figure()
+    ax = fig.add_subplot(1, 2, 1)
+    ax.set_title("Original Image")
+    imgplot_orig = plt.imshow(img_orig)
+    ax2 = fig.add_subplot(1, 2, 2)
+    ax2.set_title("Output Heatmap")
+    imgplot_final = plt.imshow(img, cmap="plasma")
+    ticks = np.linspace(
+        config['min-temp'], 
+        config['max-temp'],
+        4
+    ).tolist()
+    cbar = plt.colorbar(
+        orientation="horizontal",
+    )
+    cbar.ax.set_xticklabels([str(t) for t in ticks])
+
+    name = filename.split(".")[0]
+    extension = filename.split(".")[1]
+    fig.savefig(f"{img_out_dir}/{name}-transformed.{extension}", dpi=120)
--- a/examples/01-0003-cropped-transformed-ratio.png
+++ b/examples/01-0003-cropped-transformed-ratio.png
--- a/examples/edge_detection.py
+++ b/examples/edge_detection.py
@@ -1,21 +0,0 @@
-# MONOCHROME EDGE DETECTION
-
-import cv2 as cv
-import numpy as np
-
-file = '01-0001-cropped.png'
-file_name = file.split(".")[0]
-file_ext = file.split(".")[1]
-
-img = cv.imread(file)
-
-img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
-
-kernel = np.array([
-    [-1, -1, -1],
-    [-1, 8, -1],
-    [-1, -1, -1],
-])
-img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)
-
-cv.imwrite(f'{file_name}-edge-detection.{file_ext}', img)
--- a/examples/projected-area/output.png
+++ b/examples/projected-area/output.png
--- a/examples/projected-area/proj-area-0.png
+++ b/examples/projected-area/proj-area-0.png
--- a/examples/projected-area/proj-area-1-processed.jpg
+++ b/examples/projected-area/proj-area-1-processed.jpg
--- a/examples/projected-area/proj-area-1.jpg
+++ b/examples/projected-area/proj-area-1.jpg
--- a/examples/projected-area/proj-area-2.jpg
+++ b/examples/projected-area/proj-area-2.jpg
--- a/examples/projected-area/proj-area-3.jpg
+++ b/examples/projected-area/proj-area-3.jpg
--- a/examples/projected-area/projected_area.py
+++ b/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))
--- a/examples/pyrometry/01-0001-cropped-edge-detection.png
+++ b/examples/pyrometry/01-0001-cropped-edge-detection.png
--- a/examples/pyrometry/01-0001-cropped-transformed-ratio.png
+++ b/examples/pyrometry/01-0001-cropped-transformed-ratio.png
--- a/examples/pyrometry/01-0001-cropped.png
+++ b/examples/pyrometry/01-0001-cropped.png
--- a/examples/pyrometry/01-0001-transformed-grayscale.png
+++ b/examples/pyrometry/01-0001-transformed-grayscale.png
--- a/examples/pyrometry/01-0001.png
+++ b/examples/pyrometry/01-0001.png
--- a/examples/pyrometry/01-0002-cropped-transformed-ratio.png
+++ b/examples/pyrometry/01-0002-cropped-transformed-ratio.png
--- a/examples/pyrometry/01-0002-cropped.png
+++ b/examples/pyrometry/01-0002-cropped.png
--- a/examples/pyrometry/01-0002.png
+++ b/examples/pyrometry/01-0002.png
--- a/examples/pyrometry/01-0003-cropped.png
+++ b/examples/pyrometry/01-0003-cropped.png
--- a/examples/pyrometry/01-0003.png
+++ b/examples/pyrometry/01-0003.png
--- a/examples/pyrometry/README.md
+++ b/examples/pyrometry/README.md
--- a/examples/pyrometry/_DSC1022
+++ b/examples/pyrometry/_DSC1022
--- a/examples/pyrometry/_DSC1022
+++ b/examples/pyrometry/_DSC1022
--- a/examples/pyrometry/asdf.png
+++ b/examples/pyrometry/asdf.png
--- a/examples/pyrometry/base64_test.py
+++ b/examples/pyrometry/base64_test.py
--- a/examples/pyrometry/edge_detection.py
+++ b/examples/pyrometry/edge_detection.py
@@ -0,0 +1,78 @@
+# MONOCHROME EDGE DETECTION
+
+import cv2 as cv
+import numpy as np
+from skimage import measure, morphology, color, segmentation
+import matplotlib.pyplot as plt
+
+file = 'streaktest2.png'
+img = cv.imread(file)
+
+# blurred = cv.GaussianBlur(img, (8, 8), 0)
+
+retval, thresh_gray = cv.threshold(img, 120, 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)
+
+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)
--- a/examples/pyrometry/edge_detection_figure.png
+++ b/examples/pyrometry/edge_detection_figure.png
--- a/examples/pyrometry/firebrand_contours_opencv.png
+++ b/examples/pyrometry/firebrand_contours_opencv.png
--- a/examples/pyrometry/formula_testing.py
+++ b/examples/pyrometry/formula_testing.py
--- a/examples/pyrometry/grayscale_pyrometry.py
+++ b/examples/pyrometry/grayscale_pyrometry.py
--- a/examples/pyrometry/ratio_pyrometry.py
+++ b/examples/pyrometry/ratio_pyrometry.py
--- a/examples/pyrometry/streaktest-edge-detection.png
+++ b/examples/pyrometry/streaktest-edge-detection.png
--- a/examples/pyrometry/streaktest.png
+++ b/examples/pyrometry/streaktest.png
--- a/examples/pyrometry/streaktest2-edge-detection.png
+++ b/examples/pyrometry/streaktest2-edge-detection.png
--- a/examples/pyrometry/streaktest2.png
+++ b/examples/pyrometry/streaktest2.png
--- a/firebrand_detection.py
+++ b/firebrand_detection.py
--- a/flask_frontend.py
+++ b/flask_frontend.py
@@ -1,6 +1,8 @@
-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

@@ -12,13 +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 = 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']),
@@ -28,15 +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')

+    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
+        legend=key,
+        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()
--- a/plotly_util.py
+++ b/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
+    )
--- a/ratio_pyrometry.py
+++ b/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(
@@ -13,11 +14,16 @@ def rg_ratio_normalize(
    ISO, 
    MIN_TEMP, 
    MAX_TEMP,
-    eqn_scaling_factor
+    eqn_scaling_factor,
 ):
+    """
+    Get normalized G/R -> temperature data + list of all temperatures
+    """
    # copy image into new array & chop off alpha values (if applicable)
    imgnew = imgarr.copy()[:,:,:3]

+    positive_temps = []
+
    for i in range(len(imgarr)):
        for j in range(len(imgarr[i])):
            px = imgarr[i][j]
@@ -32,12 +38,14 @@ def rg_ratio_normalize(
            # remove pixels outside calibration range
            if (MIN_TEMP != None and temp_C < MIN_TEMP) or (MAX_TEMP != None and temp_C > MAX_TEMP):
                temp_C = MIN_TEMP
+            elif temp_C > MIN_TEMP:
+                positive_temps.append(temp_C)

            # scale light intensity to calculated temperature
            pix_i = scale_temp(temp_C, MIN_TEMP, MAX_TEMP)
            imgnew[i][j] = [pix_i, pix_i, pix_i]

-    return imgnew
+    return imgnew, positive_temps


@jit(nopython=True)
@@ -78,12 +86,105 @@ 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)

-    img = rg_ratio_normalize(
+    # ---------------------------------------------------------
+    # -- 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,
        f_stop,
@@ -91,7 +192,7 @@ def ratio_pyrometry_pipeline(
        ISO,
        MIN_TEMP,
        MAX_TEMP,
-        eqn_scaling_factor
+        eqn_scaling_factor,
    )

    # build & apply smoothing conv kernel
@@ -105,7 +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)
@@ -126,4 +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
+    return img_orig, img_jet, tempkey, ptemps, individual_firebrands
--- a/screenshots/projected_area_in.png
+++ b/screenshots/projected_area_in.png
--- a/screenshots/projected_area_out.png
+++ b/screenshots/projected_area_out.png
--- a/screenshots/pyro_results.png
+++ b/screenshots/pyro_results.png
--- a/screenshots/temp_dist_plot.png
+++ b/screenshots/temp_dist_plot.png
--- a/size_projection.py
+++ b/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])
--- a/static/app.css
+++ b/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;
+}
--- a/static/js/csv_download.js
+++ b/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;
+}
--- a/static/js/img_preview.js
+++ b/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);
+    }
+};
--- a/templates/app.css
+++ b/templates/app.css
@@ -1,4 +0,0 @@
-.form {
-    display: flex;
-    flex-direction: column;
-}
--- a/templates/base.html
+++ b/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>
--- a/templates/base.jinja2
+++ b/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>
--- a/templates/index.jinja2
+++ b/templates/index.jinja2
@@ -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 %} 
--- a/templates/projected-area-results.html
+++ b/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 %}
--- a/templates/projected-area.html
+++ b/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 %}
--- a/templates/pyrometry-results.html
+++ b/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 %}
--- a/templates/results.jinja2
+++ b/templates/results.jinja2
@@ -1,67 +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>
-
-<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 %}
Author	SHA1	Message	Date
turtlebasket	1de21e93e1	basic firebrand recognition Co-authored-by: Alex <AlexanderMcDowell@users.noreply.github.com>	2022-11-14 22:47:43 -08:00
turtlebasket	59e0f2d861	firebrand edge detection demo Co-authored-by: Alex <AlexanderMcDowell@users.noreply.github.com>	2022-11-13 17:57:41 -08:00
turtlebasket	cc2820da90	start firebrand detection	2022-11-11 19:39:19 -08:00
turtlebasket	f8d4f85858	fully working csv download	2022-11-04 18:58:35 -07:00
turtlebasket	76e178176d	save csv + paper dims Co-authored-by: Alex <AlexanderMcDowell@users.noreply.github.com>	2022-11-04 18:42:22 -07:00
turtlebasket	fa1e988344	use static js	2022-11-04 17:55:25 -07:00
turtlebasket	dcf78bb88d	fix mm^2 issue	2022-11-03 17:36:22 -07:00
turtlebasket	481de4dfb1	update content	2022-10-31 21:01:37 -07:00
turtlebasket	8a364b81e6	interface improvements Co-authored-by: Alex <AlexanderMcDowell@users.noreply.github.com>	2022-10-28 20:49:54 -07:00
turtlebasket	97ab6a1ed1	size projections in web interface Co-authored-by: Alex <AlexanderMcDowell@users.noreply.github.com>	2022-10-28 20:11:50 -07:00
turtlebasket	405b9f7f91	restructure	2022-10-28 17:52:47 -07:00
turtlebasket	52fe136f3f	Merge branch 'master' of https://git.turtlebasket.ml/datadonkey-com/berkeley-firelab-general	2022-10-28 15:19:02 -07:00
turtlebasket	e4516e12d9	update templates	2022-10-28 15:18:51 -07:00
Your Name	55110d6736	Added projected area code for images	2022-10-28 04:16:34 +00:00
turtlebasket	407a103913	start projected area	2022-10-27 14:12:25 -07:00
turtlebasket	6635404cf9	restructure	2022-10-27 14:11:06 -07:00
turtlebasket	81e8195fb7	update readme	2022-10-27 12:11:39 -07:00
turtlebasket	6f69f508b6	tweak formatting	2022-10-27 12:05:52 -07:00
turtlebasket	f83a98ffe0	temp probability density plot	2022-10-27 12:01:35 -07:00
turtlebasket	d10665fd67	cleanups	2022-10-27 10:31:42 -07:00