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2 Commits

Author SHA1 Message Date
michael f83a98ffe0 temp probability density plot 2022-10-27 12:01:35 -07:00
michael d10665fd67 cleanups 2022-10-27 10:31:42 -07:00
13 changed files with 478 additions and 60 deletions

5
.gitignore vendored
View File

@ -1,5 +1,10 @@
# CUSTOM # CUSTOM
images-input/*
!images-input/.gitkeep
images-output/*
!images-output/.gitkeep
config.yaml
.vscode/ .vscode/
*.swp *.swp

View File

@ -10,3 +10,7 @@ dev:
clean: clean:
rm -rf static/* rm -rf static/*
touch static/.gitkeep touch static/.gitkeep
batch:
pipenv run python3 batch-process.py

View File

@ -10,6 +10,10 @@ flask = "*"
gunicorn = "*" gunicorn = "*"
werkzeug = "*" werkzeug = "*"
pyyaml = "*" pyyaml = "*"
matplotlib = "*"
plotly = "*"
pandas = "*"
scipy = "==1.8.1"
[dev-packages] [dev-packages]

368
Pipfile.lock generated
View File

@ -1,7 +1,7 @@
{ {
"_meta": { "_meta": {
"hash": { "hash": {
"sha256": "0cfa12a983973f0699999da1f8adcf4662998d4d37158ae1eed984cb7b773fc3" "sha256": "3428842daebc7c8a255790fde5231377c05479a39d5ce2977f043e58c7c80826"
}, },
"pipfile-spec": 6, "pipfile-spec": 6,
"requires": { "requires": {
@ -24,6 +24,89 @@
"markers": "python_version >= '3.7'", "markers": "python_version >= '3.7'",
"version": "==8.1.3" "version": "==8.1.3"
}, },
"contourpy": {
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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": { "flask": {
"hashes": [ "hashes": [
"sha256:642c450d19c4ad482f96729bd2a8f6d32554aa1e231f4f6b4e7e5264b16cca2b", "sha256:642c450d19c4ad482f96729bd2a8f6d32554aa1e231f4f6b4e7e5264b16cca2b",
@ -32,6 +115,14 @@
"index": "pypi", "index": "pypi",
"version": "==2.2.2" "version": "==2.2.2"
}, },
"fonttools": {
"hashes": [
"sha256:2bb244009f9bf3fa100fc3ead6aeb99febe5985fa20afbfbaa2f8946c2fbdaf1",
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],
"markers": "python_version >= '3.7'",
"version": "==4.38.0"
},
"gunicorn": { "gunicorn": {
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@ -56,6 +147,80 @@
"markers": "python_version >= '3.7'", "markers": "python_version >= '3.7'",
"version": "==3.1.2" "version": "==3.1.2"
}, },
"kiwisolver": {
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],
"markers": "python_version >= '3.7'",
"version": "==1.4.4"
},
"llvmlite": { "llvmlite": {
"hashes": [ "hashes": [
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@ -136,6 +301,53 @@
"markers": "python_version >= '3.7'", "markers": "python_version >= '3.7'",
"version": "==2.1.1" "version": "==2.1.1"
}, },
"matplotlib": {
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],
"index": "pypi",
"version": "==3.6.1"
},
"numba": { "numba": {
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@ -201,7 +413,7 @@
"sha256:f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef", "sha256:f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef",
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], ],
"markers": "python_version >= '3.8'", "markers": "python_version >= '3.10'",
"version": "==1.23.4" "version": "==1.23.4"
}, },
"opencv-python": { "opencv-python": {
@ -217,6 +429,142 @@
"index": "pypi", "index": "pypi",
"version": "==4.6.0.66" "version": "==4.6.0.66"
}, },
"packaging": {
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],
"markers": "python_version >= '3.6'",
"version": "==21.3"
},
"pandas": {
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],
"index": "pypi",
"version": "==1.5.1"
},
"pillow": {
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],
"markers": "python_version >= '3.7'",
"version": "==9.2.0"
},
"plotly": {
"hashes": [
"sha256:4efef479c2ec1d86dcdac8405b6ca70ca65649a77408e39a7e84a1ea2db6c787",
"sha256:52fd74b08aa4fd5a55b9d3034a30dbb746e572d7ed84897422f927fdf687ea5f"
],
"index": "pypi",
"version": "==5.11.0"
},
"pyparsing": {
"hashes": [
"sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb",
"sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc"
],
"markers": "python_full_version >= '3.6.8'",
"version": "==3.0.9"
},
"python-dateutil": {
"hashes": [
"sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86",
"sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"
],
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
"version": "==2.8.2"
},
"pytz": {
"hashes": [
"sha256:335ab46900b1465e714b4fda4963d87363264eb662aab5e65da039c25f1f5b22",
"sha256:c4d88f472f54d615e9cd582a5004d1e5f624854a6a27a6211591c251f22a6914"
],
"version": "==2022.5"
},
"pyyaml": { "pyyaml": {
"hashes": [ "hashes": [
"sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf", "sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf",
@ -271,6 +619,22 @@
"markers": "python_version >= '3.7'", "markers": "python_version >= '3.7'",
"version": "==65.5.0" "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"
},
"werkzeug": { "werkzeug": {
"hashes": [ "hashes": [
"sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f", "sha256:7ea2d48322cc7c0f8b3a215ed73eabd7b5d75d0b50e31ab006286ccff9e00b8f",

View File

@ -67,23 +67,3 @@ To autoreload on source file changes:
``` ```
gunicorn flask_frontend:app --reload 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)

View File

@ -2,14 +2,15 @@ import yaml
import cv2 as cv import cv2 as cv
import numpy as np import numpy as np
import os import os
from matplotlib import pyplot as plt, image as mpimg
from ratio_pyrometry import rg_ratio_normalize from ratio_pyrometry import rg_ratio_normalize
config = {} config = {}
with open("./config.yaml", "r") as yaml_stream: with open("./config.yaml", "r") as yaml_stream:
config = yaml.safe_load(yaml_stream) config = yaml.safe_load(yaml_stream)
img_in_dir = "./images-input" img_input_dir = "images-input"
img_out_dir = "./images-output" img_out_dir = "images-output"
accepted_formats = [ accepted_formats = [
".jpg", ".jpg",
".jpeg", ".jpeg",
@ -19,44 +20,64 @@ accepted_formats = [
files = [] files = []
for file in os.listdir(img_in_dir): for file in os.listdir(img_input_dir):
filename = os.fsdecode(file) filename = os.fsdecode(file)
valid = False valid = False
for fmt in accepted_formats: for fmt in accepted_formats:
if filename.endswith(fmt): if filename.endswith(fmt):
files.append(os.path.join(img_in_dir, filename)) files.append(filename)
valid = True valid = True
break break
if not valid: if not valid and filename != ".gitkeep":
print(f"Invalid file extension for {filename}.") print(f"Invalid file extension for {filename}.")
exit exit
for filename in files: for filename in files:
with open(filename) as imgfile: # read image & crop
# read image & crop img_orig = cv.imread(f'{img_input_dir}/{filename}', cv.IMREAD_UNCHANGED)
img_orig = cv.imread(imgfile, cv.IMREAD_UNCHANGED)
img = rg_ratio_normalize( img = rg_ratio_normalize(
img_orig, img_orig,
config['i-darkcurrent'], config['i-darkcurrent'],
config['f-stop'], config['f-stop'],
config['exposure-time'], config['exposure-time'],
config['iso'], config['iso'],
config['min-temp'], config['min-temp'],
config['max-temp'], config['max-temp'],
config['scaling-factor'], config['scaling-factor'],
) img_out=False
)
# build & apply smoothing conv kernel # build & apply smoothing conv kernel
k = [] k = []
smoothing_radius = config['smoothing-radius'] smoothing_radius = config['smoothing-radius']
for i in range(smoothing_radius): for i in range(smoothing_radius):
k.append([1/(smoothing_radius**2) for i in range(smoothing_radius)]) k.append([1/(smoothing_radius**2) for i in range(smoothing_radius)])
kernel = np.array(k) 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 # chop off alphas & reverse bgr
img_jet = cv.applyColorMap(img, cv.COLORMAP_JET) 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)

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@ -3,7 +3,13 @@
import cv2 as cv import cv2 as cv
import numpy as np import numpy as np
file = '01-0001-cropped.png' # edge-detection kernel amplification
AMPLIFIER=9
MIN_INTENSITY=100
# file = '01-0001-cropped.png'
file = 'streaktest.png'
file_name = file.split(".")[0] file_name = file.split(".")[0]
file_ext = file.split(".")[1] file_ext = file.split(".")[1]
@ -13,7 +19,7 @@ img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
kernel = np.array([ kernel = np.array([
[-1, -1, -1], [-1, -1, -1],
[-1, 8, -1], [-1, AMPLIFIER, -1],
[-1, -1, -1], [-1, -1, -1],
]) ])
img = cv.filter2D(src=img, ddepth=-1, kernel=kernel) img = cv.filter2D(src=img, ddepth=-1, kernel=kernel)

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@ -3,6 +3,8 @@ import numpy as np
from ratio_pyrometry import ratio_pyrometry_pipeline from ratio_pyrometry import ratio_pyrometry_pipeline
import base64 import base64
import cv2 as cv import cv2 as cv
import plotly.figure_factory as ff
from scipy import stats
app = Flask( app = Flask(
__name__, __name__,
@ -18,7 +20,7 @@ def index():
def ratio_pyro(): def ratio_pyro():
f = request.files['file'] f = request.files['file']
f_bytes = np.fromstring(f.read(), np.uint8) f_bytes = np.fromstring(f.read(), np.uint8)
img_orig, img_res, key = ratio_pyrometry_pipeline( img_orig, img_res, key, ptemps = ratio_pyrometry_pipeline(
f_bytes, f_bytes,
ISO=float(request.form['iso']), ISO=float(request.form['iso']),
I_Darkcurrent=float(request.form['i_darkcurrent']), I_Darkcurrent=float(request.form['i_darkcurrent']),
@ -31,12 +33,34 @@ def ratio_pyro():
eqn_scaling_factor=float(request.form['equation_scaling_factor']) eqn_scaling_factor=float(request.form['equation_scaling_factor'])
) )
# get base64 encoded images
img_orig_b64 = base64.b64encode(cv.imencode('.png', img_orig)[1]).decode(encoding='utf-8') 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') 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.name],
show_rug=False,
show_hist=False,
)
fig.update_layout(
autosize=False,
width=800,
height=600,
)
fig.update_xaxes(
title_text="Temperature (°C)",
)
fig.update_xaxes(
title_text="Probability (1/°C)",
)
freq_plot = fig.to_html()
return render_template( return render_template(
'results.jinja2', 'results.jinja2',
img_orig_b64=img_orig_b64, img_orig_b64=img_orig_b64,
img_res_b64=img_res_b64, img_res_b64=img_res_b64,
legend=key legend=key,
freq_plot=freq_plot
) )

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@ -13,11 +13,16 @@ def rg_ratio_normalize(
ISO, ISO,
MIN_TEMP, MIN_TEMP,
MAX_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) # copy image into new array & chop off alpha values (if applicable)
imgnew = imgarr.copy()[:,:,:3] imgnew = imgarr.copy()[:,:,:3]
positive_temps = []
for i in range(len(imgarr)): for i in range(len(imgarr)):
for j in range(len(imgarr[i])): for j in range(len(imgarr[i])):
px = imgarr[i][j] px = imgarr[i][j]
@ -32,12 +37,14 @@ def rg_ratio_normalize(
# remove pixels outside calibration range # remove pixels outside calibration range
if (MIN_TEMP != None and temp_C < MIN_TEMP) or (MAX_TEMP != None and temp_C > MAX_TEMP): if (MIN_TEMP != None and temp_C < MIN_TEMP) or (MAX_TEMP != None and temp_C > MAX_TEMP):
temp_C = MIN_TEMP temp_C = MIN_TEMP
elif temp_C > MIN_TEMP:
positive_temps.append(temp_C)
# scale light intensity to calculated temperature # scale light intensity to calculated temperature
pix_i = scale_temp(temp_C, MIN_TEMP, MAX_TEMP) pix_i = scale_temp(temp_C, MIN_TEMP, MAX_TEMP)
imgnew[i][j] = [pix_i, pix_i, pix_i] imgnew[i][j] = [pix_i, pix_i, pix_i]
return imgnew return imgnew, positive_temps
@jit(nopython=True) @jit(nopython=True)
@ -83,7 +90,7 @@ def ratio_pyrometry_pipeline(
# read image & crop # read image & crop
img_orig = cv.imdecode(file_bytes, cv.IMREAD_UNCHANGED) img_orig = cv.imdecode(file_bytes, cv.IMREAD_UNCHANGED)
img = rg_ratio_normalize( img, ptemps = rg_ratio_normalize(
img_orig, img_orig,
I_Darkcurrent, I_Darkcurrent,
f_stop, f_stop,
@ -91,7 +98,7 @@ def ratio_pyrometry_pipeline(
ISO, ISO,
MIN_TEMP, MIN_TEMP,
MAX_TEMP, MAX_TEMP,
eqn_scaling_factor eqn_scaling_factor,
) )
# build & apply smoothing conv kernel # build & apply smoothing conv kernel
@ -126,4 +133,4 @@ def ratio_pyrometry_pipeline(
tempkey[temps[i]] = f"rgb({c[2]}, {c[1]}, {c[0]})" tempkey[temps[i]] = f"rgb({c[2]}, {c[1]}, {c[0]})"
# original, transformed, legend # original, transformed, legend
return img_orig, img_jet, tempkey return img_orig, img_jet, tempkey, ptemps

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@ -35,6 +35,9 @@
</td> </td>
</tr> </tr>
</table> </table>
{# Temperature Frequency Plot #}
<strong>Temperature Distribution</strong>
{{freq_plot}}
<style> <style>
.img-table-heading { .img-table-heading {