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First whack at thermal imaging with the BeagleBone Black and AGM88xx 8x8 thermal arrays sensors
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""" | |
bbb_thermal_imager.py | |
Oct. 2014 | |
First whack at thermal imaging with the BeagleBone Black and AMG88xx 8x8 | |
thermal arrays sensors. | |
Copyright (c) 2014 Alexander Hiam | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
of this software and associated documentation files (the "Software"), to deal | |
in the Software without restriction, including without limitation the rights | |
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
copies of the Software, and to permit persons to whom the Software is | |
furnished to do so, subject to the following conditions: | |
The above copyright notice and this permission notice shall be included in | |
all copies or substantial portions of the Software. | |
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN | |
THE SOFTWARE. | |
""" | |
import bbio, cv2, colorsys | |
import numpy as np | |
class ThermalImager(object): | |
AMG88_ADDR = 0x68 | |
PIXEL_DATA_START_REG = 0x80 | |
PIXEL_DATA_LENGTH = 128 | |
PIXEL_DATA_BLOCK_SIZE = 32 | |
# For some reason contiuous i2c reads of 64 bytes and up is causing kernel | |
# panic for me (buffer overflow perhaps?). Read 32 bytes at a time. | |
HSV_GRADIENT = 0 | |
BLUE_RED_GRADIENT = 1 | |
GREYSCALE_GRADIENT = 2 | |
GREY_RED_GRADIENT = 3 | |
# Should move kernel sizes to lookup tables based on the color map being | |
# used, like so: | |
GUASSIAN_KSIZE = { | |
HSV_GRADIENT : (7,7), | |
BLUE_RED_GRADIENT : (7,7), | |
GREYSCALE_GRADIENT : (7,7), | |
GREY_RED_GRADIENT : (7,7) | |
} | |
# Some BGR color values: | |
RED = [ 0, 0,255] | |
BLUE = [255, 0, 0] | |
WHITE = [255,255,255] | |
BLACK = [ 0, 0, 0] | |
GRAY = [ 10, 10, 10] | |
def __init__(self, width=150, height=150, fps=4, gradient=HSV_GRADIENT): | |
self.width = width | |
self.height = height | |
self.fps = fps | |
self.period_ms = 1000./fps | |
self.video = cv2.VideoWriter() | |
self.min_temp = float('inf') | |
self.max_temp = float('-inf') | |
self.last_auto_scale = None | |
self.auto_scale_interval_ms = 5000 # rescale every 5 seconds | |
self.gradient = gradient | |
def run(self, filename): | |
""" Loops until ctrl-C grabbing frames, processing and saving to given | |
video file. (should be .avi!) """ | |
bbio.Wire1.begin() | |
# Disabled for now, but working on overlaying the thermal image on a webcam | |
# feed. (The Logitech C270 has a 60 degree fov like the AMG88xx) | |
#self.webcam = cv2.VideoCapture(0) | |
#width = self.webcam.get(3) | |
#height = self.webcam.get(4) | |
#self.webcam_ratio = float(width)/height | |
self.video.open(filename, cv2.cv.CV_FOURCC('M','J','P','G'), self.fps, | |
(self.width, self.height)) | |
try: | |
while True: | |
start = bbio.millis() | |
frame = self.getFrame() | |
#ret, webcam_image = self.webcam.read() | |
self.autoScale(frame.min(), frame.max()) | |
image = self.generateColorMap(frame) | |
image = cv2.resize(image, (self.width, self.height), | |
interpolation=cv2.INTER_LANCZOS4) | |
# Currently does edge and object detection: | |
image = self.postProcess(image) | |
# Comment out to get higher frame rates | |
# Note: the kernels for the edge and object detection filters are | |
# currently only configured for the HSV gradient, the other color maps | |
# won't work well without tweaking kernel sizes | |
# If using the webcam as well this will overlay the two images: | |
#image = self.createOverlayedImage(image, webcam_image) | |
self.video.write(image) | |
dt = bbio.millis() - start | |
# Print the time it took to process the frame: | |
print dt # (invert for maximum frame rate) | |
leftover = self.period_ms - dt | |
if leftover > 0: | |
bbio.delay(leftover) | |
except KeyboardInterrupt: | |
return | |
def getFrame(self): | |
""" Retrieves and returns a frame of temperature data from the sensor. """ | |
registers = [] | |
for i in range(0, self.PIXEL_DATA_LENGTH, self.PIXEL_DATA_BLOCK_SIZE): | |
registers += bbio.Wire1.readTransaction(self.AMG88_ADDR, | |
self.PIXEL_DATA_START_REG+i, | |
self.PIXEL_DATA_BLOCK_SIZE) | |
frame = [] | |
for i in range(0, 128, 2): | |
value = registers[i+1]<<8 | registers[i] | |
if value & (0x1<<11): | |
# do 2's compliment conversion | |
value = value - 2048 | |
value *= 0.25 # convert to C | |
frame.append(value) | |
return np.array(frame) | |
def autoScale(self, min, max): | |
""" Sets minimum and maximum temperature values for image gradient. """ | |
if not self.last_auto_scale: | |
self.last_auto_scale = bbio.millis() | |
elif bbio.millis() - self.last_auto_scale > self.auto_scale_interval_ms: | |
# reset min and max temps to force scaling | |
self.min_temp = float('inf') | |
self.max_temp = float('-inf') | |
self.last_auto_scale = bbio.millis() | |
if min < self.min_temp: self.min_temp = min | |
if max > self.max_temp: self.max_temp = max | |
def hsvGradient(self, frame): | |
""" Creates and returns a color map based on the HSV gradient. Takes a | |
ratio map, 0.0 = 0 degrees hue in HSV space and 1.0 = 360 degree hue. """ | |
image = np.zeros((8,8,3), np.uint8) | |
x = 0 | |
y = 0 | |
for value in frame: | |
value = colorsys.hsv_to_rgb(value, 1, 1) | |
image[y][x] = map(lambda x: 255*x, value) | |
x += 1 | |
if x > 7: | |
y += 1 | |
x = 0 | |
return image | |
def twoColorGradient(self, frame, min_color, max_color): | |
""" Creates and returns a color map based on the given ratio map. Colors | |
will range linearly from 0.0=min_color to 1.0=max_color. """ | |
image = np.zeros((8,8,3), np.uint8) | |
x = 0 | |
y = 0 | |
for value in frame: | |
color = [0]*3 | |
for ch in range(3): | |
color[ch] = min_color[ch] + value * (max_color[ch] - min_color[ch]) | |
image[y][x] = color | |
x += 1 | |
if x > 7: | |
y += 1 | |
x = 0 | |
return image | |
def generateColorMap(self, frame): | |
""" Generate color values based on 1D linear gradient algorithm at | |
http://en.wikibooks.org/wiki/Color_Theory/Color_gradient """ | |
# Create a copy of the frame so we can do some in-place operations: | |
frame = np.copy(frame) | |
# Map temperature values to [0,1] | |
frame -= self.min_temp | |
frame /= (self.max_temp - self.min_temp) | |
# Create pixel color map: | |
if self.gradient == self.HSV_GRADIENT: | |
return self.hsvGradient(frame) | |
if self.gradient == self.BLUE_RED_GRADIENT: | |
return self.twoColorGradient(frame, self.BLUE, self.RED) | |
if self.gradient == self.GREYSCALE_GRADIENT: | |
return self.twoColorGradient(frame, self.BLACK, self.WHITE) | |
return self.twoColorGradient(frame, self.GREY, self.RED) | |
def createOverlayedImage(self, thermal_image, webcam_image): | |
""" Combines the frame from the webcam with the thermal image. """ | |
webcam_height = int(self.width/self.webcam_ratio) | |
image = cv2.resize(webcam_image, (self.width, webcam_height)) | |
top_border = bottom_border = (self.height - webcam_height) / 2 | |
if webcam_height + 2*top_border < self.height: | |
top_border += 1 | |
image = cv2.copyMakeBorder(image, top_border, bottom_border, 0, 0, | |
cv2.BORDER_CONSTANT, value=[0,0,0]) | |
cv2.addWeighted(image, 1.0, thermal_image, 1.0, 0, image) | |
return image | |
def getColorContours(self, orig_img, hsv_lower=[0,0,0], hsv_upper=[20,255,255]): | |
""" Finds and returns an array of contours of objects withihn the given | |
color range in the thermal color map. """ | |
img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2HSV) | |
hsv_lower = np.array(hsv_lower,np.uint8) | |
hsv_upper = np.array(hsv_upper,np.uint8) | |
in_range = cv2.inRange(img, hsv_lower, hsv_upper) | |
kernel = np.ones((15, 15), "uint8") | |
in_range = cv2.dilate(in_range, kernel) | |
contours, hierarchy = cv2.findContours(in_range, cv2.RETR_LIST, | |
cv2.CHAIN_APPROX_SIMPLE) | |
return contours | |
def detectEdges(self, orig_img): | |
""" Performs a Laplacian transform on the image and returns the result. """ | |
edges = cv2.cvtColor(orig_img, cv2.COLOR_BGR2GRAY) | |
edges = cv2.GaussianBlur(edges,(7,7),0) | |
edges = cv2.Laplacian(edges,cv2.CV_8U, ksize=5) | |
kernel = np.ones((2, 2), "uint8") | |
edges = cv2.erode(edges, kernel, iterations=1) | |
#edges = cv2.Canny(edges, 33, 33) | |
edges = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR) | |
return edges | |
def detectObjects(self, orig_img, only_largest=True, min_area=500): | |
""" Detects objects and draws boxes around them. """ | |
contours = self.getColorContours(orig_img) | |
#contours = self.detectEdges(orig_img) | |
largest_contour = None | |
largest_area = 0 | |
def drawBox(contour, color): | |
# Draws a box around the given object | |
rect = cv2.minAreaRect(contour) | |
rect = ((rect[0][0], rect[0][1]), (rect[1][0], rect[1][1]), rect[2]) | |
box = cv2.cv.BoxPoints(rect) | |
box = np.int0(box) | |
cv2.drawContours(orig_img,[box], 0, color, 2) | |
for i, contour in enumerate(contours): | |
area = cv2.contourArea(contour) | |
if area < min_area: continue | |
if only_largest: | |
if area > largest_area: | |
largest_area = area | |
largest_contour = contour | |
else: | |
drawBox(contour, (0, 0, 255)) | |
if only_largest and largest_contour != None: | |
drawBox(largest_contour, (0, 0, 255)) | |
return orig_img | |
def postProcess(self, orig_img): | |
edges = self.detectEdges(orig_img) | |
processed_img = self.detectObjects(orig_img) | |
cv2.addWeighted(edges, 1, processed_img, 1, 0, processed_img) | |
return processed_img | |
if __name__ == '__main__': | |
gradient = ThermalImager.HSV_GRADIENT | |
ThermalImager(200, 200, 4, gradient=gradient).run('video.avi') |
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