# -*- coding: utf-8 -*-
"""RaspiOakFarm.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1DkavLOvESRatwVPYDUwM-qV-28JtsJDh
"""
#21 April TOP & FROINT
!wget --content-disposition https://www.dropbox.com/scl/fi/5aeta74tuh21rhj3hqiv3/Data21April_Top.zip?rlkey=9wt0htoh2k9duizp5g23f05tp&dl=0
!unzip -j Data21April_Top.zip -d ./data_top/
!wget --content-disposition https://www.dropbox.com/scl/fi/y49v1x3sf4347hn6dxo1x/Data21April_Front.zip?rlkey=6megcxrcafvr49rsrmp3q89bx&dl=0
!unzip -j Data21April_Front.zip -d ./data_front/
#4 Aug TOP
!wget --content-disposition https://www.dropbox.com/scl/fi/ri3wuicv96y5dyqaidiab/R5_2023-08-04_24_TOP.zip?rlkey=4wcx7oc33wexykr6x39v03sjs&dl=0
!unzip -j R5_2023-08-04_24_TOP.zip -d ./data_0804_front/
#4 Aug FRONT
!wget --content-disposition https://www.dropbox.com/scl/fi/wag9c3syx5da0fk81t5ny/R5_2023-08-04_24_FRONT.zip?rlkey=uwxehcuaf4jwimqs9l1ehdn3c&dl=0
!unzip -j R5_2023-08-04_24_FRONT.zip -d ./data_front/
import numpy as np
import cv2
import os
import csv
from pathlib import Path
#font settings for labeling images
bottomLeftCornerOfText = (40,520)
bottomLeftCornerOfText_train_status = (20,20)
bottomLeftCornerOfText_assy_status = (20,40)
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 0.7
fontColor = (255,255,255)
lineType = 2
#text background
start_point = (0, 480) # Start coordinate represents the top left corner of rectangle
end_point = (960, 540) # Ending coordinate represents the bottom right corner of rectangle
color = (0, 0, 0) # Black color in BGR
thickness = -1 # Line thickness in px ( -1 = fill)
"""# Let's examine a depth file"""
fstrfull = './data_top/2023-04-17-16-00-21_depth.npy'
f = open(fstrfull,'rb')
data_disparity = np.load(f)
data_disparity[0,1:10] #print out the first 10 elements
data_disparity.max()
data_disparity.min()
rows, cols = data_disparity.shape
print(f'Total {rows} rows and {cols} columns.')
"""Using depth as a segmentation tool"""
data_disparity2 = np.zeros(shape=(rows, cols))
for row in range (0,rows):
for col in range(0,cols):
if (data_disparity[row,col] < 301) or (data_disparity[row,col] > 450):
data_disparity2[row,col] = 1
else:
data_disparity2[row,col] = ((data_disparity[row,col] - 300) / 150 * 255) #normalizing
data_disparity2 = data_disparity2.astype(np.uint8)
data_disparity2 = cv2.applyColorMap(data_disparity2, cv2.COLORMAP_JET)
from google.colab.patches import cv2_imshow
cv2_imshow(data_disparity)
from google.colab.patches import cv2_imshow
cv2_imshow(data_disparity2)
#cv2.imwrite('export.png', data_disparity2) #saves as PNG
#mid row bottom line
start_point = (0, 360) # Start coordinate represents the top left corner of rectangle
end_point = (1280, 361) # Ending coordinate represents the bottom right corner of rectangle
color = (255, 255, 255) # Blue color in BGR
thickness = -1 # Line thickness in px ( -1 = fill)
cv2.rectangle(data_disparity2, start_point, end_point, color, thickness)
#mid row top line
start_point = (0, 200) # Start coordinate represents the top left corner of rectangle
end_point = (1280, 201) # Ending coordinate represents the bottom right corner of rectangle
color = (255, 255, 255) # Blue color in BGR
thickness = -1 # Line thickness in px ( -1 = fill)
cv2.rectangle(data_disparity2, start_point, end_point, color, thickness)
#top row, top line
start_point = (0, 60) # Start coordinate represents the top left corner of rectangle
end_point = (1280, 61) # Ending coordinate represents the bottom right corner of rectangle
color = (255, 255, 255) # Blue color in BGR
thickness = -1 # Line thickness in px ( -1 = fill)
cv2.rectangle(data_disparity2, start_point, end_point, color, thickness)
#top row, bottom line
start_point = (0, 190) # Start coordinate represents the top left corner of rectangle
end_point = (1280, 191) # Ending coordinate represents the bottom right corner of rectangle
color = (255, 255, 255) # Blue color in BGR
thickness = -1 # Line thickness in px ( -1 = fill)
cv2.rectangle(data_disparity2, start_point, end_point, color, thickness)
#bottom row, top line
start_point = (0, 520) # Start coordinate represents the top left corner of rectangle
end_point = (1280, 521) # Ending coordinate represents the bottom right corner of rectangle
color = (255, 255, 255) # Blue color in BGR
thickness = -1 # Line thickness in px ( -1 = fill)
cv2.rectangle(data_disparity2, start_point, end_point, color, thickness)
#bottom row, bottom line
start_point = (0, 719) # Start coordinate represents the top left corner of rectangle
end_point = (1280, 720) # Ending coordinate represents the bottom right corner of rectangle
color = (255, 255, 255) # Blue color in BGR
thickness = -1 # Line thickness in px ( -1 = fill)
cv2.rectangle(data_disparity2, start_point, end_point, color, thickness)
cv2_imshow(data_disparity2)
"""Calculate the depth distribution
https://docs.luxonis.com/projects/api/en/latest/components/nodes/stereo_depth/#calculate-depth-using-disparity-map
"""
data_depth = np.zeros(shape=(rows, cols))
for row in range (0,rows):
for col in range(0,cols):
if (data_disparity[row,col] > 0) :
data_depth[row,col] = 883.15 * 7.5 / data_disparity[row,col] #from camera specs
print(f'Depth ranges from {data_depth.min()} to {data_depth.max()}')
row_mid = []
for row in range (200,360):
for col in range(0,cols):
if (data_depth[row,col] < 2) or (data_depth[row,col] > 60):
row_mid.append(0)
else: #range 100 to 250 mm
row_mid.append(data_depth[row,col])
#plt.hist(row_mid, bins=range(0, 60 + binwidth, binwidth))
row_bottom = []
for row in range (520,720):
for col in range(0,cols):
if (data_depth[row,col] < 2) or (data_depth[row,col] > 60):
row_bottom.append(0)
else: #range 100 to 250 mm
row_bottom.append(data_depth[row,col])
#plt.hist(row_bottom, bins=range(0, 60 + binwidth, binwidth))
row_top = []
for row in range (60,190):
for col in range(0,cols):
if (data_depth[row,col] < 2) or (data_depth[row,col] > 60):
row_top.append(0)
else: #range 100 to 250 mm
row_top.append(data_depth[row,col])
#plt.hist(row_top, bins=range(0, 60 + binwidth, binwidth))
import matplotlib.pyplot as plt
binwidth=1
plt.hist(data_depth.flatten(), bins=range(1, 60 + binwidth, binwidth))
plt.xlabel('Depth (cm)')
plt.ylabel('Number of pixels (counts)')
plt.title('Distribution of depth data')
plt.hist(row_top, bins=range(1, 60 + binwidth, binwidth),label='top', alpha=0.5)
plt.hist(row_mid, bins=range(1, 60 + binwidth, binwidth),label='mid' , alpha=0.5)
plt.hist(row_bottom, bins=range(1, 60 + binwidth, binwidth),label='bottom', alpha=0.5)
plt.xlabel('Depth (cm)')
plt.ylabel('Number of pixels (counts)')
plt.title('Distribution of depth data for top, middle and bottom rows')
plt.legend()
"""# Compare plant heights at same time every day using top camera"""
#try out different time of day - what is the best time when height is max.
fstrfull6 = './data_top/2023-04-17-16-00-21_depth.npy'
fstrfull7 = './data_top/2023-04-18-16-00-19_depth.npy'
fstrfull8 = './data_top/2023-04-19-16-00-18_depth.npy'
fstrfull9 = './data_top/2023-04-20-16-00-22_depth.npy'
fstrfull10 = './data_top/2023-04-21-16-00-21_depth.npy'
fstrfull6rgb = './data_top/2023-04-17-16-00-21_rgb.npy'
fstrfull7rgb = './data_top/2023-04-18-16-00-19_rgb.npy'
fstrfull8rgb = './data_top/2023-04-19-16-00-18_rgb.npy'
fstrfull9rgb = './data_top/2023-04-20-16-00-22_rgb.npy'
fstrfull10rgb = './data_top/2023-04-21-16-00-21_rgb.npy'
label6 = '17Apr'
label7 = '18Apr'
label8 = '19Apr'
label9 = '20Apr'
label10 = '21Apr'
f6 = open(fstrfull6,'rb')
data6_disparity = np.load(f6)
f7 = open(fstrfull7,'rb')
data7_disparity = np.load(f7)
f8 = open(fstrfull8,'rb')
data8_disparity = np.load(f8)
f9 = open(fstrfull9,'rb')
data9_disparity = np.load(f9)
f10 = open(fstrfull10,'rb')
data10_disparity = np.load(f10)
rows, cols = data10_disparity.shape
binwidth = 1
plt.hist(data6_disparity.flatten(), bins=range(1, 1000 + binwidth, binwidth))
plt.xlabel('Disparity')
plt.ylabel('Number of pixels (counts)')
data6_depth = np.zeros(shape=(rows, cols))
data7_depth = np.zeros(shape=(rows, cols))
data8_depth = np.zeros(shape=(rows, cols))
data9_depth = np.zeros(shape=(rows, cols))
data10_depth = np.zeros(shape=(rows, cols))
data6_height = np.zeros(shape=(rows, cols))
data7_height = np.zeros(shape=(rows, cols))
data8_height = np.zeros(shape=(rows, cols))
data9_height = np.zeros(shape=(rows, cols))
data10_height = np.zeros(shape=(rows, cols))
import math
x0 = 5
y0 = 50
for row in range (0,rows):
for col in range(0,cols):
if (data6_disparity[row,col] > 0 ) :
data6_depth[row,col] = 883.15 * 7.5 / data6_disparity[row,col]
if (data6_depth[row,col] > 10 and data6_depth[row,col] < 50):
data6_height[row,col] = y0 - math.sqrt(data6_depth[row,col]*data6_depth[row,col] - x0*x0)
else:
data6_height[row,col] = 0
if (data7_disparity[row,col] > 0 ) :
data7_depth[row,col] = 883.15 * 7.5 / data7_disparity[row,col]
if (data7_depth[row,col] > 10 and data7_depth[row,col] < 50):
data7_height[row,col] = y0 - math.sqrt(data7_depth[row,col]*data7_depth[row,col] - x0*x0)
else:
data7_height[row,col] = 0
if (data8_disparity[row,col] > 0 ) :
data8_depth[row,col] = 883.15 * 7.5 / data8_disparity[row,col]
if (data8_depth[row,col] > 10 and data8_depth[row,col] < 50) :
data8_height[row,col] = y0 - math.sqrt(data8_depth[row,col]*data8_depth[row,col] - x0*x0)
else :
data8_height[row,col] = 0
if (data9_disparity[row,col] > 0) :
data9_depth[row,col] = 883.15 * 7.5 / data9_disparity[row,col]
if (data9_depth[row,col] > 10 and data9_depth[row,col] < 50) :
data9_height[row,col] = y0 - math.sqrt(data9_depth[row,col]*data9_depth[row,col] - x0*x0)
else :
data9_height[row,col] = 0
if (data10_disparity[row,col] > 0 ) :
data10_depth[row,col] = 883.15 * 7.5 / data10_disparity[row,col]
if (data10_depth[row,col] > 10 and data10_depth[row,col] < 50) :
data10_height[row,col] = y0 - math.sqrt(data10_depth[row,col]*data10_depth[row,col] - x0*x0)
else:
data10_height[row,col] = 0
binwidth = 1
plt.hist(data6_depth.flatten(), bins=range(1, 100 + binwidth, binwidth))
plt.xlabel('Depth (a.u.)')
plt.ylabel('Number of pixels (counts)')
binwidth = 1
plt.hist(data6_height.flatten(), bins=range(1, 100 + binwidth, binwidth))
plt.xlabel('Height (a.u.)')
plt.ylabel('Number of pixels (counts)')
data6_depth_plotdata = np.zeros(shape=(rows, cols))
data6_height_plotdata = np.zeros(shape=(rows, cols))
count6 = 0
for row in range (0,rows):
for col in range(0,cols):
if (data6_depth[row,col] < 2) or (data6_depth[row,col] > 60):
data6_depth_plotdata[row,col] = 0
else: #range 100 to 250 mm
data6_depth_plotdata[row,col] = (data6_depth[row,col] - 2) / 18 * 255
data6_height_plotdata[row,col] = y0 - math.sqrt(data6_depth[row,col]*data6_depth[row,col] - x0*x0)
count6+=data6_height_plotdata[row,col]
data6_depth_plotdata = data6_depth_plotdata.astype(np.uint8)
data6_depth_plotdata = cv2.applyColorMap(data6_depth_plotdata, cv2.COLORMAP_JET)
plt.figure(figsize=[8,6])
plt.imshow(data6_depth_plotdata)
plt.text(50,50, label6, c='white', fontsize='x-large', fontweight='bold')
print(count6)
data6_depth_plotdata = np.zeros(shape=(rows, cols))
data6_height_plotdata = np.zeros(shape=(rows, cols))
count6 = 0
for row in range (0,rows):
for col in range(0,cols):
if (row < 200) or (row > 350) or (data6_depth[row,col] < 2) or (data6_depth[row,col] > 20):
data6_depth_plotdata[row,col] = 0
else: #range 100 to 250 mm
data6_depth_plotdata[row,col] = (data6_depth[row,col] - 2) / 18 * 255
data6_height_plotdata[row,col] = y0 - math.sqrt(data6_depth[row,col]*data6_depth[row,col] - x0*x0)
count6+=data6_height_plotdata[row,col]
data6_depth_plotdata = data6_depth_plotdata.astype(np.uint8)
data6_depth_plotdata = cv2.applyColorMap(data6_depth_plotdata, cv2.COLORMAP_JET)
plt.figure(figsize=[8,6])
plt.imshow(data6_depth_plotdata)
plt.text(50,50, label6 + ':' + str(count6), c='white', fontsize='x-large', fontweight='bold')
print(count6)
f = open(fstrfull6rgb,'rb')
rgb6_data = np.load(f)
plt.figure(figsize=[8,6])
plt.imshow(rgb6_data)
plt.text(50,100, label6 + ':' + str(count6), c='white', fontsize='x-large', fontweight='bold')
binwidth = 1
plt.hist(data7_depth.flatten(), bins=range(1, 100 + binwidth, binwidth))
plt.xlabel('Depth (cm)')
plt.ylabel('Number of pixels (counts)')
data7_depth_plotdata = np.zeros(shape=(rows, cols))
data7_height_plotdata = np.zeros(shape=(rows, cols))
count7 = 0
for row in range (0,rows):
for col in range(0,cols):
if (row < 200) or (row > 350) or (data7_depth[row,col] < 2) or (data7_depth[row,col] > 20):
data7_depth_plotdata[row,col] = 0
else: #range 100 to 250 mm
data7_depth_plotdata[row,col] = (data7_depth[row,col] - 2) / 18 * 255
data7_height_plotdata[row,col] = y0 - math.sqrt(data7_depth[row,col]*data7_depth[row,col] - x0*x0)
count7+=data7_height_plotdata[row,col]
data7_depth_plotdata = data7_depth_plotdata.astype(np.uint8)
data7_depth_plotdata = cv2.applyColorMap(data7_depth_plotdata, cv2.COLORMAP_JET)
plt.figure(figsize=[8,6])
plt.imshow(data7_depth_plotdata)
plt.text(50,50, label7 + ':' + str(count7), c='white', fontsize='x-large', fontweight='bold')
print(count7)
f = open(fstrfull7rgb,'rb')
rgb7_data = np.load(f)
plt.figure(figsize=[8,6])
plt.imshow(rgb7_data)
plt.text(50,100, label7 + ':' + str(count7), c='white', fontsize='x-large', fontweight='bold')
binwidth = 1
plt.hist(data8_depth.flatten(), bins=range(1, 100 + binwidth, binwidth))
plt.xlabel('Depth (cm)')
plt.ylabel('Number of pixels (counts)')
data8_depth_plotdata = np.zeros(shape=(rows, cols))
data8_height_plotdata = np.zeros(shape=(rows, cols))
count8 = 0
for row in range (0,rows):
for col in range(0,cols):
if (row < 200) or (row > 350) or (data8_depth[row,col] < 2) or (data8_depth[row,col] > 20):
data8_depth_plotdata[row,col] = 0
else: #range 100 to 250 mm
data8_depth_plotdata[row,col] = (data8_depth[row,col] - 2) / 18 * 255
data8_height_plotdata[row,col] = y0 - math.sqrt(data8_depth[row,col]*data8_depth[row,col] - x0*x0)
count8+=data8_height_plotdata[row,col]
data8_depth_plotdata = data8_depth_plotdata.astype(np.uint8)
data8_depth_plotdata = cv2.applyColorMap(data8_depth_plotdata, cv2.COLORMAP_JET)
plt.figure(figsize=[8,6])
plt.imshow(data8_depth_plotdata)
plt.text(50,50, label8 + ':' + str(count8), c='white', fontsize='x-large', fontweight='bold')
print(count8)
f = open(fstrfull8rgb,'rb')
rgb8_data = np.load(f)
plt.figure(figsize=[8,6])
plt.imshow(rgb8_data)
plt.text(50,100, label8 + ':' + str(count8), c='white', fontsize='x-large', fontweight='bold')
binwidth = 1
plt.hist(data9_depth.flatten(), bins=range(1, 100 + binwidth, binwidth))
plt.xlabel('Depth (cm)')
plt.ylabel('Number of pixels (counts)')
data9_depth_plotdata = np.zeros(shape=(rows, cols))
data9_height_plotdata = np.zeros(shape=(rows, cols))
count9 = 0
for row in range (0,rows):
for col in range(0,cols):
if (row < 200) or (row > 350) or (data9_depth[row,col] < 2) or (data9_depth[row,col] > 20):
data9_depth_plotdata[row,col] = 0
else: #range 100 to 250 mm
data9_depth_plotdata[row,col] = (data9_depth[row,col] - 2) / 18 * 255
data9_height_plotdata[row,col] = y0 - math.sqrt(data9_depth[row,col]*data9_depth[row,col] - x0*x0)
count9+=data9_height_plotdata[row,col]
data9_depth_plotdata = data9_depth_plotdata.astype(np.uint8)
data9_depth_plotdata = cv2.applyColorMap(data9_depth_plotdata, cv2.COLORMAP_JET)
plt.figure(figsize=[8,6])
plt.imshow(data9_depth_plotdata)
plt.text(50,50, label9 + ':' + str(count9), c='white', fontsize='x-large', fontweight='bold')
print(count9)
f = open(fstrfull9rgb,'rb')
rgb9_data = np.load(f)
plt.figure(figsize=[8,6])
plt.imshow(rgb9_data)
plt.text(50,100, label9 + ':' + str(count9), c='white', fontsize='x-large', fontweight='bold')
data10_depth_plotdata = np.zeros(shape=(rows, cols))
data10_height_plotdata = np.zeros(shape=(rows, cols))
count10=0
for row in range (0,rows):
for col in range(0,cols):
if (row < 200) or (row > 350) or (data10_depth[row,col] < 2) or (data10_depth[row,col] > 20):
data10_depth_plotdata[row,col] = 0
else: #range 100 to 250 mm
data10_depth_plotdata[row,col] = (data10_depth[row,col] - 2) / 18 * 255
data10_height_plotdata[row,col] = y0 - math.sqrt(data10_depth[row,col]*data10_depth[row,col] - x0*x0)
count10+=data10_height_plotdata[row,col]
data10_depth_plotdata = data10_depth_plotdata.astype(np.uint8)
data10_depth_plotdata = cv2.applyColorMap(data10_depth_plotdata, cv2.COLORMAP_JET)
plt.figure(figsize=[8,6])
plt.imshow(data10_depth_plotdata)
plt.text(50,50, label10 + ':' + str(count10), c='white', fontsize='x-large', fontweight='bold')
print(count10)
f = open(fstrfull10rgb,'rb')
rgb10_data = np.load(f)
plt.figure(figsize=[8,6])
plt.imshow(rgb10_data)
plt.text(50,100, label10 + ':' + str(count10), c='white', fontsize='x-large', fontweight='bold')
counts = [count6, count7, count8, count9, count10]
label = [label6, label7, label8, label9, label10]
plt.plot(label,counts,'bo:')
cv2_imshow(rgb10_data)
#cv2.imwrite('rgb10.png', rgb10_data)
counts[0]
#normalized plot
ncounts = np.zeros(len(counts))
for i in range(0,len(ncounts)):
ncounts[i] = counts[i]/counts[0]
plt.plot(label,ncounts,'bo:')
plt.ylabel('Normalized ROI area')
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