Every tomato plant is different. The time it takes for a fruit to mature and become ripe for harvest varies heavily based on its color and many other factors. It is a pain for farmers to harvest at the right time and exact location to harvest manually. Our solution uses automated drone imagery with fruit color analysis over time along with location information to tell farmers exactly when to start harvesting and how to maximize quality and quantity.
# import the necessary packages
import numpy as np
%matplotlib inline
import cv2
from matplotlib import pyplot as plt
# load the image
image = cv2.imread('t1.png')# define the list of boundaries
boundaries = [
#([17, 15, 100], [50, 56, 200]),
#([86, 31, 4], [220, 88, 50]),
#([25, 146, 190], [62, 174, 250]),
([103, 86, 65], [145, 133, 128])
#([200,0,0],[225,255,255])
]# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype = "uint8")
upper = np.array(upper, dtype = "uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask = mask)
plt.imshow(np.hstack([image, output]))
# show the images
# cv2.imshow("images", np.hstack([image, output]))
# cv2.waitKey(0)
import numpy as np
import cv2
from matplotlib import pyplot as plt
from skimage import data
coins = data.coins()
bins=np.arange(0,256)
hist, bins = np.histogram(coins, bins=bins)
#hist, bins = np.histogram(x, bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.show()
from matplotlib import pyplot as plt
from skimage import data
from skimage.feature import blob_dog, blob_log, blob_doh
from math import sqrt
from skimage.color import rgb2gray
from scipy import misc # try
image = misc.imread('t1.png')
image_gray = image[:,:,1]
blobs_log = blob_log(image_gray, max_sigma=10, num_sigma=5, threshold=.1)
# Compute radii in the 3rd column.
blobs_log[:, 2] = blobs_log[:, 2] * sqrt(2)
blobs_dog = blob_dog(image_gray, max_sigma=2, threshold=.051)
blobs_dog[:, 2] = blobs_dog[:, 2] * sqrt(2)
blobs_doh = blob_doh(image_gray, max_sigma=2, threshold=.01)
blobs_list = [blobs_log, blobs_dog, blobs_doh]
colors = ['yellow', 'lime', 'red']
titles = ['Laplacian of Gaussian', 'Difference of Gaussian',
'Determinant of Hessian']
sequence = zip(blobs_list, colors, titles)
for blobs, color, title in sequence:
fig, ax = plt.subplots(1, 1)
ax.set_title(title)
ax.imshow(image, interpolation='nearest')
for blob in blobs:
y, x, r = blob
c = plt.Circle((x, y), r, color=color, linewidth=2, fill=False)
ax.add_patch(c)
plt.show()
from matplotlib import pyplot as plt
import numpy as np
import cv2
%matplotlib inline
from copy import deepcopy
image = cv2.imread('t2.png')
i = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
img = deepcopy(i)
##plt.imshow(i)
just_red = deepcopy(i[:,:,0])
#i = cv2.imread()
green_off = deepcopy(i)
green_off[:,:,1] = 0
blue_green_off = deepcopy(green_off)
blue_green_off[:,:,2] = 0
#print (i.shape, red.shape)
#plt.imshow(red, cmap='gray')
##plt.imshow(blue_green_off)
plot_image = np.concatenate((i, blue_green_off), axis=1)
plt.imshow(plot_image)<matplotlib.image.AxesImage at 0x7f3de1e51510>
blue_green_off[(blue_green_off > 250)] = 0
blue_green_off[(blue_green_off < 200)] = 0
##plt.imshow(blue_green_off)
plot_image = np.concatenate((i, blue_green_off), axis=1)
plt.imshow(plot_image)
data = blue_green_off[blue_green_off>0]
print (len(data))
avg = np.mean(data)
print (avg)32209
213.734453103
hist, bins = np.histogram(data)width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.show()
img = cv2.imread('t1.png')
edges = cv2.Canny(img,100,10)
plt.subplot(121),plt.imshow(img,cmap = 'gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(edges,cmap = 'gray')
plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
plt.show()
import cv2
import numpy as np
from matplotlib import pyplot as plt
from copy import deepcopy
img = cv2.imread('t1.png')
#img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#get rid of very bright and very dark regions
delta=30
lower_gray = np.array([delta, delta,delta])
upper_gray = np.array([255-delta,255-delta,255-delta])
# Threshold the image to get only selected
mask = cv2.inRange(img, lower_gray, upper_gray)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(img,img, mask= mask)
#Convert to HSV space
HSV_img = cv2.cvtColor(res, cv2.COLOR_BGR2HSV)
hue = HSV_img[:, :, 0]
#select maximum value of H component from histogram
hist = cv2.calcHist([hue],[0],None,[256],[0,256])
hist= hist[1:, :] #suppress black value
elem = np.argmax(hist)
hist_new = deepcopy(hist)
hist_new[elem] = 0
new_elem = np.argmax(hist_new)
#elem = hist.argsort()[-3:][::-1][1]
elem = new_elem
print np.max(hist), np.argmax(hist)
print np.max(hist_new), np.argmax(hist_new)
tolerance=10
lower_gray = np.array([elem-tolerance, 0,0])
upper_gray = np.array([elem+tolerance,255,255])
# Threshold the image to get only selected
mask = cv2.inRange(HSV_img, lower_gray, upper_gray)
# Bitwise-AND mask and original image
res2 = cv2.bitwise_and(img,img, mask= mask)
titles = ['Original Image', 'Selected Gray Values', 'Hue', 'Result']
images = [img, res, hue, res2]
for i in xrange(4):
plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
plt.show()26919.0 39
26280.0 38
res3 = images[0]-images[3]
res4 = cv2.cvtColor(res3, cv2.COLOR_HSV2BGR)
res5 = cv2.cvtColor(res4, cv2.COLOR_BGR2GRAY)print res5.shape
hist, bins = np.histogram(res5)
#hist, bins = np.histogram(x, bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
print (np.mean(res5))
plt.show()
plt.imshow(res5)(492, 1277)
52.361568972
<matplotlib.image.AxesImage at 0x7f3ddd4802d0>
#res5[res5>(np.mean(res5)-5)]=0
threshold = 40
tomato_color_param = np.mean(res5)
print (tomato_color_param)
if (tomato_color_param) > threshold:
print (True)
else:
print (False)52.361568972
True