i use opencv and .What should i do ? What is the issue here ?
.What should i do ? What is the issue here ?
e:
Output image
for k in range(len(finallist[i][j])):
y, x, w, h = finallist[i][j][k][0], finallist[i][j][k][1],
finallist[i][j][k][2], \finallist[i][j][k][3]
roi = iii[x - 3:x + h, y :y + w + 2]
roi1 = cv2.copyMakeBorder(roi, 10, 10, 7, 7, cv2.BORDER_CONSTANT, value=[255, 255])
img = cv2.resize(roi1, None, fx=4, fy=6, interpolation=cv2.INTER_CUBIC)
adaptiveThresh = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY, 15, 12)
out = pytesseract.image_to_string(adaptiveThresh)
if (len(out) == 0):
out = pytesseract.image_to_string(adaptiveThresh, config='--psm 10--')
to_out = to_out + " " + out
print(to_out)
todump.append(to_out)
cv2.imshow('image', img)
cv2.waitKey(0)
# cv2.destroyAllWindows()
Related
I am trying to execute a while loop that holds a function with parameters. However, I have noticed that the parameters inside the while loop are not updated which leads to an infinite while loop. Is there a reason behind the fact that the function parameters are not being updated after every loop?
import shapefile
from osgeo import gdal
#import rasterio
print (gdal.VersionInfo())
def pointInRect(x, y, x1, y1, w, h): # check if a raster point is in another raster
x2, y2 = x1+w, y1+h
if (x1 < x and x < x2):
if (y1 < y and y < y2):
return True
return False
# Open the shapes centroids
shp_cntrds = 'Path to centroids'
sf_cntrds = shapefile.Reader(shp_cntrds)
shapes_cntrds = sf_cntrds.shapes()
records_cntrds = sf_cntrds.records()
# adjust labels position according to its shapes centroids position
for i in range(len(records_cntrds)):
print(i)
tods = gdal.Open(str(records_cntrds[i][1]))
width = tods.RasterXSize
height = tods.RasterYSize
tods.SetGeoTransform([shapes_cntrds[i].points[0][0] - (width * 0.005), 0.01, 0,
shapes_cntrds[i].points[0][1] + (height * 0.005), 0, -0.01])
gt = tods.GetGeoTransform()
left = gt[0]
bottom = gt[3] + width * gt[4] + height * gt[5]
right = gt[0] + width * gt[1] + height * gt[2]
top = gt[3]
srs = osr.SpatialReference()
srs.SetUTM(32, 1) # set crs
srs.SetWellKnownGeogCS('WGS84') # set crs
tods.SetProjection(srs.ExportToWkt()) # set Projection and save file
print(width, height)
tods = None
# iterate through Labels and move labels away from each others if they overlapp
for i in range(len(records_cntrds)):
tods1 = gdal.Open(str(records_cntrds[i][1])) # records of the centroid shapefile contains the raster file path
width = tods1.RasterXSize
height = tods1.RasterYSize
gt = tods1.GetGeoTransform()
left = gt[0]
bottom = gt[3] + width * gt[4] + height * gt[5]
right = gt[0] + width * gt[1] + height * gt[2]
top = gt[3]
face = [x for x in list(range(len(records_cntrds))) if x != i]
tods1 = None
for j in face:
if str(records_cntrds[i][1]) == str(records_cntrds[j][1]):
pass
else:
ds_raster_face = gdal.Open(str(records_cntrds[j][1]))
#print(str(records_cntrds[i][1]))
#print(str(records_cntrds[j][1]))
gt_face = ds_raster_face.GetGeoTransform()
width_face = ds_raster_face.RasterXSize
height_face = ds_raster_face.RasterYSize
left_face = gt_face[0]
bottom_face = gt_face[3] + width_face * gt_face[4] + height_face * gt_face[5]
right_face = gt_face[0] + width_face * gt_face[1] + height_face * gt_face[2]
top_face = gt_face[3]
width1 = width
left1 = left
height1 = height
bottom1 = bottom
while pointInRect(left_face, bottom_face, left1, bottom1, width1*0.01, height1*0.01) :
tods2 = gdal.Open(str(records_cntrds[i][1]))
gt = tods2.GetGeoTransform()
width1 = tods2.RasterXSize
height1 = tods2.RasterYSize
left1 = gt[0]
bottom1 = gt[3] + width1 * gt[4] + height1 * gt[5]
print("while executed")
tods2.SetGeoTransform([(shapes_cntrds[i].points[0][0] - (width1 * 0.005)) - 2.7, 0.01, 0,
(shapes_cntrds[i].points[0][1] + (height1 * 0.005)) - 2.8, 0, -0.01])
print("coordinates changed to",(i, left1, bottom1, width1, height1))
tods2 = None
The while loop should break when the function return false but it is repeating the same thing. Are the gt values not updatet or are they initialized again ?
I am having an issue with the ggplot code line where R doesn't like the "group = Year".
Here is what my data looks like:
> head(data.scores.pa)
NMDS1 NMDS2 NMDS3 Site Year Elevation Fire history
1 -0.737547 0.73473457 0.7575643 BF 2004 1710 Burnt
......
> head(spp.scrs2)
species MDS1 MDS2 pval
1 Acrothamnus.montanus 0.8383 -0.02382347 1e-04
........
> head(vec.sp.df.pa)
MDS1 MDS2 species pvals
Elevation 0.834847 0.747474 Elevation 0.005
Here is the code I am using:
>xy <- ggplot(data.scores.pa, aes(x = NMDS1, y = NMDS2, group = Year)) +
geom_point(size = 3, aes(shape = Fire history, colour = Year))+
stat_ellipse(mapping = NULL, data = NULL, geom = "path", position = "identity", type = "t", level = 0.95, segments = 51, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) +
geom_segment(data=vec.sp.df.pa, aes(x=0,xend=MDS1,y=0,yend=MDS2),
arrow = arrow(length = unit(0.5,"cm")),colour="grey")+
geom_text_repel(data=vec.sp.df.pa,aes(x=MDS1,y=MDS2,label=species),size=2)+
geom_segment(data=spp.scrs2,aes(x=0,xend=MDS1,y=0,yend=MDS2),
arrow = arrow(length = unit(0.5, "cm")),colour="black")+
geom_text_repel(data=spp.scrs2, aes(x=MDS1,y=MDS2,label=species),size=2)+
annotate("text", x = -1.6, y = 1, label = paste0("3D stress: ", format(ord.pa$stress, digits = 4)), hjust = 0) +
theme_cowplot() + scale_color_brewer(palette = "BrBG", direction = 1) +
theme(panel.border = element_rect(colour = "black"))+
ggtitle("All Sites - distance data using Bray-Curtis")+
labs(x = "NMDS1", y = "NMDS2")
> Error in FUN(X[[i]], ...) : object 'Year' not found
However, when I remove the geom_segment and geom_text_repel code lines it fixes the problem and I am able to plot the graph...
Is anyone able to provide some insight into this issue?
Thank you!
I have code:
while self.cap.isOpened():
i += 1
length = int(self.cap.get(cv.CAP_PROP_FRAME_COUNT))
pbar.setValue(( i / length) * 100)
ret, frame = self.cap.read()
height = int( frame.shape[0] )
width = int( frame.shape[1] )
dim = (width, height)
frame = cv.resize(frame, dim, interpolation = cv.INTER_AREA)
org_frame = frame
if not ret:
continue
aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)
parameters = aruco.DetectorParameters_create()
corners, ids, _ = aruco.detectMarkers(frame, aruco_dict, parameters=parameters)
if np.all(ids != None):
for c in corners :
x1 = (c[0][0][0], c[0][0][1])
x2 = (c[0][1][0], c[0][1][1])
x3 = (c[0][2][0], c[0][2][1])
x4 = (c[0][3][0], c[0][3][1])
im_dst = frame
size = self.img_src.shape
pts_dst = np.array([x1, x2, x3, x4])
pts_src = np.array(
[
[0,0],
[size[1] - 1, 0],
[size[1] - 1, size[0] -1],
[0, size[0] - 1 ]
],dtype=float
);
h, status = cv.findHomography(pts_src, pts_dst)
temp = cv.warpPerspective(self.img_src.copy(), h, (org_frame.shape[1], org_frame.shape[0]))
cv.fillConvexPoly(org_frame, pts_dst.astype(int), 0, 16);
#cv.addWeighted(org_frame, 0.7, pts_dst.astype(int), 0.3,16, 0);
org_frame = cv.add(org_frame, temp)
self.out.write(org_frame)
cv.imshow('Test', org_frame)
else:
self.out.write(frame)
cv.imshow('Test', frame)
if cv.waitKey(1) & 0xFF == ord('q'):
break
It looks for a label in the frame and inserts my mask image there.
I'm interested in this line:
cv.fillConvexPoly(org_frame, pts_dst.astype(int), 0, 16);
Everything works, but when I insert a transparent image, it is filled with black, how can I add transparency?
Mask:
P.S
sorry for my English
I'm trying to implement RGB to HSV conversion from opencv in pure numpy using formula from here:
def rgb2hsv_opencv(img_rgb):
img_hsv = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV)
return img_hsv
def rgb2hsv_np(img_rgb):
assert img_rgb.dtype == np.float32
height, width, c = img_rgb.shape
r, g, b = img_rgb[:,:,0], img_rgb[:,:,1], img_rgb[:,:,2]
t = np.min(img_rgb, axis=-1)
v = np.max(img_rgb, axis=-1)
s = (v - t) / (v + 1e-6)
s[v==0] = 0
# v==r
hr = 60 * (g - b) / (v - t + 1e-6)
# v==g
hg = 120 + 60 * (b - r) / (v - t + 1e-6)
# v==b
hb = 240 + 60 * (r - g) / (v - t + 1e-6)
h = np.zeros((height, width), np.float32)
h = h.flatten()
hr = hr.flatten()
hg = hg.flatten()
hb = hb.flatten()
h[(v==r).flatten()] = hr[(v==r).flatten()]
h[(v==g).flatten()] = hg[(v==g).flatten()]
h[(v==b).flatten()] = hb[(v==b).flatten()]
h[h<0] += 360
h = h.reshape((height, width))
img_hsv = np.stack([h, s, v], axis=-1)
return img_hsv
img_bgr = cv2.imread('00000.png')
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
img_rgb = img_rgb / 255.0
img_rgb = img_rgb.astype(np.float32)
img_hsv1 = rgb2hsv_np(img_rgb)
img_hsv2 = rgb2hsv_opencv(img_rgb)
print('max diff:', np.max(np.fabs(img_hsv1 - img_hsv2)))
print('min diff:', np.min(np.fabs(img_hsv1 - img_hsv2)))
print('mean diff:', np.mean(np.fabs(img_hsv1 - img_hsv2)))
But I get big diff:
max diff: 240.0
min diff: 0.0
mean diff: 0.18085355
Do I missing something?
Also maybe it's possible to write numpy code more efficient, for example without flatten?
Also I have hard time finding original C++ code for cvtColor function, as I understand it should be actually function cvCvtColor from C code, but I can't find actual source code with formula.
From the fact that the max difference is exactly 240, I'm pretty sure that what's happening is in the case when both or either of v==r, v==g are simultaneously true alongside v==b, which gets executed last.
If you change the order from:
h[(v==r).flatten()] = hr[(v==r).flatten()]
h[(v==g).flatten()] = hg[(v==g).flatten()]
h[(v==b).flatten()] = hb[(v==b).flatten()]
To:
h[(v==r).flatten()] = hr[(v==r).flatten()]
h[(v==b).flatten()] = hb[(v==b).flatten()]
h[(v==g).flatten()] = hg[(v==g).flatten()]
The max difference may start showing up as 120, because of that added 120 in that equation. So ideally, you would want to execute these three lines in the order b->g->r. The difference should be negligible then (still noticing a max difference of 0.01~, chalking it up to some round off somewhere).
h[(v==b).flatten()] = hb[(v==b).flatten()]
h[(v==g).flatten()] = hg[(v==g).flatten()]
h[(v==r).flatten()] = hr[(v==r).flatten()]
I'm studding tensorflow and I wrote some code but it does not work good.
the data was downloaded from uci:http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
I want to find the client who will subscribe a term deposit,but the result matched is 0.
I use 3 layers neural network and sigmod for output.
My code is like this,please help me.
hidden_layer1 = 200
hidden_layer2 = 200
x = tf.placeholder(tf.float32,[None,16])
y = tf.placeholder(tf.float32,[None,1])
Weights_L1 = tf.Variable(tf.random_normal([16,hidden_layer1]))
biases_L1 = tf.Variable(tf.random_normal([1,hidden_layer1]))
Wx_plus_b_L1 = tf.matmul(x,Weights_L1) + biases_L1
L1=tf.nn.relu(Wx_plus_b_L1)
Weights_L2 = tf.Variable( tf.random_normal([hidden_layer1,1]))
biases_L2 = tf.Variable( tf.random_normal([1,1]))
Wx_plus_b_L2 = tf.matmul(L1,Weights_L2) + biases_L2
pred = tf.nn.sigmoid(Wx_plus_b_L2)
loss = tf.reduce_mean(tf.square(y-pred))
learning_rate=0.05
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
pred_correct = tf.equal(y,pred)
accuracy = tf.reduce_mean(tf.cast(pred_correct,tf.float32))
batch_num = 0
with tf.Session() as ss:
ss.run(tf.global_variables_initializer())
for step in range(500):
ss.run(train_step,feed_dict={x:bank_train_x,y:bank_train_y})
if step%100==0:
batch_num = batch_num +1
acc1 = ss.run(accuracy,feed_dict={x:bank_train_x,y:bank_train_y})
print("train acc"+ str(step) + ", " + str(acc1) +" , batch_num:" + str(batch_num))
#print(ss.run(learning_rate,feed_dict={global_:step}))
p = ss.run(pred,feed_dict={x:bank_train_x,y:bank_train_y})
acc2 = ss.run(accuracy,feed_dict={x:bank_test_x,y:bank_test_y})
print("test acc" + str(acc2))
def calc(pred,y):
l = y.shape[0]
a = 0
b=0
c=0
d=0
for i in range(l):
if (p[i] >0.5 and y[i] == 0):
a = a +1
elif (p[i] >0.5 and y[i] == 1):
b = b+1
elif (p[i] <0.5 and y[i] == 0):
c = c+1
elif (p[i] <0.5 and y[i] == 1):
d = d +1
print(a,b,c,d)
calc(p,bank_train_y)
#the result is 169 0 34959 4629