I'm trying to make a face recognition program but the problem is the face encoding shape of some encodings are bigger than the others and thus im getting the error
ValueError: setting an array element with a sequence.
Here's my code to generate the encodings
class FaceEncoder():
def __init__(self, files, singleton = False, model_path='./models/lbpcascade_animeface.xml', scale_factor=1.1, min_neighbours=1):
self.singleton = singleton
self.files = files
self.model = model_path
self.scale_factor = scale_factor
self.min_neighbours = min_neighbours
def encode(self, singleton=False):
if self.singleton == False:
encodings = []
labels = []
for file in self.files:
cascade = cv2.CascadeClassifier(self.model)
image = cv2.imread(file)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
faces = cascade.detectMultiScale(rgb, self.scale_factor, self.min_neighbours)
if len(faces) > 0:
print('Found face in '+file)
encodings.append(faces.flatten())
labels.append(file.split('/')[2])
else:
print('Couldnt find face in '+file)
return encodings, labels
Here are some of the encodings
[204 96 211 211]
[525 168 680 680]
[205 11 269 269]
[ 165 31 316 316 1098 181 179 179]
[ 113 422 1371 1371]
[ 71 86 183 183]
[209 19 33 33 88 27 60 60 133 80 65 65 68 117 52 52]
[117 77 149 149]
[ 63 77 284 284]
[370 222 490 490]
[433 112 114 114 183 98 358 358]
[ 44 35 48 48 192 34 48 48]
[210 82 229 229]
[429 90 153 153]
[318 50 174 174 118 142 120 120]
you should not put several found rects into the same list entry.
if there are many faces found, put each on its own row, and add a label per face found (not per image)
then, what you have now, are NOT "encodings", just mere boxes / rectangles.
read up on how to get real encodings (facenet, spherenet ?), then you need to:
crop the face region fom the image
resize it to the nn input size (e.g. 96x96)
run it through the nn to receive the encoding
save that along with a label to a db/list
I have a labeled image of detected particles and a dataframe with the corresponding area of each labeled particle. What I want to do is filter out every particle on the image with an area smaller than a specified value.
I got it working with the example below, but I know there must be a smarter and especially faster way.
For example skipping the loop by comparing the image with the array.
Thanks for your help!
Example:
labels = df["label"][df.area > 5000].to_numpy()
mask = np.zeros(labeled_image.shape)
for label in labels:
mask[labeled_image == label] = 1
Dataframe:
label centroid-0 centroid-1 area
0 1 15 3681 191
1 2 13 1345 390
2 3 43 3746 885
3 4 32 3616 817
4 5 20 4250 137
... ... ... ...
3827 3828 4149 1620 130
3828 3829 4151 852 62
3829 3830 4155 330 236
3830 3831 4157 530 377
3831 3832 4159 3975 81
You can use isin to check equality to several labels. The resulting boolean array can be directly used as the mask after casting to the required type (e.g. int):
labels = df.loc[df.area.gt(5000), 'label']
mask = np.isin(labeled_image, labels).astype(int)
I'm trying to read text from an image, using OpenCV and Pytesseract, but with poor results.
The image I'm interested in reading the text is: https://www.lubecreostorepratolapeligna.it/gb/img/logo.png
This is the code I am using:
pytesseract.pytesseract.tesseract_cmd = r'D:\Program Files\pytesseract\tesseract.exe'
image = cv2.imread(path_to_image)
# converting image into gray scale image
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('grey image', gray_image)
cv2.waitKey(0)
# converting it to binary image by Thresholding
# this step is require if you have colored image because if you skip this part
# then tesseract won't able to detect text correctly and this will give incorrect result
threshold_img = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# display image
cv2.imshow('threshold image', threshold_img)
# Maintain output window until user presses a key
cv2.waitKey(0)
# Destroying present windows on screen
cv2.destroyAllWindows()
# now feeding image to tesseract
text = pytesseract.image_to_string(threshold_img)
print(text)
The result of the execution is : ["cu"," ","LUBE"," ","STORE","PRATOLA PELIGNA"]
But the result should be these 7 words: ["cucine", "LUBE", "CREO", "kitchens", "STORE", "PRATOLA", "PELIGNA"]
Is there anyone who could help me to solve this problem ?
Edit, 17.12.2020: Using preprocessing now it recognizes all, but the "O" in CREO. See the stages in ocr8.py. Then ocr9.py demonstrates (but not automated yet) finding the lines of text by the coordinates returned from pytesseract.image_to_boxes(), approcimate size of the letters and inter-symbol distance, then extrapolating one step ahead and searching for a single character (--psm 8).
It happened that Tesseract had actually recognized the "O" in CREO, but it read it as ♀, probably confused by the little "k" below etc.
Since it is a rare and "strange"/unexpected symbol, it could be corrected - replaced automatically (see the function Correct()).
There is a technical detail: Tesseract returns the ANSI/ASCII symbol 12, (0x0C) while the code in my editor was in Unicode/UTF-8 - 9792. So I coded it inside as chr(12).
The latest version: ocr9.py
You mentioned that PRATOLA and PELIGNA have to be given sepearately - just split by " ":
splitted = text.split(" ")
RECOGNIZED
CUCINE
LUBE
STORE
PRATOLA PELIGNA
CRE [+O with correction and extrapolation of the line]
KITCHENS
...
C 39 211 47 221 0
U 62 211 69 221 0
C 84 211 92 221 0
I 107 211 108 221 0
N 123 211 131 221 0
E 146 211 153 221 0
L 39 108 59 166 0
U 63 107 93 166 0
B 98 108 128 166 0
E 133 108 152 166 0
S 440 134 468 173 0
T 470 135 499 173 0
O 500 134 539 174 0
R 544 135 575 173 0
E 580 135 608 173 0
P 287 76 315 114 0
R 319 76 350 114 0
A 352 76 390 114 0
T 387 76 417 114 0
O 417 75 456 115 0
L 461 76 487 114 0
A 489 76 526 114 0
P 543 76 572 114 0
E 576 76 604 114 0
L 609 76 634 114 0
I 639 76 643 114 0
G 649 75 683 115 0
N 690 76 722 114 0
A 726 76 764 114 0
C 21 30 55 65 0
R 62 31 93 64 0
E 99 31 127 64 0
K 47 19 52 25 0
I 61 19 62 25 0
T 71 19 76 25 0
C 84 19 89 25 0
H 96 19 109 25 0
E 113 19 117 25 0
N 127 19 132 25 0
S 141 19 145 22 0
These are from getting "boxes".
Initial message:
I guess that for the area where "cucine" is, an adaptive threshold may segment it better or maybe applying some edge detection first.
Kitchens seems very small, what about trying to enlarge that area/distance.
For the CREO, I guess it's confused with the big and small size of adjacent captions.
For the "O" in creo, you may apply dilate in order to close the gap of the "O".
Edit: I played a bit, but without Tesseract and it needs more work. My goal was to make the letters more contrasting, may need some of these processings to be applied selectively only on the Cucine, maybe applying the recognition in two passes. When getting those partial words "Cu", apply adaptive threshold etc. (below) and OCR on a top rectangle around "CU..."
Binary Threshold:
Adaptive Threshold, Median blur (to clean noise) and invert:
Dilate connects small gaps, but it also destroys detail.
import cv2
import numpy as np
#pytesseract.pytesseract.tesseract_cmd = r'D:\Program Files\pytesseract\tesseract.exe'
path_to_image = "logo.png"
#path_to_image = "logo1.png"
image = cv2.imread(path_to_image)
h, w, _ = image.shape
w*=3; h*=3
w = (int)(w); h = (int) (h)
image = cv2.resize(image, (w,h), interpolation = cv2.INTER_AREA) #Resize 3 times
# converting image into gray scale image
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('grey image', gray_image)
cv2.waitKey(0)
# converting it to binary image by Thresholding
# this step is require if you have colored image because if you skip this part
# then tesseract won't able to detect text correctly and this will give incorrect result
#threshold_img = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# display image
threshold_img = cv2.adaptiveThreshold(gray_image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,13,3) #cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11,2)[1]
cv2.imshow('threshold image', threshold_img)
cv2.waitKey(0)
#threshold_img = cv2.GaussianBlur(threshold_img,(3,3),0)
#threshold_img = cv2.GaussianBlur(threshold_img,(3,3),0)
threshold_img = cv2.medianBlur(threshold_img,5)
cv2.imshow('medianBlur', threshold_img)
cv2.waitKey(0)
threshold_img = cv2.bitwise_not(threshold_img)
cv2.imshow('Invert', threshold_img)
cv2.waitKey(0)
#kernel = np.ones((1, 1), np.uint8)
#threshold_img = cv2.dilate(threshold_img, kernel)
#cv2.imshow('Dilate', threshold_img)
#cv2.waitKey(0)
cv2.imshow('threshold image', thrfeshold_img)
# Maintain output window until user presses a key
cv2.waitKey(0)
# Destroying present windows on screen
cv2.destroyAllWindows()
# now feeding image to tesseract
text = pytesseract.image_to_string(threshold_img)
print(text)
I have file having EncodedPixels mask of different size
1: I want to convert these EncodedPixels in binary and resize all into 1024 and then again convert in to EncodedPixels.
Explanation:
In file there is image-Mask in Encoded Pixels form, and images have different dimensions (5000x5000, 260x260 etc) So I resize all images in to 1024x1024, Now I want to resize each image-mask according to image 1024x1024.
I my mind there is only one possible solution (might be more available) to resize mask is first we need to convert run length encoding pixel in to binary and then we are able to resize mask easily.
File Link: link here
This code will use to resize binary mask.
from PIL import Image
import numpy as np
pil_image = Image.fromarray(binary_mask)
pil_image = pil_image.resize((new_width, new_height), Image.NEAREST)
resized_binary_mask = np.asarray(pil_image)
Encoded Pixels Example
['6068157 7 6073371 20 6078584 34 6083797 48 6089010 62 6094223 72 6099436 76 6104649 80
6109862 85 6115075 89 6120288 93 6125501 98 6130714 102 6135927 106 6141140 111 6146354 114 6151567 118 6156780 123 6161993 127 6167206 131 6172419 136 6177632 140 6182845 144 6188058 149 6193271 153 6198484 157 6203697 162 6208910 166 6214124 169 6219337 174 6224550 178 6229763 182 6234976 187 6240189 191 6245402 195 6250615 200 6255828 204 6261041 208 6266254 213 6271467 218 6276680 224 6281893 229 6287107 233 6292320 238 6297533 244 6302746 249 6307959 254 6313172 259 6318385 265 6323598 270 6328811 275 6334024 280 6339237 286 6344450 291 6349663 296 6354877 300 6360090 306 6365303 311 6370516 316 6375729 322 6380942 327 6386155 332 6391368 337 6396581 343 6401794 348 6407007 353 6412220 358 6417433 364 6422647 368 6427860 373 6433073 378 6438286 384 6443499 389 6448712 394 6453925 399 6459138 405 6464351 410 6469564 415 6474777 420 6479990 426 17204187 78 17208797 227 17209412 56 17214025 203 17214637 34 17219253 179 17219862 11 17224481 155 17229709 131 17234937 107 17240165 83 17245393 60 17250621 36 17255849 12']
I need to change the names of a subset of columns in a dataframe from whatever number they are to that number plus a string suffix. I know there is a function to add a suffix, but it doesn't seem to work on just indices.
I create a list with all the column indices in it, then run a loop that, for each item in that list, it renames the dataframe column that matches the list item to the same number, plus the suffix string.
if scalename == "CDR":
print(scaledf.columns.tolist())
oldCols = scaledf.columns[7:].tolist()
for f in range(len(oldCols)):
changeCol = int(oldCols[f])
print(changeCol)
scaledf.rename(columns = {changeCol:scalename + str(changeCol)})
print(scaledf.columns)
This doesn't work.
The code will print out the column names, and prints out every item, but it does not rename the columns. It doesn't throw errors, it just doesn't work. I've tried variation after variation, and gotten all kinds of other errors, but this error-free code does nothing. It just runs, and doesn't rename anything.
Any help would be seriously appreciated! Thank you.
Adding sample of list:
45
52
54
55
59
60
61
66
67
68
69
73
74
75
80
81
82
94
101
103
104
108
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115
116
117
129
136
138
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143
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145
150
151
157
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171
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185
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213
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236
Try this:
scaledf = scaledf.rename(columns=lambda c:scalename + str(c) if c in oldCols else c)