I want to overlay a given image above a list of images and copy all images that fit to one folder what is the best programation language should I use?
Here is example of images I want to overlay the red hat and copy all the images with red hat to ine folder
here is image :
I tried with Python many exmaple but it didn't work
I tired this I found it in this website but it still didn't work:
import cv2
import numpy as np
from matplotlib import pyplot as plt
from PIL import Image
import os, errno
threshold = 0.8 #set threshold
resultsDirectory = 'results'
sourceDirectory = os.fsencode(r'C:\Users\Amir\Desktop\Nouveau dossier (2)\rrr')
templateDirectory = os.fsencode(r'C:\Users\Amir\Desktop\Nouveau dossier (2)\3.png')
detectedCount = 0
for file in os.listdir(sourceDirectory):
filename = os.fsdecode(file)
if filename.endswith(".jpg") or filename.endswith(".png"):
print (filename)
img_rgb = cv2.imread(r'C:\Users\Amir\Desktop\Nouveau dossier (2)\rrr'+filename)
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
for templateFile in os.listdir(templateDirectory):
templateFilename = os.fsdecode(templateFile)
if filename.endswith(".jpg") or filename.endswith(".png"):
template = cv2.imread(r'C:\Users\Amir\Desktop\Nouveau dossier (2)\3.png'+templateFilename,0)
w, h = template.shape[::-1]
res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
loc = np.where( res >= threshold)
if (len(loc[0])):
detectedCount = detectedCount + 1
for pt in zip(*loc[::-1]):
cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)
cv2.imwrite(resultsDirectory+'/res_'+filename+'.png',img_rgb)
print ('/res_'+filename+'.png'+' saved')
# break
print ('detected positive ' + str(detectedCount))
continue
else:
continue
Related
I am trying to write a code where after matching with a given template, the detected part of that frame becomes the template for the next frame.
temp = "image.png"
while True:
try:
_, frame = cap.read()
copy = frame.copy()
w,h=temp.shape[:-1]
res=cv2.matchTemplate(frame,temp,cv2.TM_CCOEFF_NORMED)
threshold=0.75
#try:
loc=np.where(res>=threshold)
print(len(loc))
for pt in zip(*loc[::-1]):
#cv2.rectangle(img,pt,(pt[0]+w,pt[1]+h),(0,255,255),2)
point = pt
cropped_image = copy[point[1]:point[1]+h, point[0]:point[0]+w]
temp = cropped_image #update the template
but after writing this code the template matching is going in totally wrong direction, even though if i remove the "temp = cropped_image" then the cropped_image is actually good.
You can find x,y,w,h of the matched image with cv2.minMaxLoc()
import cv2
src = cv2.imread("source.png", cv2.IMREAD_GRAYSCALE)
templit = cv2.imread("initial_template.png", cv2.IMREAD_GRAYSCALE)
result = cv2.matchTemplate(src, templit, cv2.TM_SQDIFF_NORMED)
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(result)
x, y = minLoc
h, w = templit.shape
cropped_img = src[y: y + h, x: x + w]
//do template matching again with cropped_image
import cv2
import os
os.sys.path
from os import listdir
from PIL import Image
from numpy import asarray
from numpy import expand_dims
import pickle
folder="data_test_deteksi"
database = {}
for filename in listdir(folder):
path = folder + filename
gbr1 = cv2.imread(folder + filename)
wajah = HaarCascade.detectMultiScale(gbr1,1.1,4)
if len(wajah)>0:
x1, y1, width, height = wajah[0]
else:
x1, y1, width, height = 1, 1, 10, 10
x1, y1 = abs(x1), abs(y1)
x2, y2 = x1 + width, y1 + height
gbr = cv2.cvtColor(gbr1, cv2.COLOR_BGR2RGB)
gbr = Image.fromarray(gbr) # konversi dari OpenCV ke PIL
gbr_array = asarray(gbr)
face = gbr_array[y1:y2, x1:x2]
face = Image.fromarray(face)
face = face.resize((160,160))
face = asarray(face)
face = face.astype('float32')
mean, std = face.mean(), face.std()
face = (face - mean) / std
face = expand_dims(face, axis=0)
signature = MyFaceNet.predict(face)
database[os.path.splitext(filename)[0]]=signature
There is nothing named as HaarCascade in opencv, you need to use cv2.CascadeClassifier. First download haarcascade xml file and then pass the path of that file like this
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
i used this code below to extract patches from a image.
extract code:
import os
import glob
from PIL import Image
Image.MAX_IMAGE_PIXELS = None # to avoid image size warning
imgdir = "/path/to/image/folder"
filelist = [f for f in glob.glob(imgdir + "**/*.png", recursive=True)]
savedir = "/path/to/image/folder/output"
start_pos = start_x, start_y = (0, 0)
cropped_image_size = w, h = (256, 256)
for file in filelist:
img = Image.open(file)
width, height = img.size
frame_num = 1
for col_i in range(0, width, w):
for row_i in range(0, height, h):
crop = img.crop((col_i, row_i, col_i + w, row_i + h))
name = os.path.basename(file)
name = os.path.splitext(name)[0]
save_to= os.path.join(savedir, name+"_{:03}.png")
crop.save(save_to.format(frame_num))
frame_num += 1
Now i want to reconstruct this imagem from all those patches extracted before, i've tried 2 diffenret codes
so my DB is 120x256x256x3 extracted patches, theres 120 patches to fit in 3840x2048 shape..:
patches = []
for directory_path in glob.glob('D:\join_exemplo'):
for img_path in glob.glob(os.path.join(directory_path, "*.png")):
img = cv2.imread(img_path,1)
patches.append(img)
input_patches = np.array(patches)
first i've tried sklearn.feature_extraction.image importing reconstruct_from_patches_2d, but got a black image:
reconstruct = reconstruct_from_patches_2d(input_patches, input_image)
reconstruct = reconstruct.astype(np.uint8)
Image.fromarray(reconstruct, 'RGB').save(r'D:\join_exemplo\re\re3.png')
also tried, this below but got a grayscale tone pattern image
input_image = (3840,2048,3)
reconstructed_arr = np.zeros(shape=(3840,2048,3))
#%%
>>> step = 256
>>> for x in range(img.shape[0]):
for y in range(img.shape[1]):
x_pos, y_pos = x * step, y * step
reconstructed_arr[x_pos:x_pos + 512, y_pos:y_pos + 512] = img[x, y, 0, ...]
>>> (input_image == reconstructed_arr).all()
True
cv2.imwrite(r'D:\join_exemplo\re\re.png',reconstructed_arr)
Can someone see whats wrong? sorry about my bad english
I have written two scripts main_program.py and detector.py to do perform face detection and recognition. I first train the classifier then use it recognize faces. But the training isn't happening because the program exits with an error.
here is the code for both the scripts:
For main_program.py :-
import cv2
import os
import numpy as np
import detector as dec
test_img = cv2.imread(r'C:\Users\JasonPC\Desktop\FaceDetection\Test Images\j1.jpg')
faces_detected, gray_img = dec.faceDetect(test_img)
print('Faces Detected: ', faces_detected)
faces, faceID = dec.labels_for_training_data(r'C:\Users\JasonPC\Desktop\FaceDetection\Resources')
face_recognizer = dec.train_classifier(faces, faceID)
name = {0:'Obama', 1:'Trump'}
for faces in faces_detected:
(x, y, w, h) = face
roi_gray = gray_img[y : y + h, x : x + h]
label, confidence = face_recognizer.predict(roi_gray)
confidence('confidence: ', confidence)
print('label: ', label)
dec.draw_rect(test_img, face)
predicted_name = name[label]
dec.put_text(test_img, predicted_name, x, y)
result_img = cv2.resize(test_img, (200, 200))
cv2.imshow('Face Detection', result_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
And here the code for detector.py
import cv2
import os
import numpy as np
def faceDetect(img):
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
classifier = cv2.CascadeClassifier(r'C:\Users\JasonPC\Documents\CodeVault\Python\FaceDetection\Classifiers\haarcascade_frontalface_alt.xml')
# classifier = cv2.CascadeClassifier(r'C:\Users\JasonPC\Documents\CodeVault\Python\FaceRecognition\Classifier.xml')
# val = classifier.load(r'C:\Users\JasonPC\Documents\CodeVault\Python\FaceRecognition\Classifier.xml')
faces = classifier.detectMultiScale(gray_img, scaleFactor = 1.5, minNeighbors = 5 )
return faces, gray_img
def labels_for_training_data(directory):
faces = []
faceID = []
for path, subdirnames, filenames in os.walk(directory):
for filename in filenames:
if filename.startswith("."):
print('Skipping system file...')
continue
id = os.path.basename(path)
img_path = os.path.join(path, filename)
print('img_path: ', img_path)
print('id: ', id)
test_img = cv2.imread(img_path)
if test_img is None:
print('Image is not loaded properly')
continue
faces_rect, gray_img = faceDetect(test_img)
if len(faces_rect) != 1:
continue # since multiple faces has been detected in an single image
(x, y , w, h) = faces_rect[0]
roi_gray = gray_img[y : y + 1, x : x + h]
faces.append(roi_gray)
faceID.append(int(id))
return faces, faceID
def train_classifier(faces, faceID):
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer.train(faces, np.array(faceID))
return face_recognizer
def draw_rect(test_img, face):
(x, y , w, h) = face
cv2.rectangle(test_img, (x, y), (x + w, y + h), (255, 0, 0), thickness = 5)
def put_text(test_img, text, x, y):
cv2.putText(test_img, text, (x, y), cv2.FONT_HERSHEY_DUPLEX, 5, (255,0, 0), 6)
But I am stuck with an error!
Traceback (most recent call last):
File "c:/Users/JasonPC/Desktop/FaceDetection/main_program.py", line
29, in <module>
face_recognizer = dec.train_classifier(faces, faceID)
File "c:\Users\JasonPC\Desktop\FaceDetection\detector.py", line 44,
in train_classifier
face_recognizer.train(faces, np.array(faceID))
cv2.error: OpenCV(4.3.0) C:\projects\opencv-python\opencv\modules\core\src\matrix.cpp:235: error: (-215:Assertion failed) s >= 0 in function 'cv::setSize'
Please help me debug this error!
many many thanks!
I am trying to do character detect for handwriting letters.
Upon recognizing itself, I use tesseract or opencv SVM, and works fine till now.
Everything works fine for segment letters until I hit those connected ones.
I use the following code to segment letters:
# -*- coding: utf-8 -*-
import numpy as np
import cv2
# from matplotlib import pyplot as plt
from os.path import dirname, join, basename
import sys
from glob import glob
trainpic=[]
targetdir = dirname(__file__)+'tmporigin'
#print glob(join(dirname(__file__)+'/cat','*.jpg'))
img = {}
debug = True
a_num = 0
for fn in glob(join(targetdir, '*')):
filename = basename(fn)
trainpic.append(cv2.imread(fn, 0))
img_rgb = cv2.imread(fn)
img = cv2.imread(fn, 0)
image_close = cv2.morphologyEx(img_rgb, cv2.MORPH_CLOSE, np.ones((1, 7), np.uint8))
#if debug:
# cv2.imshow('morphology', image_close)
# key = cv2.waitKey(0)
_, contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
samples = np.empty((0, 100))
responses = []
# keys = [i for i in range(48, 58)]
tmp_list = []
tmpcount = 0
for cnt in contours:
print 'contourarea:%s' % cv2.contourArea(cnt)
if cv2.contourArea(cnt) > 130: # 50 300
[x, y, w, h] = cv2.boundingRect(cnt)
print 'boundingRect width:%s' % w
print 'boundingRect height:%s' % h
if h > 28:
cv2.rectangle(img_rgb, (x, y), (x+w, y+h), (0, 0, 255), 2)
roi = img[y:y+h, x:x+w]
roismall = cv2.resize(roi, (45, 55))
if debug:
cv2.imshow('norm', img_rgb)
key = cv2.waitKey(0)
# tmp_list.append(roi)
tmpfilename = fn if tmpcount == 0 else fn.rsplit('.', 1)[0] + '_' + str(tmpcount) + '.png'
cv2.imwrite(tmpfilename, roismall)
tmpcount += 1
else:
print 'contarea less, skip...'
# print img[num].shape
a_num += 1
print '%s images processed' % a_num
So, its fine to handle letters with inter space like this guy(split to D and B):
However, failed to segment connected letters like this:
I googled a lot for connected letters and find related links like these two:
enter link description here
enter link description here
I tried a lot, e.g. morphology dilate, erode, open, close, watershed .etc but didn't fix my problem.
I use opencv 3.2.0 and python 2.7.10 upon my Ubuntu desktop.
Any suggestion is great appreciated.
Thanks.
Wesley