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')
Related
given below is the code that is available on the website as well as on the tutorial videohttps://www.youtube.com/watch?v=wa2ARoUUdU8&t=118s
import cv2
from cvzone.HandTrackingModule import HandDetector
import numpy as np
import math
import time
cap = cv2.VideoCapture(0)
detector = HandDetector(maxHands=1)
offset = 20
imgSize = 300
folder = "Data/C"
counter = 0
while True:
success, img = cap.read()
hands, img = detector.findHands(img)
if hands:
hand = hands[0]
x, y, w, h = hand['bbox']
imgWhite = np.ones((imgSize, imgSize, 3), np.uint8) * 255
imgCrop = img[y - offset:y + h + offset, x - offset:x + w + offset]
imgCropShape = imgCrop.shape
aspectRatio = h / w
if aspectRatio > 1:
k = imgSize / h
wCal = math.ceil(k * w)
imgResize = cv2.resize(imgCrop, (wCal, imgSize))
imgResizeShape = imgResize.shape
wGap = math.ceil((imgSize - wCal) / 2)
imgWhite[:, wGap:wCal + wGap] = imgResize
else:
k = imgSize / w
hCal = math.ceil(k * h)
imgResize = cv2.resize(imgCrop, (imgSize, hCal))
imgResizeShape = imgResize.shape
hGap = math.ceil((imgSize - hCal) / 2)
imgWhite[hGap:hCal + hGap, :] = imgResize
cv2.imshow("ImageCrop", imgCrop)
cv2.imshow("ImageWhite", imgWhite)
cv2.imshow("Image", img)
key = cv2.waitKey(1)
if key == ord("s"):
counter += 1
cv2.imwrite(f'{folder}/Image_{time.time()}.jpg', imgWhite)
print(counter)
there is a traceback error while running this code(probably problem in importing cvzone module)(screenshots attached)screenshot of error
I tried reinstalling the packages with the same version, used in the video.
How do I deal with this problem?
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
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'm creating a heatmap for a CNN as per this tutorial.
In the last part:
def create_patiens_cam(case, plane):
patient_id = case['id']
mri = case['mri']
folder_path = f'./CAMS/{plane}/{patient_id}/'
if os.path.isdir(folder_path):
shutil.rmtree(folder_path)
os.makedirs(folder_path)
os.makedirs(folder_path + 'slices/')
os.makedirs(folder_path + 'cams/')
params = list(mrnet.parameters())
weight_softmax = np.squeeze(params[-2].cpu().data.numpy())
num_slices = mri.shape[1]
global feature_blobs
feature_blobs = []
mri = mri.to(device)
logit = mrnet(mri)
size_upsample = (256, 256)
feature_conv = feature_blobs[0]
h_x = F.softmax(logit, dim=1).data.squeeze(0)
probs, idx = h_x.sort(0, True)
probs = probs.cpu().numpy()
idx = idx.cpu().numpy()
slice_cams = returnCAM(feature_blobs[-1], weight_softmax, idx[:1])
for s in tqdm_notebook(range(num_slices), leave=False):
slice_pil = (transforms
.ToPILImage()(mri.cpu()[0][s] / 255))
slice_pil.save(folder_path + f'slices/{s}.png',
dpi=(300, 300))
img = mri[0][s].cpu().numpy()
img = img.transpose(1, 2, 0)
heatmap = (cv2
.cvtColor(cv2.applyColorMap(
cv2.resize(slice_cams[s], (256, 256)),
cv2.COLORMAP_JET),
cv2.COLOR_BGR2RGB)
)
result = heatmap * 0.3 + img * 0.5
pil_img_cam = Image.fromarray(np.uint8(result))
pil_img_cam.save(folder_path + f'cams/{s}.png', dpi=(300, 300))
I have created a folder "CAMS" in my 'mrnet' folder. However when running this last code (in jupyter notebook) I get no errors but no png's are being created. Anyone has any idea what could be wrong or where I could look to see what's wrong as I get no errors?
FULL CODE:
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 13 21:54:40 2021
#author: GlaDOS
"""
import os
import io
import requests
from PIL import Image
from torchvision import models, transforms
from torch.autograd import Variable
from torch.nn import functional as F
import numpy as np
import cv2
import pdb
from matplotlib import pyplot as plt
import sys
sys.path.append('C:/Users/GlaDOS/mrnet')
import shutil
import torch
import model
from dataloader import MRDataset
from tqdm import tqdm_notebook
task = 'acl'
plane = 'sagittal'
prefix = 'sag'
model_name = [name for name in os.listdir('C:/Users/GlaDOS/mrnet/models/')
if (task in name) and
(plane in name) and
(prefix in name)][0]
is_cuda = torch.cuda.is_available()
device = torch.device("cuda" if is_cuda else "cpu")
mrnet = torch.load(f'C:/Users/GlaDOS/mrnet/models/{model_name}')
mrnet = mrnet.to(device)
_ = mrnet.eval()
dataset = MRDataset('C:/Users/GlaDOS/mrnet/data/',
task,
plane,
transform=None,
train=False)
loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=0,
drop_last=False)
def returnCAM(feature_conv, weight_softmax, class_idx):
size_upsample = (256, 256)
bz, nc, h, w = feature_conv.shape
slice_cams = []
for s in range(bz):
for idx in class_idx:
cam = weight_softmax[idx].dot(feature_conv[s].reshape((nc, h*w)))
cam = cam.reshape(h, w)
cam = cam - np.min(cam)
cam_img = cam / np.max(cam)
cam_img = np.uint8(255 * cam_img)
slice_cams.append(cv2.resize(cam_img, size_upsample))
return slice_cams
patients = []
for i, (image, label, _) in tqdm_notebook(enumerate(loader), total=len(loader)):
patient_data = {}
patient_data['mri'] = image
patient_data['label'] = label[0][0][1].item()
patient_data['id'] = '0' * (4 - len(str(i))) + str(i)
patients.append(patient_data)
acl = list(filter(lambda d: d['label'] == 1, patients))
def create_patiens_cam(case, plane):
patient_id = case['id']
mri = case['mri']
folder_path = f'C:/Users/GlaDOS/mrnet/cams/{plane}/{patient_id}/'
if os.path.isdir(folder_path):
shutil.rmtree(folder_path)
os.makedirs(folder_path)
os.makedirs(folder_path + 'slices/')
os.makedirs(folder_path + 'cams/')
params = list(mrnet.parameters())
weight_softmax = np.squeeze(params[-2].cpu().data.numpy())
num_slices = mri.shape[1]
global feature_blobs
feature_blobs = []
mri = mri.to(device)
logit = mrnet(mri)
size_upsample = (256, 256)
feature_conv = feature_blobs[0]
h_x = F.softmax(logit, dim=1).data.squeeze(0)
probs, idx = h_x.sort(0, True)
probs = probs.cpu().numpy()
idx = idx.cpu().numpy()
slice_cams = returnCAM(feature_blobs[-1], weight_softmax, idx[:1])
for s in tqdm_notebook(range(num_slices), leave=False):
slice_pil = (transforms
.ToPILImage()(mri.cpu()[0][s] / 255))
slice_pil.save(folder_path + f'slices/{s}.png',
dpi=(300, 300))
img = mri[0][s].cpu().numpy()
img = img.transpose(1, 2, 0)
heatmap = (cv2
.cvtColor(cv2.applyColorMap(
cv2.resize(slice_cams[s], (256, 256)),
cv2.COLORMAP_JET),
cv2.COLOR_BGR2RGB)
)
result = heatmap * 0.3 + img * 0.5
pil_img_cam = Image.fromarray(np.uint8(result))
pil_img_cam.save(folder_path + f'cams/{s}.png', dpi=(300, 300))
Use seaborn:
import seaborn as sns
sns_plot.savefig('output.png')
I have a folder of images and I want to crop them in a circular shape.
This is the original image:
The result that I want is this:
My code is:
import os
import glob
from PIL import Image, ImageDraw, ImageFilter
import numpy as np
def mask_circle_solid(pil_img, background_color, blur_radius, offset=0):
background = Image.new(pil_img.mode, pil_img.size, background_color)
offset = blur_radius * 2 + offset
mask = Image.new("L", pil_img.size, 0)
draw = ImageDraw.Draw(mask)
draw.ellipse((offset, offset, pil_img.size[0] - offset, pil_img.size[1] - offset), fill=255)
mask = mask.filter(ImageFilter.GaussianBlur(blur_radius))
return Image.composite(pil_img, background, mask)
def mask_circle_transparent(pil_img, blur_radius, offset=0):
offset = blur_radius * 2 + offset
mask = Image.new("L", pil_img.size, 0)
draw = ImageDraw.Draw(mask)
draw.ellipse((offset, offset, pil_img.size[0] - offset, pil_img.size[1] - offset), fill=255)
mask = mask.filter(ImageFilter.GaussianBlur(blur_radius))
result = pil_img.copy()
result.putalpha(mask)
return result
def crop_max_square(pil_img):
return crop_center(pil_img, min(pil_img.size), min(pil_img.size))
def crop_center(pil_img, crop_width, crop_height):
img_width, img_height = pil_img.size
return pil_img.crop(((img_width - crop_width) // 2,
(img_height - crop_height) // 2,
(img_width + crop_width) // 2,
(img_height + crop_height) // 2))
im = []
for f in glob.iglob("./*.jpg"):
im.append(np.asarray(Image.open(f)))
thumb_width = 150
im = np.array(im)
list_files = os.listdir(".")
list_files.sort()
list_files.remove("Test.py")
list_files.remove(".DS_Store")
for i in range(0,len(im)):
im_square[i] = crop_max_square(im[i]).resize((thumb_width, thumb_width), Image.LANCZOS)
im_thumb[i] = mask_circle_transparent(im_square[i], 4)
im_thumb[i].save(list_files[i])
I have used the functions from this article:
Generate square or circular thumbnail images with Python, Pillow
But I get this error:
line 30, in crop_max_square
return crop_center(pil_img, min(pil_img.size), min(pil_img.size))
TypeError: 'int' object is not iterable
I'm not sure if you really need to use numpy to load the files, but you can do what the "Sample code for batch processing" of the page you shared does:
import os
import glob
from PIL import Image, ImageDraw, ImageFilter
def mask_circle_solid(pil_img, background_color, blur_radius, offset=0):
background = Image.new(pil_img.mode, pil_img.size, background_color)
offset = blur_radius * 2 + offset
mask = Image.new("L", pil_img.size, 0)
draw = ImageDraw.Draw(mask)
draw.ellipse((offset, offset, pil_img.size[0] - offset, pil_img.size[1] - offset), fill=255)
mask = mask.filter(ImageFilter.GaussianBlur(blur_radius))
return Image.composite(pil_img, background, mask)
def mask_circle_transparent(pil_img, blur_radius, offset=0):
offset = blur_radius * 2 + offset
mask = Image.new("L", pil_img.size, 0)
draw = ImageDraw.Draw(mask)
draw.ellipse((offset, offset, pil_img.size[0] - offset, pil_img.size[1] - offset), fill=255)
mask = mask.filter(ImageFilter.GaussianBlur(blur_radius))
result = pil_img.copy()
result.putalpha(mask)
return result
def crop_max_square(pil_img):
return crop_center(pil_img, min(pil_img.size), min(pil_img.size))
def crop_center(pil_img, crop_width, crop_height):
img_width, img_height = pil_img.size
return pil_img.crop(((img_width - crop_width) // 2,
(img_height - crop_height) // 2,
(img_width + crop_width) // 2,
(img_height + crop_height) // 2))
im = []
jpgs_files_path = "./" # Replace the "./" path by the path where the .jpg images are.
files = glob.glob(os.path.join(jpgs_files_path, '*.jpg'))
thumb_width = 150
for f in files:
im = Image.open(f)
im_thumb = crop_max_square(im).resize((thumb_width, thumb_width), Image.LANCZOS)
im_thumb = mask_circle_transparent(im_thumb, 4)
ftitle, fext = os.path.splitext(os.path.basename(f))
im_thumb.save(os.path.join("./dstdir/", ftitle + '_thumbnail.png'), quality=95)
As JPG does not support transparency you need to discard the Alpha Channel or save as something that supports transparency like PNG.