顔の画像だけを保存したいです

Question

顔の画像だけを出力して保存したいです。
今はパソコンやイスも認識して保存してしまいます。

from ctypes import *
import math
import random
import cv2
import numpy as np

def sample(probs):
s = sum(probs)
probs = [a/s for a in probs]
r = random.uniform(0, 1)
for i in range(len(probs)):
r = r - probs[i]
if r <= 0:
return i
return len(probs)-1

def c_array(ctype, values):
arr = (ctype*len(values))()
arr[:] = values
return arr

def array_to_image(arr):
arr = arr.transpose(2,0,1)
c = arr.shape[0]
h = arr.shape[1]
w = arr.shape[2]
arr = (arr/255.0).flatten()
data = c_array(c_float, arr)
im = IMAGE(w,h,c,data)
return im

class BOX(Structure):
fields = [("x", c_float),
("y", c_float),
("w", c_float),
("h", c_float)]

class DETECTION(Structure):
fields = [("bbox", BOX),
("classes", c_int),
("prob", POINTER(c_float)),
("mask", POINTER(c_float)),
("objectness", c_float),
("sort_class", c_int)]

class IMAGE(Structure):
fields = [("w", c_int),
("h", c_int),
("c", c_int),
("data", POINTER(c_float))]

class METADATA(Structure):
fields = [("classes", c_int),
("names", POINTER(c_char_p))]

#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
lib = CDLL("libdarknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int

predict = lib.network_predict
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)

set_gpu = lib.cuda_set_device
set_gpu.argtypes = [c_int]

make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE

get_network_boxes = lib.get_network_boxes
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
get_network_boxes.restype = POINTER(DETECTION)

make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p]
make_network_boxes.restype = POINTER(DETECTION)

free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]

free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]

network_predict = lib.network_predict
network_predict.argtypes = [c_void_p, POINTER(c_float)]

reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]

load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p

do_nms_obj = lib.do_nms_obj
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]

do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]

free_image = lib.free_image
free_image.argtypes = [IMAGE]

letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGE

load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATA

load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE

rgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]

predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)

def classify(net, meta, im):
out = predict_image(net, im)
res = []
for i in range(meta.classes):
res.append((meta.names[i], out[i]))
res = sorted(res, key=lambda x: -x[1])
return res

def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):

im = load_image(image, 0, 0)

tmp_arr = np.ndarray.copy(image)

im = array_to_image(image)
rgbgr_image(im)
num = c_int(0)
pnum = pointer(num)
predict_image(net, im)
dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
num = pnum[0]
if (nms): do_nms_obj(dets, num, meta.classes, nms);

res = []
for j in range(num):
    for i in range(meta.classes):
        if dets[j].prob[i] > 0:
            b = dets[j].bbox
            res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
res = sorted(res, key=lambda x: -x[1])

free_image(im)

free_detections(dets, num)
return res

if name == "main":
#net = load_net("cfg/densenet201.cfg", "/home/pjreddie/trained/densenet201.weights", 0)
#im = load_image("data/wolf.jpg", 0, 0)
#meta = load_meta("cfg/imagenet1k.data")
#r = classify(net, meta, im)
#print r[:10]

net = load_net("cfg/yolov3.cfg", "yolov3.weights", 0)
meta = load_meta("cfg/coco.data")

#cap = cv2.VideoCapture(1)
#image_path = "home/python/Darknet/photo.jpg"
#while( cap.isOpened() ):

#while( image_path.isOpened() ):

ret, arr=cap.read()

ret

cv2.imshow('Capture',arr)

key=cv2.waitKey(1)

#if key & 0x00FF == ord('q'):

break

r = detect2(net, meta, im)

#arr = cv2.imread("data/dog.jpg")
arr = cv2.imread("data/photo.jpg")
#arr = cv2.imread("data/1photo.jpg")
#arr = cv2.imread("data/2photo.jpg")
#arr = cv2.imread("data/25-2.jpg")
#arr = cv2.imread("image_path")
#arr = cv2.imread("data/photo.jpg")
r = detect(net, meta,arr)
print len(r)

print r

count=0
for i in range(len(r)):

print r[i][0], r[i][1]

 (x,y,w,h) = r[i][2]
        
 print("Coordinate=", x, y, w, h)
 x1=round(x, 1)
 y1=round(y, 1)
 w1=round(w, 1)
 h1=round(h, 1)

arr2 = arr[x1 :y1 ,w1:h1]

arr2 = arr[int(x1-w1/2):int(y1-h1/2),int(x1+w1/2):int(y1+h1/2)]

arr2 = arr[int(x-w/2):int(y-h/2),int(x+w/2):int(y+h/2)]

arr2 = arr[int(x+w/2):int(y+h/2),int(x-w/2):int(y-h/2)]

arr2 = arr[int(x+w/2):int(y+h/2),int(x-w/3):int(y-h/2)]

 arr2 = arr[int(y-h/2):int(y+h/2),int(x-w/2):int(x+w/2)]
 color = (0,255,255)
        
 pen_w = 2


        

cv2.rectangle(arr, (int(x-w/2), int(y-h/2)), (int(x+w/2), int(y+h/2)), color, thickness=pen_w)

cv2.putText(arr,r[i][0]+'{:.2f}'.format(r[i][1]),(int(x-w/2),int(y-h/+20.0)),cv2.FONT_HERSHEY_PLAIN,1.5,(0,255,255),2,cv2.LINE_AA)

 ret=cv2.imwrite(str(count)+'result'+'.jpg',arr2)

ret=cv2.imwrite('result'+str(count)+'.jpg',arr)

ret=cv2.imwrite(image_path+'result'+str(count)+'.jpg',img)

 if not ret:
    print('Failed to write image.')

 count+=1

imread

 print(count)      

cv2.imshow('Detected',arr)

 key=cv2.waitKey(1)
    
 if key & 0x00FF == ord('q'):
        
    break

#image_path.release()
#cap.release()

cv2.destroyAllWindows()

Comments

[Q71]

少なくとも、他の人に読んでもらえる文を書いて下さい。