def grade_2(image_part):
correct = 0
questions = get_questions(image_part)
for i in range(15):
if debug:
show_image(
questions[i]
) #------------------------------------------------------------------------------------------
choices_list = get_choices_2(questions[i])
correct += grade_question(choices_list, FINAL_ANSWER_2[i])
if debug:
print correct #------------------------------------------------------------------------------------------
return correct
def grade_all(warped):
total_grade = 0
try:
gray = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
except:
thresh = cv2.threshold(warped, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
if debug:
show_image(
thresh
) #------------------------------------------------------------------------------------------
first_part, second_part, third_part = crop_image(thresh)
total_grade = grade_1(first_part) + grade_2(second_part) + grade_3(
third_part)
return total_grade
def image_trans(image):
if DEBUG:
show_image(image)
edged = cv2.Canny(image, 75, 200)
if DEBUG:
show_image(edged)
thresh = cv2.threshold(image, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
hybrid = edged + thresh
hybrid = edged + thresh
if CASE == 0:
img, cnts, hierarchy = cv2.findContours(hybrid.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
elif CASE == 1:
img, cnts, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
docCnt = None
if len(cnts) > 0:
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.01 * peri, True)
if len(approx) == 4:
docCnt = approx
break
if docCnt is None:
if DEBUG:
print "No contour is found"
return None, None
warped = four_point_transform(IMAGE, docCnt.reshape(4, 2))
if DEBUG:
cv2.drawContours(image, [docCnt], -1, 255, -1)
show_image(image)
return warped, cv2.contourArea(docCnt)
def option1_callback(self):
show.show_image(self.pic_inf.file_name)
def feature_extraction_single_latent(self,img_file, output_path = None, show_processes=False ):
#block = True
img = io.imread(img_file)
name = os.path.basename(img_file)
mask_CNN,_ = self.ROI_model.run(img)
h,w = mask_CNN.shape
#mask = mask_dilation(mask, block_size=16)
latent_template = template.Template()
minu_thr = 0.3
# template set 1: no ROI and enhancement are required
# texture image is used for coase segmentation
descriptor_imgs = []
texture_img = preprocessing.FastCartoonTexture(img, sigma=2.5, show=False)
descriptor_imgs.append(texture_img)
contrast_img_guassian = preprocessing.local_constrast_enhancement_gaussian(img)
quality_map, _, _ = get_maps.get_quality_map_dict(texture_img, self.dict_all, self.dict_ori,
self.dict_spacing, block_size=16, process=False)
quality_map_pixel = cv2.resize(quality_map, (0, 0), fx=16, fy=16)
#plt.imshow(quality_map_pixel,cmap='gray')
#plt.show()
mask_coarse = quality_map_pixel > 0.3
mask_coarse = mask_coarse.astype(np.int)
mask = mask_coarse * mask_CNN
# show.show_mask(mask_CNN, img, fname='mask_RCNN.jpeg',block=block)
# show.show_mask(mask_coarse,img,fname = 'mask_coarse.jpeg',block=block)
# show.show_mask(mask, img, fname='mask.jpeg',block=block)
#show.show_mask(mask, AEC_img, fname='mask_AEC.jpeg',block=block)
# plt.imshow(AEC_img,cmap = 'gray')
# plt.show(block=block)
# plt.close()
#show.show_mask(mask_CNN, img, fname='mask_RCNN.jpeg',block=block)
# AEC_img[mask == 0] = 128
# plt.imshow(AEC_img, cmap='gray')
# plt.show(block=block)
# plt.close()
AEC_img = self.enhancement_model.run(texture_img)
quality_map, dir_map, fre_map = get_maps.get_quality_map_dict(AEC_img, self.dict_all, self.dict_ori,self.dict_spacing, block_size=16, process=False)
blkH, blkW = dir_map.shape
if show_processes:
show.show_orientation_field(img, dir_map,mask = mask,fname='OF.jpeg')
# mnt = self.minu_model.run(contrast_img_mean, minu_thr=0.1)
# mnt = self.remove_spurious_minutiae(mnt, mask)
# minutiae_sets.append(mnt)
#
# fname = output_path + os.path.splitext(name)[0] + '_contrast_img_mean.jpeg'
# show.show_minutiae(contrast_img_mean, mnt, block=block, fname=fname)
enh_contrast_img = filtering.gabor_filtering_pixel(contrast_img_guassian, dir_map + math.pi / 2, fre_map,
mask=mask,
block_size=16, angle_inc=3)
enh_texture_img = filtering.gabor_filtering_pixel(texture_img, dir_map + math.pi / 2, fre_map,
mask=mask,
block_size=16, angle_inc=3)
if show_processes:
show.show_image(texture_img, mask=mask, block=True, fname='cropped_texture_image.jpeg')
show.show_image(AEC_img, mask=mask, block=True, fname='cropped_AEC_image.jpeg')
show.show_image(enh_contrast_img, mask=mask, block=True, fname='cropped_enh_image.jpeg')
#np.ones((h, w), np.int)
descriptor_imgs.append(enh_contrast_img)
quality_map2, _ , _ = get_maps.get_quality_map_dict(enh_contrast_img, self.dict_all,self.dict_ori,self.dict_spacing, block_size=16,
process=False)
quality_map_pixel2 = cv2.resize(quality_map2, (0, 0), fx=16, fy=16)
mask2 = quality_map_pixel2 > 0.50
#mask = mask*mask2
minutiae_sets = []
mnt = self.minu_model.run(contrast_img_guassian, minu_thr=0.05)
mnt = self.remove_spurious_minutiae(mnt, mask)
minutiae_sets.append(mnt)
if show_processes:
fname = 'minutiae_texture_img.jpeg'
show.show_minutiae(texture_img, mnt, mask=mask,block=block, fname=fname)
mnt = self.minu_model.run(AEC_img, minu_thr=0.3)
mnt = self.remove_spurious_minutiae(mnt, mask2)
minutiae_sets.append(mnt)
if show_processes:
fname = 'minutiae_AEC_img.jpeg'
show.show_minutiae(AEC_img, mnt, mask=mask, block=block, fname=fname)
mnt = self.minu_model.run(enh_contrast_img, minu_thr=0.3)
mnt = self.remove_spurious_minutiae(mnt, mask2)
minutiae_sets.append(mnt)
if show_processes:
fname = 'minutiae_enh_contrast_img.jpeg'
show.show_minutiae(enh_contrast_img, mnt, mask=mask,block=block, fname=fname)
mnt = self.minu_model.run(enh_texture_img, minu_thr=0.3)
mnt = self.remove_spurious_minutiae(mnt, mask2)
minutiae_sets.append(mnt)
# minutiae template 1
des = descriptor.minutiae_descriptor_extraction(texture_img, minutiae_sets[0], self.patch_types, self.des_models,
self.patchIndexV, batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=minutiae_sets[0],
des=des, oimg=dir_map, mask=mask)
latent_template.add_minu_template(minu_template)
# minutiae template 2
des = descriptor.minutiae_descriptor_extraction(texture_img, minutiae_sets[1], self.patch_types,
self.des_models,
self.patchIndexV, batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=minutiae_sets[1],
des=des, oimg=dir_map, mask=mask)
latent_template.add_minu_template(minu_template)
# minutiae template 3
des = descriptor.minutiae_descriptor_extraction(enh_texture_img, minutiae_sets[2], self.patch_types,
self.des_models,
self.patchIndexV, batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=minutiae_sets[2],
des=des, oimg=dir_map, mask=mask)
latent_template.add_minu_template(minu_template)
# minutiae template 4
des = descriptor.minutiae_descriptor_extraction(enh_texture_img, minutiae_sets[3], self.patch_types,
self.des_models,
self.patchIndexV, batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=minutiae_sets[3],
des=des, oimg=dir_map, mask=mask)
latent_template.add_minu_template(minu_template)
return latent_template
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 对象检测
cascade = cv2.CascadeClassifier(cascade_path)
# detectMultiScale函数。它可以检测出图片中所有的人脸，并将人脸用vector保存各个人脸的坐标、大小（用矩形表示)，可能有多个
facerect = cascade.detectMultiScale(frame_gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(10, 10))
if len(facerect) > 0:
print('face detected')
color = (255, 255, 255) # 白
for rect in facerect:
x, y = rect[0:2] # 前两项是坐标
width, height = rect[2:4] # 后两项是大小
image = frame[y - 10:y + height,
x:x + width] # frame是整张图，只截取人脸的特定区域的图
result = _predict(model, image)
if result == 0: # boss
print('someone is approaching')
show_image()
else:
print('Nobody')
k = cv2.waitKey(100) # 输入27时，会中止程序
if k == 27:
break
cap.release()
cv2.destroyAllWindows()