def enhanceVideo(frame, resize):
if (resize):
frame = cv2.resize(frame, (640, 352))
enhancedFrame = retinex.automatedMSRCR(frame)
return enhancedFrame
def retinex_shadow_removal(image, retinex_type):
with open('config.json', 'r') as f:
config = json.load(f)
if retinex_type == 'automatedMSRCR':
return retinex.automatedMSRCR(image, config['sigma_list'])
elif retinex_type == 'MSRCRP':
return retinex.MSRCP(image, config['sigma_list'], config['low_clip'],
config['high_clip'])
elif retinex_type == 'MSRCR':
return retinex.MSRCR(image, config['sigma_list'], config['G'],
config['b'], config['alpha'], config['beta'],
config['low_clip'], config['high_clip'])
else:
return image
def __data_generation(self, list_IDs_temp):
'Generates data containing batch_size samples'
# Initialization
X = [
np.empty((self.batch_size, self.dim[0], self.dim[1], 3)),
np.empty((self.batch_size, self.dim[0], self.dim[1], 3))
]
Y = np.empty((self.batch_size), dtype=int)
for i, ID in enumerate(list_IDs_temp): # ID is name of file
img = cv2.imread(ID)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (self.dim[1], self.dim[0]))
if self.type_gen == 'train':
img = self.sequence_augment(img)
new_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
new_img = np.expand_dims(new_img, -1)
new_img = automatedMSRCR(new_img, [10, 20, 30])
new_img = cv2.cvtColor(new_img[:, :, 0], cv2.COLOR_GRAY2RGB)
X[0][i] = img / 255.0
X[1][i] = new_img / 255.0
else:
new_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
new_img = np.expand_dims(new_img, -1)
new_img = automatedMSRCR(new_img, [10, 20, 30])
new_img = cv2.cvtColor(new_img[:, :, 0], cv2.COLOR_GRAY2RGB)
X[0][i] = img / 255.0
X[1][i] = new_img / 255.0
Y[i] = self.labels[ID]
return X, Y
with open('config.json', 'r') as f:
config = json.load(f)
# 遍历读入的图像
for img_name in img_list:
if img_name == '.gitkeep':
continue
# 读入图像
img = cv2.imread(os.path.join(data_path, img_name))
img_histogram = coloredHistoEqual(img)
img_gamma = adjust_gamma(img)
# 多尺度Retinex带色彩恢复
img_msrcr = retinex.MSRCR(img, config['sigma_list'], config['G'],
config['b'], config['alpha'], config['beta'],
config['low_clip'], config['high_clip'])
img_amsrcr = retinex.automatedMSRCR(img, config['sigma_list'])
# MSR with chromaticity preservation
img_msrcp = retinex.MSRCP(img, config['sigma_list'], config['low_clip'],
config['high_clip'])
# 实验结果
shape = img.shape
cv2.imshow('Image', img)
cv2.imshow('retinex', img_msrcr)
cv2.imshow('Histogram equalization', img_histogram)
cv2.imshow('Gamma Correction', img_gamma)
cv2.imshow('Automated retinex', img_amsrcr)
cv2.imshow('MSRCP', img_msrcp)
cv2.waitKey()
ret, frame = cap.read()
bboxes = classifier.detectMultiScale(frame)
for box in bboxes:
x, y, width, height = box
x2, y2 = int(x + 1.0*width), int(y + 1.2*height)
x, y = int(x-0.0*width), int(y-0.2*height)
img = frame[y:y2, x:x2]
print(type(img))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (299, 299))
new_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
new_img = np.expand_dims(new_img, -1)
new_img = automatedMSRCR(new_img, [10, 20, 30])
new_img = cv2.cvtColor(new_img[:, :, 0], cv2.COLOR_GRAY2RGB)
preds = model.predict([np.expand_dims(img / 255.0, 0), np.expand_dims(new_img / 255.0, 0)])
if preds[0][0] > 0.90:
rectangle(frame, (x, y), (x2, y2), (0,0,255), 1)
else:
rectangle(frame, (x, y), (x2, y2), (0,255,0), 1)
out.write(frame)
imshow('face detection', frame)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
cap.release()
def prepare_body_retinex(image):
image = prepare_body(image)
image = retinex.automatedMSRCR(image, [15, 80, 250])
return image
