一、hash分镜
导入包——哈希算法——定义根目录、分镜——显示在网页里
from flask import Flask,render_template
import os
import cv2
app = Flask(__name__)
def aHash(img): #定义哈希值函数
img = cv2.resize(img, (8, 8))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
s = 0
hash_str = ''
for i in range(8):
for j in range(8):
s = s + gray[i, j]
avg = s / 64
for i in range(8):
for j in range(8):
if gray[i, j] > avg:
hash_str = hash_str + '1'
else:
hash_str = hash_str + '0'
return hash_str
def cmpHash(hash1, hash2): #定义比较哈希值的函数
n = 0
print(hash1)
print(hash2)
if len(hash1) != len(hash2):
return -1
# 遍历判断
for i in range(len(hash1)):
# 不相等则n计数+1，n最终为相似度
if hash1[i] != hash2[i]:
n = n + 1
return n
def genframe(): #定义根目录，分镜
v_path = "static/ghz.mp4"
image_save = "static/hash"
if not(os.path.exists(image_save)):
os.mkdir(image_save)
cap=cv2.VideoCapture(v_path)
fc=cap.get(cv2.CAP_PROP_frame_COUNT)
_,img1=cap.read() #读取第一张图像
cv2.imwrite("static/hash/image{}.jpg".format(0),img1)
print(fc)
for i in range(int(fc)-1):
_,img2 = cap.read()
hash1 = aHash(img1)
hash2 = aHash(img2)
n = cmpHash(hash1, hash2)
if (n>35): #数值越大，分的帧数越小
cv2.imwrite("static/hash/image{}.jpg".format(i), img2)
img1=img2
@app.route('/hash')
def index():
genframe()
path='static/hash'
filename = os.listdir(path)
framecount=len(filename)
filename.sort(key= lambda x:int(x[5:-4]))
print(filename)
return render_template("hash.html", filename=filename, framecount=framecount)
if "__main__" == __name__:
app.run(port="5008")
注意在打开网页时末尾要加上“/hash"
运行结果
二、直方图比较
from flask import Flask,render_template
import cv2
import os
app = Flask(__name__)
os.chdir(r"C:UserslenovoAppDataLocalProgramsPythonPython3711.10")
# 通过得到RGB每个通道的直方图来计算相似度
def classify_hist_with_split(image1, image2, size=(256, 256)):
# 将图像resize后，分离为RGB三个通道，再计算每个通道的相似值
image1 = cv2.resize(image1, size)
image2 = cv2.resize(image2, size)
sub_image1 = cv2.split(image1)
sub_image2 = cv2.split(image2)
sub_data = 0
for im1, im2 in zip(sub_image1, sub_image2):
sub_data += calculate(im1, im2)
sub_data = sub_data / 3
return sub_data
# 计算单通道的直方图的相似值
def calculate(image1, image2):
hist1 = cv2.calcHist([image1], [0], None, [256], [0.0, 255.0])
hist2 = cv2.calcHist([image2], [0], None, [256], [0.0, 255.0])
# 计算直方图的重合度
degree = 0
for i in range(len(hist1)):
if hist1[i] != hist2[i]:
degree = degree + (1 - abs(hist1[i] - hist2[i]) / max(hist1[i], hist2[i]))
else:
degree = degree + 1
degree = degree / len(hist1)
return degree
def genframe(): # 定义根目录，分镜
v_path = "static/ghz.mp4"
image_save = "static/hist"
if not (os.path.exists(image_save)):
os.mkdir(image_save)
cap = cv2.VideoCapture(v_path)
fc = cap.get(cv2.CAP_PROP_frame_COUNT)
print(fc)
_, img1 = cap.read() # 读取第一张图像
cv2.imwrite("static/hist/image{}.jpg".format(0), img1)
print(int(fc))
for i in range(248):
_, img2 = cap.read()
n = classify_hist_with_split(img1,img2)
if (n < 0.6): # 数值越大，分的帧数越小
cv2.imwrite("static/hist/image{}.jpg".format(i), img2)
img1 = img2
genframe()
@app.route('/hist')
def index():
path='static/hist'
histfile = os.listdir(path)
histcount=int(len(histfile))
histfile.sort(key= lambda x:int(x[5:-4])) #对List的元素排序
print(histfile)
return render_template('hist.html',path=path,histfile=histfile,histcount=histcount)
if "__main__" == __name__:
app.run(port="5009")
注意：直方图与哈希算法分开来写更清晰，运行时注意选择的是哪个py文件
顺序是：先直方图，再定义定义根目录和分镜，最后呈现在网页里
运行结果：
