555 lines
20 KiB
Python
555 lines
20 KiB
Python
import cv2
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import os
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import numpy as np
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def divide(video, folder_name):
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# 获取视频的帧数
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frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
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# print(frame_count)
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save_count = 0
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# 创建保存帧的文件夹
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if not os.path.exists(folder_name):
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os.makedirs(folder_name)
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# 循环读取视频中的每一帧
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for i in range(frame_count):
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_, frame = video.read()
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# dst = cv2.resize(frame, (960, 540))
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cv2.imwrite(folder_name+"/frame_{}.jpg".format(i), frame)
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if i % 50 == 0:
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print(i/frame_count)
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def divide_into_n(video):
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frame_number = 1 # 记录读取到的帧数，初始值为1
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frame_interval = 100 # 每隔100帧（截取1帧）
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if not os.path.exists("frames3"):
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os.makedirs("frames3")
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while (video.isOpened()): # 视频文件被打开后
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retval, frame = video.read() # 读取视频帧
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if retval == True: # 读取到视频帧后
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if (frame_number % frame_interval == 0): # 每隔100帧
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cv2.imwrite("frames3/" + str(frame_number) + ".jpg", frame) # 截取并保存1帧
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else: # 没有读取到视频帧
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break # 终止循环
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frame_number = frame_number + 1 # 读取到的视频帧执行自加操作
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cv2.waitKey(1) # 1毫秒后播放视频文件的下一帧
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print("视频帧已截取完成！") # 控制台输出提示信息
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def merge(folder_name, frame_num):
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l=[]
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for i in range(frame_num):
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l.append(str(i)+".jpg")
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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# 这里的outpy.mp4v就是视频保存路径
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image0 = cv2.imread(folder_name + "/00000.jpg")
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iw, ih, ic = np.shape(image0)
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out = cv2.VideoWriter('outpy.mp4v', cv2.VideoWriter_fourcc(*'DIVX'), 20, (iw, ih))
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"""
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注意这里的width和height一定要和图片的一致，否则保存的视频是无法播放的
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"""
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# i是帧图片的名字(含有后缀名)
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for i in l:
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img12 = cv2.imread("./frame4/new/"+str(i)) # savepath为图片帧目录名
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out.write(img12) # out为刚刚创建的保存视频的对象
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out.release()
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def generate_video(image_folder,frame_id):
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video_name = image_folder+'result.mp4'
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fps = 20
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images = [img for img in os.listdir(image_folder) if img.endswith(".jpg")]
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frame = cv2.imread(os.path.join(image_folder, images[0]))
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height, width, layers = frame.shape
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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video = cv2.VideoWriter(video_name, fourcc, fps, (width, height))
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for frame_index in range(1,frame_id+1):
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image = ""+str(frame_index)+".jpg"
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print(image)
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video.write(cv2.imread(os.path.join(image_folder, image)))
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cv2.destroyAllWindows()
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video.release()
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def modify_image():
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# base_path = "./frame4/000.jpg"
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# target_path = "./frame4/frame_1913.jpg"
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base_path = "./frame6/000.jpg"
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target_path = "./frame6/frame_280.jpg"
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base_picture = cv2.imread(base_path)
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target_picture = cv2.imread(target_path)
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# kernel = np.ones((5, 5), np.float32) / 25 # 5x5 的平均滤波器核：
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# target_picture = cv2.filter2D(target_picture, -1, kernel)
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# print(base_picture.shape)
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up = 44
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down = 139
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left = 291
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right = 349
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for i in range(up, down): # 上下
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for j in range(left, right): # 左右
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# base_picture[i, j] = [128, 128, 128]
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base_value = base_picture[i,j]
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target_value = target_picture[i, j]
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# if i-up<5 or down-i<5 or j-left<5 or right-j<5:
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# lRGB = np.mean(base_value, target_value)
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# base_picture[i, j][0] = lRGB[0]
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# base_picture[i, j][1] = lRGB[1]
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# base_picture[i, j][2] = lRGB[2]
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base_picture[i, j] = target_value
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# resize_base = target_picture[up:down, left:right]
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#
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# mask = 255 * np.ones(resize_base.shape, resize_base.dtype)
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#
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# center = (int((left + right) / 2), int((up + down) / 2))
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#
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# base_picture = cv2.seamlessClone(resize_base, base_picture, mask, center, cv2.MONOCHROME_TRANSFER) # NORMAL_CLONE 也可以
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# kernel = np.ones((5, 5), np.float32) / 25 # 5x5 的平均滤波器核：
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# base_picture = cv2.filter2D(base_picture, -1, kernel)
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cv2.imshow("after", base_picture)
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cv2.imwrite("./frame6/0000.jpg", base_picture) # 截取并保存1帧
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# 等待显示
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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def get_video_size(video):
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width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
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frame_num = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
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return width, height, frame_num
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def get_all_frame_rgb(folder_name,image_num):
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image0 = cv2.imread(folder_name+"/frame_0.jpg")
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iw, ih, ic = np.shape(image0)
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# print(iw,ih)
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rgb_list = []
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# for i in range(iw*ih):
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# rgb_list.append([])
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# print("complete list initialization")
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for i in range(image_num):
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image_path = folder_name+"/frame_"+str(i)+".jpg"
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image = cv2.imread(image_path)
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for x in range(iw):
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for y in range(ih):
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print(image[x,y])
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# rgb_list.append(rgb_value)
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# rgb_list[x*ih+y].append(rgb_value)
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print("analyzing frame",i)
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# print(rgb_list)
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rgb_list = []
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def resize_img(folder_name,image_num,cut_top_size):
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for i in range(image_num):
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image_path = folder_name+"/frame_"+str(i)+".jpg"
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image = cv2.imread(image_path)
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new_image = image[cut_top_size:621,0:1103]
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cv2.imwrite("frame4/new/" + str(i) + ".jpg", new_image) # 截取并保存1帧
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def expand_position(p1,p2,img, frame_id, weight, expand_mode):
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base_path = folder_name + "00000.jpg"
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base_image = cv2.imread(base_path)
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# print(1)
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new_up = p1[1]
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new_down = p2[1]
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new_left = p1[0]
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new_right = p2[0]
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need_expand = False
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prev_need = False
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width = int(base_image.shape[1])
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height = int(base_image.shape[0])
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threshold_cha = 50
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threshold_different_num = 5
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lr_range = 5
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t_range = 5
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d_range = 15
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if expand_mode == 1:
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lr_range = 30 + int(frame_id / weight) # 20
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t_range = 35 + int(frame_id / (weight)) # video444 -> weight*0.4 # 35
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d_range = 25 + int(frame_id / weight) # 25
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if expand_mode == 2:
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lr_range = 50
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d_range = 270 # 25
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for x in range(max(0,p1[0]),max(0,p1[0]-lr_range),-1):
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prev_need = need_expand
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need_expand = False
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need_num=0
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for y in range(p1[1],p2[1]):
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cha = 0
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cha += abs(int(base_image[y, x][0]) - int(img[y, x][0]))
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cha += abs(int(base_image[y, x][1]) - int(img[y, x][1]))
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cha += abs(int(base_image[y, x][2]) - int(img[y, x][2]))
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# cv2.rectangle(img, (x,y), (x,y), (255, 0, 0), 2, 1) # origin
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# cv2.imshow("Tracking", img)
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# cv2.waitKey(0)
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# cv2.rectangle(base_image, (x, y), (x, y), (255, 0, 0), 2, 1) # origin
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# cv2.imshow("Tracking", base_image)
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# cv2.waitKey(0)
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if cha>threshold_cha:
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need_num += 1
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# print(x,y,base_image[x, y],img[x, y])
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need_expand = True
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# break
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if need_num>threshold_different_num:
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# print(x,need_num)
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new_left = x
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for x in range(min(width,p2[0]),min(width,p2[0]+lr_range),1):
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prev_need = need_expand
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need_expand = False
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need_num=0
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for y in range(p1[1],p2[1]):
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cha = 0
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cha += abs(int(base_image[y, x][0]) - int(img[y, x][0]))
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cha += abs(int(base_image[y, x][1]) - int(img[y, x][1]))
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cha += abs(int(base_image[y, x][2]) - int(img[y, x][2]))
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if cha>threshold_cha:
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need_num += 1
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# print(x,y,base_image[x, y],img[x, y])
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need_expand = True
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# break
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if need_num>threshold_different_num:
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new_right = x
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for y in range(p1[1],max(1,p1[1]-t_range),-1):
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prev_need = need_expand
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need_expand = False
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need_num=0
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for x in range(p1[0],p2[0]):
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cha = 0
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cha += abs(int(base_image[y, x][0]) - int(img[y, x][0]))
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cha += abs(int(base_image[y, x][1]) - int(img[y, x][1]))
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cha += abs(int(base_image[y, x][2]) - int(img[y, x][2]))
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if cha>threshold_cha:
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need_num += 1
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# print(x,y,base_image[x, y],img[x, y])
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need_expand = True
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# break
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if need_num>threshold_different_num:
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new_up = y
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for y in range(p2[1],min(height,p2[1]+d_range),1):
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prev_need = need_expand
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need_expand = False
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need_num=0
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for x in range(p1[0],p2[0]):
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cha = 0
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cha += abs(int(base_image[y, x][0]) - int(img[y, x][0]))
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cha += abs(int(base_image[y, x][1]) - int(img[y, x][1]))
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cha += abs(int(base_image[y, x][2]) - int(img[y, x][2]))
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if cha>threshold_cha:
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need_num += 1
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# print(x,y,base_image[x, y],img[x, y])
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need_expand = True
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# break
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if need_num>threshold_different_num:
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new_down = y
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return (new_left,new_up),(new_right,new_down)
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# return (max(0,p1[0]-lr_range),max(1,p1[1]-t_range)),(min(width,p2[0]+lr_range),min(height,p2[1]+d_range)) # 查看扩大的最大范围
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def cover_base(img,p1,p2):
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base_path = folder_name + "00000.jpg"
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base_image = cv2.imread(base_path)
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width = int(img.shape[1])
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height = int(img.shape[0])
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edge_adding = 10
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w1=0.2
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w2=0.5
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w3=0.8
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for x in range(max(0,p1[0]-edge_adding),min(p2[0]+edge_adding,width)):
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for y in range(max(0,p1[1]-edge_adding),min(p2[1]+edge_adding,height)):
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cha0 = abs(int(base_image[y, x][0]) - int(img[y, x][0]))
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cha1 = abs(int(base_image[y, x][1]) - int(img[y, x][1]))
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cha2 = abs(int(base_image[y, x][2]) - int(img[y, x][2]))
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if x==max(0,p1[0]-edge_adding):
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img[y, x][0] = int(base_image[y, x ][0])*w1 + int(img[y, x-1][0])*(1-w1)
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img[y, x][1] = int(base_image[y, x ][1])*w1 + int(img[y, x-1][1])*(1-w1)
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img[y, x][2] = int(base_image[y, x][2])*w1 + int(img[y, x-1][2])*(1-w1)
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# print(1)
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elif x==min(p2[0]+edge_adding,width):
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img[y, x][0] = int(base_image[y, x ][0])*w1 + int(img[y, x+1][0])*(1-w1)
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img[y, x][1] = int(base_image[y, x][1])*w1 + int(img[y, x+1][1])*(1-w1)
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img[y, x][2] = int(base_image[y, x ][2])*w1 + int(img[y, x+1][2])*(1-w1)
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elif y==max(0,p1[1]-edge_adding):
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img[y, x][0] = int(base_image[y, x][0])*w1 + int(img[y-1, x][0])*(1-w1)
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img[y, x][1] = int(base_image[y, x][1])*w1 + int(img[y-1, x][1])*(1-w1)
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img[y, x][2] = int(base_image[y, x][2])*w1 + int(img[y-1, x][2])*(1-w1)
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elif y==p2[1]+edge_adding:
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img[y, x][0] = int(base_image[y, x][0])*w1 + int(img[y+1, x][0])*(1-w1)
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img[y, x][1] = int(base_image[y, x][1])*w1 + int(img[y+1, x][1])*(1-w1)
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img[y, x][2] = int(base_image[y, x][2])*w1 + int(img[y+1, x][2])*(1-w1)
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elif x==max(0,p1[0]-edge_adding)+1:
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img[y, x][0] = int(base_image[y, x ][0])*w2 + int(img[y, x-2][0])*(1-w2)
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img[y, x][1] = int(base_image[y, x ][1])*w2 + int(img[y, x-2][1])*(1-w2)
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img[y, x][2] = int(base_image[y, x][2])*w2 + int(img[y, x-2][2])*(1-w2)
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# print(1)
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elif x==min(p2[0]+edge_adding,width)-1:
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img[y, x][0] = int(base_image[y, x ][0])*w2 + int(img[y, x+2][0])*(1-w2)
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img[y, x][1] = int(base_image[y, x][1])*w2 + int(img[y, x+2][1])*(1-w2)
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img[y, x][2] = int(base_image[y, x ][2])*w2 + int(img[y, x+2][2])*(1-w2)
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elif y==max(0,p1[1]-edge_adding)+1:
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img[y, x][0] = int(base_image[y, x][0])*w2 + int(img[y-2, x][0])*(1-w2)
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img[y, x][1] = int(base_image[y, x][1])*w2 + int(img[y-2, x][1])*(1-w2)
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img[y, x][2] = int(base_image[y, x][2])*w2 + int(img[y-2, x][2])*(1-w2)
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elif y==p2[1]+edge_adding-1 and y<height-2:
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img[y, x][0] = int(base_image[y, x][0])*w2 + int(img[y+2, x][0])*(1-w2)
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img[y, x][1] = int(base_image[y, x][1])*w2 + int(img[y+2, x][1])*(1-w2)
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img[y, x][2] = int(base_image[y, x][2])*w2 + int(img[y+2, x][2])*(1-w2)
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elif x==max(0,p1[0]-edge_adding)+2:
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img[y, x][0] = int(base_image[y, x ][0])*w3 + int(img[y, x-3][0])*(1-w3)
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img[y, x][1] = int(base_image[y, x ][1])*w3 + int(img[y, x-3][1])*(1-w3)
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img[y, x][2] = int(base_image[y, x][2])*w3 + int(img[y, x-3][2])*(1-w3)
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# print(1)
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elif x==min(p2[0]+edge_adding,width)-2:
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img[y, x][0] = int(base_image[y, x ][0])*w3 + int(img[y, x+3][0])*(1-w3)
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img[y, x][1] = int(base_image[y, x][1])*w3 + int(img[y, x+3][1])*(1-w3)
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img[y, x][2] = int(base_image[y, x ][2])*w3 + int(img[y, x+3][2])*(1-w3)
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elif y==max(0,p1[1]-edge_adding)+2:
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img[y, x][0] = int(base_image[y, x][0])*w3 + int(img[y-3, x][0])*(1-w3)
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img[y, x][1] = int(base_image[y, x][1])*w3 + int(img[y-3, x][1])*(1-w3)
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img[y, x][2] = int(base_image[y, x][2])*w3 + int(img[y-3, x][2])*(1-w3)
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elif y==p2[1]+edge_adding-2 and y<height-3:
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img[y, x][0] = int(base_image[y, x][0])*w3 + int(img[y+3, x][0])*(1-w3)
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img[y, x][1] = int(base_image[y, x][1])*w3 + int(img[y+3, x][1])*(1-w3)
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img[y, x][2] = int(base_image[y, x][2])*w3 + int(img[y+3, x][2])*(1-w3)
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else:
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img[y,x]=base_image[y,x]
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# 模糊部分
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# roi = img[max(0,p1[1]-edge_adding):min(p2[1]+edge_adding,575),max(0,p1[0]-edge_adding):min(p2[0]+edge_adding,1103)]
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# blur = cv2.GaussianBlur(roi, (5, 5), 0)
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# img[max(0, p1[1] - edge_adding):min(p2[1] + edge_adding, 575), max(0, p1[0] - edge_adding):min(p2[0] + edge_adding, 1103)] = blur
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return img
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def seamless_cover_base(img, p1, p2):
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base_path = folder_name + "00000.jpg"
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base_image = cv2.imread(base_path)
|
||
edge_adding = 10
|
||
|
||
width = int(img.shape[1])
|
||
height = int(img.shape[0])
|
||
|
||
pure_cover = cover_base(img, p1, p2)
|
||
|
||
has_touched_bottom = False
|
||
if min(p2[1]+edge_adding, height) == height:
|
||
has_touched_bottom = True
|
||
|
||
resize_base = pure_cover[max(0, p1[1]-edge_adding):min(p2[1]+edge_adding, height), max(0, p1[0]-edge_adding):min(p2[0]+edge_adding, width)]
|
||
|
||
mask = 255 * np.ones(resize_base.shape, resize_base.dtype)
|
||
|
||
center = (int((min(p2[0]+edge_adding, width)+max(0, p1[0]-edge_adding)) / 2), int((min(p2[1]+edge_adding, height)+max(0, p1[1]-edge_adding)) / 2))
|
||
# print(p1,p2,center)
|
||
|
||
normal_clone = cv2.seamlessClone(resize_base, img, mask, center, cv2.NORMAL_CLONE) # NORMAL_CLONE 也可以
|
||
return normal_clone
|
||
|
||
# if has_touched_bottom:
|
||
# return cover_base(img, p1, p2)
|
||
# else:
|
||
# normal_clone = cv2.seamlessClone(resize_base, img, mask, center, cv2.MONOCHROME_TRANSFER) # NORMAL_CLONE 也可以
|
||
# return normal_clone
|
||
|
||
|
||
def prevent_big_change(p1, p2, prev1, prev2):
|
||
v10 = p1[0]
|
||
v11 = p1[1]
|
||
v20 = p2[0]
|
||
v21 = p2[1]
|
||
|
||
max_cha = 20
|
||
|
||
if prev1[0]-p1[0]>max_cha:
|
||
v10=prev1[0]-max_cha
|
||
elif p1[0]-prev1[0]>max_cha:
|
||
v10=prev1[0]+max_cha
|
||
|
||
if prev1[1]-p1[1]>max_cha:
|
||
v11=prev1[1]-max_cha
|
||
elif p1[1]-prev1[1]>max_cha:
|
||
v11=prev1[1]+max_cha
|
||
|
||
if prev2[0] - p2[0] > max_cha:
|
||
v20 = prev2[0] - max_cha
|
||
elif p2[0] - prev2[0] > max_cha:
|
||
v20 = prev2[0] + max_cha
|
||
|
||
if prev2[1] - p2[1] > max_cha:
|
||
v21 = prev2[1] - max_cha
|
||
elif p2[1] - prev2[1] > max_cha:
|
||
v21 = prev2[1] + max_cha
|
||
|
||
return (v10,v11),(v20,v21)
|
||
|
||
|
||
def find_object(cut_top_size,trace_file,img_prefix,expand_mode,img_out_folder):
|
||
file = open(trace_file, encoding="utf-8")
|
||
content = file.readlines()
|
||
|
||
new_p1 = (0, 0)
|
||
new_p2 = (0, 0)
|
||
prev_new_p1 = (0, 0)
|
||
prev_new_p2 = (0, 0)
|
||
|
||
img_out_folder = "output/" # 合成视频的源文件夹
|
||
|
||
frame_id = 0
|
||
content_size = len(content)
|
||
for item in content:
|
||
item = item.strip("\n")
|
||
item = item.replace(",","")
|
||
item = item.replace("(","")
|
||
item = item.replace(")","")
|
||
up = int(item.split(" ")[1]) - cut_top_size
|
||
down = int(item.split(" ")[3]) - cut_top_size
|
||
left = int(item.split(" ")[0])
|
||
right = int(item.split(" ")[2])
|
||
|
||
p1 = (left,up)
|
||
p2 = (right,down)
|
||
|
||
image_path = folder_name + img_prefix + str(frame_id) + ".jpg"
|
||
# print(image_path)
|
||
image = cv2.imread(image_path)
|
||
width = int(image.shape[1])
|
||
height = int(image.shape[0])
|
||
|
||
prev_new_p1 = new_p1
|
||
prev_new_p2 = new_p2
|
||
|
||
weight = 1
|
||
if frame_id<content_size/3:
|
||
weight = 3
|
||
elif frame_id<content_size/2:
|
||
weight = 2
|
||
elif frame_id < content_size / 1.5:
|
||
weight = 1.7
|
||
elif frame_id < content_size / 1.2:
|
||
weight = 1.3
|
||
else:
|
||
weight = 1
|
||
|
||
if expand_mode == 0:
|
||
weight = 10
|
||
|
||
if p2[1]>height:
|
||
p2 = (p2[0],height)
|
||
new_p1, new_p2 = expand_position(p1, p2, image, frame_id, weight,expand_mode) # 扩大框框
|
||
|
||
# 防止目标跟踪框的突变
|
||
if frame_id>1:
|
||
new_p1, new_p2 = prevent_big_change(new_p1, new_p2, prev_new_p1, prev_new_p2)
|
||
|
||
# cv2.rectangle(image, p1, p2, (255, 0, 0), 2, 1) # origin
|
||
# cv2.rectangle(image, new_p1, new_p2, (255, 0, 0), 2, 1) # draw adaptive rectangle
|
||
# image = cover_base(image, new_p1, new_p2) # cover one entity 覆盖原图
|
||
image = seamless_cover_base(image, new_p1, new_p2) # cover one entity 覆盖原图
|
||
# blurred = cv2.GaussianBlur(image, (1, 5), 0) # 模糊整个图像
|
||
|
||
frame_id += 1
|
||
cv2.imshow("Tracking", image)
|
||
|
||
print(frame_id/content_size)
|
||
|
||
# cv2.imwrite(img_out_folder + str(frame_id) + ".jpg", image) # 截取并保存1帧
|
||
|
||
cv2.waitKey(0)
|
||
|
||
# generate_video(img_out_folder,frame_id)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# 读取视频
|
||
video_name = "333.avi"
|
||
my_video = cv2.VideoCapture(video_name)
|
||
|
||
w, h, n = get_video_size(my_video)
|
||
print("width:", w)
|
||
print("height:", h)
|
||
print("frame_num:", n)
|
||
|
||
cut_top_size = 0
|
||
trace_txt = "position.txt"
|
||
image_prefix = "frame_"
|
||
expand_mode = 0 # 1-bigger, 0-no-change
|
||
|
||
folder_name = "frame7/"
|
||
|
||
if video_name=="003.avi":
|
||
folder_name = "frame6/"
|
||
elif video_name=="444.avi":
|
||
folder_name = "frame5/"
|
||
elif video_name=="333.avi":
|
||
cut_top_size = 46
|
||
expand_mode = 1
|
||
image_prefix = ""
|
||
folder_name = "frame4/new/"
|
||
elif video_name == "lab.mp4":
|
||
folder_name = "frame7/"
|
||
expand_mode = 2
|
||
else:
|
||
print("no this video")
|
||
|
||
# divide(my_video,folder_name)
|
||
# divide_into_n(my_video)
|
||
# merge(folder_name,n)
|
||
|
||
img_folder = "output/" # 合成视频的源文件夹
|
||
# generate_video(img_folder,500)
|
||
|
||
# modify_image()
|
||
# get_all_frame_rgb(folder_name,n)
|
||
# resize_img(folder_name,n,cut_top_size)
|
||
|
||
# cut_top_size = 46
|
||
# cut_top_size = 0
|
||
# trace_txt = "position.txt"
|
||
# image_prefix = "frame_"
|
||
# expand_mode = 0 # 1-bigger, 0-no-change
|
||
find_object(cut_top_size,trace_txt,image_prefix,expand_mode,img_folder)
|
||
|
||
# 关闭视频文件
|
||
my_video.release()
|