第一種做法：?

import os import sys import random import math import numpy as np import skimage.io import matplotlib import matplotlib.pyplot as plt import cv2 import colorsys os.environ['CUDA_VISIBLE_DEVICES'] = '1' # Root directory of the project ROOT_DIR = os.path.abspath("./") print(ROOT_DIR) # Import Mask RCNN sys.path.append(ROOT_DIR) # To find local version of the library from mrcnn import utils import mrcnn.model as modellib from mrcnn import visualize # # Import COCO config sys.path.append(os.path.join(ROOT_DIR, "samples/coco/")) # To find local version import coco# Directory to save logs and trained model MODEL_DIR = os.path.join(ROOT_DIR, "logs")# Local path to trained weights file COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5") print(COCO_MODEL_PATH)IMAGE_DIR = os.path.join(ROOT_DIR, "images")class InferenceConfig(coco.CocoConfig):# Set batch size to 1 since we'll be running inference on# one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPUGPU_COUNT = 1IMAGES_PER_GPU = 1config = InferenceConfig() config.display()def random_colors(N, bright=True):"""Generate random colors.To get visually distinct colors, generate them in HSV space thenconvert to RGB."""brightness = 1.0 if bright else 0.7hsv = [(i / N, 1, brightness) for i in range(N)]colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))random.shuffle(colors)return colors def apply_mask(image, mask,color,alpha=0.5):"""Apply the given mask to the image."""for c in range(3):image[:, :, c] = np.where(mask == 1,image[:, :, c] *(1 - alpha) + alpha*color[c]* 255,image[:, :, c])return image def load_model():model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)# Load weights trained on MS-COCOmodel.load_weights(COCO_MODEL_PATH, by_name=True)# COCO Class names# Index of the class in the list is its ID. For example, to get ID of# the teddy bear class, use: class_names.index('teddy bear')class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane','bus', 'train', 'truck', 'boat', 'traffic light','fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird','cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear','zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie','suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball','kite', 'baseball bat', 'baseball glove', 'skateboard','surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup','fork', 'knife', 'spoon', 'bowl', 'banana', 'apple','sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza','donut', 'cake', 'chair', 'couch', 'potted plant', 'bed','dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote','keyboard', 'cell phone', 'microwave', 'oven', 'toaster','sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors','teddy bear', 'hair drier', 'toothbrush']# file_names = next(os.walk(IMAGE_DIR))[2]file_names=['3627527276_6fe8cd9bfe_z.jpg']image = cv2.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))# Run detectionresults = model.detect([image], verbose=1)# Visualize resultsr = results[0]print(r['rois'])print('======================')print(np.array(r['masks']).shape)print('========================')print(r['class_ids'])print('========================')print(r['scores'])# visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],# class_names, r['scores'],ax=False)boxes=r['rois']masks=r['masks']class_ids=r['class_ids']scores=r['scores']N = r['rois'].shape[0]colors = random_colors(N)print('colors=', colors)for i in range(N):ymin, xmin, ymax, xmax = boxes[i]print(boxes[i])color = colors[i]# Maskmask = masks[:, :, i]image = apply_mask(image, mask, color)# add textclass_id = class_ids[i]score = scores[i] if scores is not None else Nonelabel = class_names[class_id]caption = "{} {:.3f}".format(label, score) if score else labelcv2.putText(image, caption, (xmin, ymin + 8), cv2.FONT_HERSHEY_SIMPLEX, 0.3, color=(255, 255, 255), thickness=1)cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)cv2.imwrite('1.jpg', image) if __name__ == '__main__':load_model()

注意np.where的妙用，其中r['masks']是（none，none，4）的ndarray形式的mask,通過取每一個channel蓋在原圖片上。?

第二種做法：

#coding:utf-8 import numpy as np import matplotlib.pyplot as plt import cv2 import imutils from matplotlib.patches import Polygon import osdef get_contour(image_mask,black_mask,image,color):global dummy_mask,line_maskimage_thre = cv2.threshold(image* 255, 127, 255, cv2.THRESH_BINARY)[1]# cnts=cv2.findContours(# image_thre.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)# print('cnts=',cnts.shape)cnts = cv2.findContours(image_thre, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)contours = cnts[0] if imutils.is_cv2() else cnts[1]c_ = sorted(contours, key=cv2.contourArea, reverse=True)for i in c_:if i.shape[0]>120:pig_cnt = np.squeeze(i)"""change color"""line_mask=cv2.drawContours(image_mask, [pig_cnt], -1, color, 5, cv2.LINE_AA)fill_mask=cv2.drawContours(black_mask, [pig_cnt], 0, (0,0,240), thickness=cv2.FILLED)# dummy_mask=cv2.add(line_mask,fill_mask)dummy_mask = cv2.addWeighted(line_mask,1., fill_mask,0.4,0)# dummy_mask = cv2.drawContours(dummy_mask, [pig_cnt], 0, (random_b, random_g, random_r), thickness=cv2.FILLED)return dummy_mask def get_channel_color(channels):color_dict={}for i in range(channels):random_b = int(np.random.randint(0, 255, 1)[0])random_g = int(np.random.randint(0, 255, 1)[0])random_r = int(np.random.randint(0, 255, 1)[0])color_dict[i]=(random_b,random_g,random_r)return color_dictdef pig_contour():img_path='./image_low_clear'show_path='./show_low_clear'output_path='./image_mask_low_clear'if not os.path.exists(output_path):os.mkdir(output_path)show_list_path = [os.path.join(show_path, i) for i in os.listdir(show_path)]channels_list=[]for i in show_list_path:channel=np.load(i).shape[-1]channels_list.append(channel)channel=max(channels_list)print('channel=',channel)color_dict = get_channel_color(channels=channel)imgs_list_path = [os.path.join(img_path, i) for i in os.listdir(img_path)]for i,img_list_path in enumerate(imgs_list_path):pig_mask_list_path = show_path+'/'+img_list_path.split('/')[-1].replace('jpg','npy')print('img_list_path=',img_list_path)print('pig_mask_list_path=',pig_mask_list_path)if pig_mask_list_path.split('/')[-1] in os.listdir(show_path):pig_mask = np.load(pig_mask_list_path)image_h, image_w, channels = pig_mask.shapeblack_mask = np.zeros((image_h, image_w, 3),dtype=np.float32)image=cv2.imread(img_list_path)image_mask=image.astype(np.float32)for i in range(channels):global dummy_mask# print('i=',i)dummy_mask=get_contour(image_mask,black_mask, pig_mask[..., i],color=color_dict[i])cv2.imwrite(output_path+'/'+img_list_path.split('/')[-1], dummy_mask)def unlock_mv(sp):""" 將視頻轉換成圖片sp: 視頻路徑 """cap = cv2.VideoCapture(sp)suc = cap.isOpened() # 是否成功打開frame_count = 0while suc:frame_count += 1suc, frame = cap.read()params = []params.append(2) # params.append(1)cv2.imwrite('mv\\%d.jpg' % frame_count, frame, params)cap.release()print('unlock image: ', frame_count)# 圖片轉視頻 def jpg_video():""" 將圖片合成視頻. sp: 視頻路徑，fps: 幀率 """image_path = './image_mask_low_clear'images_list_path=[os.path.join(image_path,i) for i in os.listdir(image_path)]images_list_path=sorted(images_list_path,key=lambda x:int(x.split('/')[-1].split('.')[0]))# print(images_list_path)h,w,_=cv2.imread(images_list_path[0]).shapefps=4fourcc = cv2.VideoWriter_fourcc(*'MJPG')videoWriter = cv2.VideoWriter('predict_low_clear.avi', fourcc, fps, (w, h)) # 最后一個是保存圖片的尺寸for i,image_list_path in enumerate(images_list_path):frame = cv2.imread(image_list_path)videoWriter.write(frame)videoWriter.release()#視頻轉圖片 interval是fps加1,如果去合成視頻fps==6 def video2frame():video_src_path = "./data/xiaomi8_videos"# video_formats = [".MP4", ".MOV"] 我的數據集都是.mp4所以不需要進行分類判斷frame_save_path = "./data/xiaomi8_images/"if not os.path.exists(frame_save_path):os.mkdir(frame_save_path)interval = 5"""將視頻按固定間隔讀取寫入圖片:param video_src_path: 視頻存放路徑:param formats:　包含的所有視頻格式:param frame_save_path:　保存路徑:param frame_width:　保存幀寬:param frame_height:　保存幀高:param interval:　保存幀間隔:return:　幀圖片"""videos = os.listdir(video_src_path)for each_video in videos:print("正在讀取視頻：", each_video)each_video_name = each_video[:-4]print(each_video_name)if not os.path.exists(frame_save_path + each_video_name):os.mkdir(frame_save_path + each_video_name)each_video_save_full_path = os.path.join(frame_save_path, each_video_name) + "/"each_video_full_path = os.path.join(video_src_path, each_video)cap = cv2.VideoCapture(each_video_full_path)frame_index = 0frame_count = 0if cap.isOpened():success = Trueelse:success = Falseprint("讀取失敗!")while(success):success, frame = cap.read()print("---> 正在讀取第%d幀:" % frame_index, success) # 我的是Python3.6if frame_index % interval == 0 and success: # 如路徑下有多個視頻文件時視頻最后一幀報錯因此條件語句中加and success# resize_frame = cv2.resize(frame, (frame_width, frame_height), interpolation=cv2.INTER_AREA)cv2.imwrite(each_video_save_full_path + "%d.png" % frame_count, frame)frame_count += 1frame_index += 1cap.release()if __name__ == '__main__':pig_contour()# jpg_video()

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