diff --git a/mask/mask_rcnn.py b/mask/mask_rcnn.py deleted file mode 100644 index 39d87e9..0000000 --- a/mask/mask_rcnn.py +++ /dev/null @@ -1,170 +0,0 @@ -import cv2 as cv -import argparse -import numpy as np -import os.path -import sys -import random - -# Inicjalizacja parametrów -confThreshold = 0.5 -maskThreshold = 0.3 - -args = parser.parse_args() - -# Rysuje obrawmowanie zwierzęcia, koloruje i zaznacza maską -def drawBox(frame, classId, conf, left, top, right, bottom, classMask): - # obramowanie. - cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3) - - # etykieta obiektu - label = '%.2f' % conf - if classes: - assert(classId < len(classes)) - label = '%s:%s' % (classes[classId], label) - - # wyświetla etykietę - labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1) - top = max(top, labelSize[1]) - cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (255, 255, 255), cv.FILLED) - cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 1) - - # zmiana rozmiaru maski i nałożenie na obiekt - classMask = cv.resize(classMask, (right - left + 1, bottom - top + 1)) - mask = (classMask > maskThreshold) - roi = frame[top:bottom+1, left:right+1][mask] - - colorIndex = random.randint(0, len(colors)-1) - color = colors[colorIndex] - - frame[top:bottom+1, left:right+1][mask] = ([0.3*color[0], 0.3*color[1], 0.3*color[2]] + 0.7 * roi).astype(np.uint8) - - # rysuje kontury na obrazie - mask = mask.astype(np.uint8) - im2, contours, hierarchy = cv.findContours(mask,cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE) - cv.drawContours(frame[top:bottom+1, left:right+1], contours, -1, color, 3, cv.LINE_8, hierarchy, 100) - -# dla każdej ramki maskuje obraz -def postprocess(boxes, masks): - # N - liczba znalezionych obramowań - # C - liczba klas - # H,W- wysokość i szerokość - numClasses = masks.shape[1] - numDetections = boxes.shape[2] - - frameH = frame.shape[0] - frameW = frame.shape[1] - - for i in range(numDetections): - box = boxes[0, 0, i] - mask = masks[i] - score = box[2] - if score > confThreshold: - classId = int(box[1]) - - # zaznacza ramkę - left = int(frameW * box[3]) - top = int(frameH * box[4]) - right = int(frameW * box[5]) - bottom = int(frameH * box[6]) - - left = max(0, min(left, frameW - 1)) - top = max(0, min(top, frameH - 1)) - right = max(0, min(right, frameW - 1)) - bottom = max(0, min(bottom, frameH - 1)) - - # aktywacja maski - classMask = mask[classId] - - # rysuje wszystko na obrazie - drawBox(frame, classId, score, left, top, right, bottom, classMask) - - -# załaduj nazwy -classesFile = "mscoco_labels.names"; -classes = None -with open(classesFile, 'rt') as f: - classes = f.read().rstrip('\n').split('\n') - -# Give the textGraph and weight files for the model -textGraph = "./mask.pbtxt"; -modelWeights = "./mask/frozen_inference_graph.pb"; - -# Load the network -net = cv.dnn.readNetFromTensorflow(modelWeights, textGraph); -net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV) -net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU) - -# Load the classes -colorsFile = "colors.txt"; -with open(colorsFile, 'rt') as f: - colorsStr = f.read().rstrip('\n').split('\n') -colors = [] #[0,0,0] -for i in range(len(colorsStr)): - rgb = colorsStr[i].split(' ') - color = np.array([float(rgb[0]), float(rgb[1]), float(rgb[2])]) - colors.append(color) - -winName = 'Mask-RCNN Object detection and Segmentation in OpenCV' -cv.namedWindow(winName, cv.WINDOW_NORMAL) - -outputFile = "mask_rcnn_out_py.avi" -if (args.image): - # Open the image file - if not os.path.isfile(args.image): - print("Input image file ", args.image, " doesn't exist") - sys.exit(1) - cap = cv.VideoCapture(args.image) - outputFile = args.image[:-4]+'_mask_rcnn_out_py.jpg' -elif (args.video): - # Open the video file - if not os.path.isfile(args.video): - print("Input video file ", args.video, " doesn't exist") - sys.exit(1) - cap = cv.VideoCapture(args.video) - outputFile = args.video[:-4]+'_mask_rcnn_out_py.avi' -else: - # Webcam input - cap = cv.VideoCapture(0) - -# Get the video writer initialized to save the output video -if (not args.image): - vid_writer = cv.VideoWriter(outputFile, cv.VideoWriter_fourcc('M','J','P','G'), 28, (round(cap.get(cv.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv.CAP_PROP_FRAME_HEIGHT)))) - -while cv.waitKey(1) < 0: - - # Get frame from the video - hasFrame, frame = cap.read() - - # Stop the program if reached end of video - if not hasFrame: - print("Done processing !!!") - print("Output file is stored as ", outputFile) - cv.waitKey(3000) - break - - # Create a 4D blob from a frame. - blob = cv.dnn.blobFromImage(frame, swapRB=True, crop=False) - - # Set the input to the network - net.setInput(blob) - - # Run the forward pass to get output from the output layers - boxes, masks = net.forward(['detection_out_final', 'detection_masks']) - - # Extract the bounding box and mask for each of the detected objects - postprocess(boxes, masks) - - # Put efficiency information. - t, _ = net.getPerfProfile() - label = 'Mask-RCNN on 2.5 GHz Intel Core i7 CPU, Inference time for a frame : %0.0f ms' % abs(t * 1000.0 / cv.getTickFrequency()) - cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0)) - - # Write the frame with the detection boxes - if (args.image): - cv.imwrite(outputFile, frame.astype(np.uint8)); - else: - vid_writer.write(frame.astype(np.uint8)) - - cv.imshow(winName, frame) - -