Prześlij pliki do 'mask'

mask/checkpoint

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+model_checkpoint_path: "model.ckpt"
+all_model_checkpoint_paths: "model.ckpt"

mask/mask_rcnn.py

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+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)
+
+