opencv小白07PracticalExercise学习笔记01

faceNet = cv.dnn.readNet(faceModel, faceProto)
frameFace, bboxes = getFaceBox(faceNet, frame)
    blob = cv.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)
    net.setInput(blob)
    detections = net.forward()
    
    gender = genderList[genderPreds[0].argmax()]

faceCascade = cv2.CascadeClassifier('./model/haarcascade_frontalface_default.xml')
outOpencvHaar, bboxes = detectFaceOpenCVHaar(faceCascade, frame)
    faces = faceCascade.detectMultiScale(frameGray)

img = np.flip(img,axis=1)#翻转

mask2 = cv2.inRange(hsv,lower_red,upper_red)# 0,1点阵

mask1 = mask1+mask2

# Refining the mask corresponding to the detected red color
mask1 = cv2.morphologyEx(mask1, cv2.MORPH_OPEN, np.ones((3,3),np.uint8),iterations=2)
mask1 = cv2.dilate(mask1,np.ones((3,3),np.uint8),iterations = 1)#去除噪音
mask2 = cv2.bitwise_not(mask1)#取反

# Generating the final output
res1 = cv2.bitwise_and(background,background,mask=mask1)#从背景中获取布的部分
res2 = cv2.bitwise_and(img,img,mask=mask2)#从前景中获取布之外部分
final_output = cv2.addWeighted(res1,1,res2,1,0)#合并，布以外=前景，布内部＝背景，效果就是布变成透视的(隐身效果的布)

modelConfiguration = "yolov3.cfg";
modelWeights = "yolov3.weights";

net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

# Create a 4D blob from a frame.
blob = cv.dnn.blobFromImage(frame, 1/255, (inpWidth, inpHeight), [0,0,0], 1, crop=False)

# Sets the input to the network
net.setInput(blob)

# Runs the forward pass to get output of the output layers
outs = net.forward(getOutputsNames(net))
    # Get the names of all the layers in the network
    layersNames = net.getLayerNames()
    # Get the names of the output layers, i.e. the layers with unconnected outputs
    return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Remove the bounding boxes with low confidence
postprocess(frame, outs)
    for out in outs:
        for detection in out:
            scores = detection[5:]
            classId = np.argmax(scores)
            confidence = scores[classId]
            if confidence > confThreshold:
                center_x ,center_y=xx
                width ,height , left,top = xx
                classIds.append(classId)
                confidences.append(float(confidence))
                boxes.append([left, top, width, height])

    # Perform non maximum suppression to eliminate redundant overlapping boxes with
    # lower confidences.
    indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
    for i in indices:
        drawPred(classIds[i], confidences[i], left, top, left + width, top + height)

# Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
t, _ = net.getPerfProfile()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))

gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray_image,127,255,0)
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
	# calculate moments for each contour
	M = cv2.moments(c)
	cX = int(M["m10"] / M["m00"])
	cY = int(M["m01"] / M["m00"])
	
	cv2.circle(img, (cX, cY), 5, (255, 255, 255), -1)