Detection and classification of objects placed in front of a video camera-CodePudding

Through this code, I aim to detect an object in real-time that will be put in front of the video camera and classify it. My reasoning is the following I tried to make two for loops the first one for the detection, and once the object is detected I want to apply the classification through the second for loop. I don't know if my reasoning is correct or not, I tested the code but I received this error

ValueError       Traceback (most recent call last)
<ipython-input-1-88a18bf89e71> in <module>()

     85         for obj_coordinates in objs:
---> 87             x1, x2, y1, y2 = apply_offsets(obj_coordinates, class_offsets)
     88             gray_obj = gray_obj[y1:y2, x1:x2]
     89             try:

/home/nada/Desktop/testforimage/src/utils/inference.pyc in apply_offsets(obj_coordinates, offsets)
     25 
     26 def apply_offsets(obj_coordinates, offsets):
---> 27     x, y, width, height = obj_coordinates
     28     x_off, y_off = offsets
     29     return (x - x_off, x   width   x_off, y - y_off, y   height   y_off)

ValueError: too many values to unpack

Could you please correct the following code and tell me if my reasoning is correct or not and thank you in advance.

video_capture = cv2.VideoCapture(0)
if video_capture.isOpened():
 frame = video_capture.read()
else:
 rval = False 
while True:
    rval, frame = video_capture.read()
    gray_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 
    gray_image = cv2.cvtColor(np.float32(imgUMat), cv2.COLOR_RGB2GRAY) 
    blur = cv2.GaussianBlur(gray_image, (5,5) , 0)

    ctrs = cv2.findContours(blur.copy(),cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    rects = [cv2.boundingRect(ctr) for ctr in ctrs]
for coordinates in rects:
    a1, a2, b1, b2 = app_offsets(coordinates, obj_offsets)
    gray_image = gray_image[b1:b2, a1:a2]
    try:
        gray_image = cv2.resize(gray_image, (obj_target_size))
    except:
        continue

    gray_image = preprocess_input(gray_image, True)
    gray_image = np.expand_dims(gray_image, 0)
    gray_image = np.expand_dims(gray_image, -1)
    objs = obj_detection.predict(gray_image)
    key = cv2.waitKey(1)
    b,g,r = cv2.split(frame) # get b,g,r
    rgb_img = cv2.merge([r,g,b]) # switch it to rgb
    
    for obj_coordinates in objs:
        x1, x2, y1, y2 = apply_offsets(obj_coordinates, class_offsets)
        gray_obj = gray_obj[y1:y2, x1:x2]
        try:
            gray_obj = cv2.resize(gray_obj, (class_target_size))
        except:
            continue

        gray_obj = preprocess_input(gray_obj, True)
        gray_obj = np.expand_dims(gray_obj, 0)
        gray_obj = np.expand_dims(gray_obj, -1)
        class_prediction = class_classifier.predict(gray_obj)
        class_probability = np.max(class_prediction)
        class_label_arg = np.argmax(class_prediction)
        class_text = emotion_labels[class_label_arg]
        class_window.append(class_text)

CodePudding user response：

In line 20 ctrs = cv2.findContours(blur.copy(),cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE), the function returns the contours and its hierarchy.

To draw the bounding box for each contour, you need to pass the first output (contours)

Change the line to the following:

ctrs = cv2.findContours(blur.copy(),cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[0]

CodePudding user response：

It is your obj_coordinates that does not seem to be a 4-uple. It is an element of objs produced by obj_detection.predict(gray_image). The code context you shared to us is insufficient to tell what is wrong in that function.