AI Goat.py

""" 
Make a program that takes an image and classifies it as either "a goat" or "not a goat"
"""

# load the image and convert it to grayscale
image = cv2.imread("images/goat.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# initialize the list of class labels and the image
CLASSES = ["background", "aeroplane", "bicycle", "bird", 
    "boat", "bottle", "bus", "car", 
    "cat", "chair", "cow", 
    "diningtable", "dog", "horse", 
    "motorbike", "person", "pottedplant", 
    "sheep", "sofa", "train", 
    "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

# define the model parameters
model = cv2.dnn.readNetFromCaffe("models/goat.prototxt", "models/goat.caffemodel")

# create the blob with the image
blob = cv2.dnn.blobFromImage(image, 0.007843, (W, H), 127.5)

# run the model
model.setInput(blob)
detections = model.forward()

# loop over the detections
for i in np.arange(0, detections.shape[2]):
    # extract the confidence (i.e., probability) associated with 
    # the prediction
    confidence = detections[0, 0, i, 2]

    # filter out weak detections by ensuring the `confidence` is 
    # greater than the minimum confidence
    if confidence > args["confidence"]:
        # extract the index of the class label from the `detections`, 
        # then compute the (x, y)-coordinates of the bounding box for 
        # the object
        idx = int(detections[0, 0, i, 1])
        box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
        (startX, startY, endX, endY) = box.astype("int")

        # display the prediction
        label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
        print("[INFO] {}".format(label))
        cv2.rectangle(image, (startX, startY), (endX, endY), 
            COLORS[idx], 2)
        y = startY - 15 if startY - 15 > 15 else startY + 15
        cv2.putText(image, label, (startX, y), 
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

# show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)