file = request.files['image']

img = cv2.imdecode(np.fromstring(file.read(), np.uint8), cv2.IMREAD_UNCHANGED)

#img = cv2.imread('image.jpg', cv2.IMREAD_UNCHANGED)

if img.shape[2] == 4:

img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)

classes = ['person', 'bicycle', 'car', 'motorbike', 'aeroplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'sofa', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tvmonitor', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']

#net = cv2.dnn.readNet('yolov3-tiny.weights', 'yolov3-tiny.cfg')

net = cv2.dnn.readNetFromDarknet("./yolov3-tiny.cfg", "./yolov3-tiny.weights")

net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)

net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

blob = cv2.dnn.blobFromImage(img, scalefactor=1/255, size=(416, 416), swapRB=True)

net.setInput(blob)

output_layers = net.getUnconnectedOutLayersNames()

layer_outputs = net.forward(output_layers)

boxes = []

confidences = []

class_ids = []

for output in layer_outputs:

for detection in output:

scores = detection[5:]

class_id = np.argmax(scores)

confidence = scores[class_id]

if confidence > 0.5:

center_x = int(detection[0] * img.shape[1])

center_y = int(detection[1] * img.shape[0])

width = int(detection[2] * img.shape[1])

height = int(detection[3] * img.shape[0])

left = int(center_x - width / 2)

top = int(center_y - height / 2)

boxes.append([left, top, width, height])

confidences.append(float(confidence))

class_ids.append(class_id)

indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

results = []

label = '' # 定義變數 label 並賦予一個空字符串

for i in indices:

i = i[0]

box = boxes[i]

left = box[0]

top = box[1]

width = box[2]

height = box[3]

label = classes[class_ids[i]]

results.append({'label': label,'left': left,'top': top,'right': left + width,'bottom': top + height})