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| import cv2 | |
| import numpy as np | |
| import time | |
| import sys | |
| import os | |
| CONFIDENCE = 0.5 | |
| SCORE_THRESHOLD = 0.5 | |
| IOU_THRESHOLD = 0.5 | |
| # the neural network configuration | |
| config_path = "cfg/yolov3.cfg" | |
| # the YOLO net weights file | |
| weights_path = "weights/yolov3.weights" | |
| # loading all the class labels (objects) | |
| labels = open("data/coco.names").read().strip().split("\n") | |
| # generating colors for each object for later plotting | |
| colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") | |
| # load the YOLO network | |
| net = cv2.dnn.readNetFromDarknet(config_path, weights_path) | |
| # path_name = "images/city_scene.jpg" | |
| path_name = sys.argv[1] | |
| image = cv2.imread(path_name) | |
| file_name = os.path.basename(path_name) | |
| filename, ext = file_name.split(".") | |
| h, w = image.shape[:2] | |
| # create 4D blob | |
| blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False) | |
| # sets the blob as the input of the network | |
| net.setInput(blob) | |
| # get all the layer names | |
| ln = net.getLayerNames() | |
| try: | |
| ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] | |
| except IndexError: | |
| # in case getUnconnectedOutLayers() returns 1D array when CUDA isn't available | |
| ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()] | |
| # feed forward (inference) and get the network output | |
| # measure how much it took in seconds | |
| start = time.perf_counter() | |
| layer_outputs = net.forward(ln) | |
| time_took = time.perf_counter() - start | |
| print(f"Time took: {time_took:.2f}s") | |
| boxes, confidences, class_ids = [], [], [] | |
| # loop over each of the layer outputs | |
| for output in layer_outputs: | |
| # loop over each of the object detections | |
| for detection in output: | |
| # extract the class id (label) and confidence (as a probability) of | |
| # the current object detection | |
| scores = detection[5:] | |
| class_id = np.argmax(scores) | |
| confidence = scores[class_id] | |
| # discard weak predictions by ensuring the detected | |
| # probability is greater than the minimum probability | |
| if confidence > CONFIDENCE: | |
| # scale the bounding box coordinates back relative to the | |
| # size of the image, keeping in mind that YOLO actually | |
| # returns the center (x, y)-coordinates of the bounding | |
| # box followed by the boxes' width and height | |
| box = detection[:4] * np.array([w, h, w, h]) | |
| (centerX, centerY, width, height) = box.astype("int") | |
| # use the center (x, y)-coordinates to derive the top and | |
| # and left corner of the bounding box | |
| x = int(centerX - (width / 2)) | |
| y = int(centerY - (height / 2)) | |
| # update our list of bounding box coordinates, confidences, | |
| # and class IDs | |
| boxes.append([x, y, int(width), int(height)]) | |
| confidences.append(float(confidence)) | |
| class_ids.append(class_id) | |
| # perform the non maximum suppression given the scores defined before | |
| idxs = cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD) | |
| font_scale = 1 | |
| thickness = 1 | |
| # ensure at least one detection exists | |
| if len(idxs) > 0: | |
| # loop over the indexes we are keeping | |
| for i in idxs.flatten(): | |
| # extract the bounding box coordinates | |
| x, y = boxes[i][0], boxes[i][1] | |
| w, h = boxes[i][2], boxes[i][3] | |
| # draw a bounding box rectangle and label on the image | |
| color = [int(c) for c in colors[class_ids[i]]] | |
| cv2.rectangle(image, (x, y), (x + w, y + h), color=color, thickness=thickness) | |
| text = f"{labels[class_ids[i]]}: {confidences[i]:.2f}" | |
| # calculate text width & height to draw the transparent boxes as background of the text | |
| (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] | |
| text_offset_x = x | |
| text_offset_y = y - 5 | |
| box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) | |
| overlay = image.copy() | |
| cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) | |
| # add opacity (transparency to the box) | |
| image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) | |
| # now put the text (label: confidence %) | |
| cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, | |
| fontScale=font_scale, color=(0, 0, 0), thickness=thickness) | |
| # cv2.imshow("image", image) | |
| # if cv2.waitKey(0) == ord("q"): | |
| # pass | |
| cv2.imwrite(filename + "_yolo3." + ext, image) |