import numpy as np import cv2 import os import json from tqdm import tqdm from glob import glob import matplotlib.pyplot as plt import tensorflow as tf from tensorflow.keras import layers, models, optimizers from custom_layers import yolov4_neck, yolov4_head, nms from utils import load_weights, get_detection_data, draw_bbox, voc_ap, draw_plot_func, read_txt_to_list from config import yolo_config from loss import yolo_loss class Yolov4(object): def __init__(self, weight_path=None, class_name_path='coco_classes.txt', config=yolo_config, ): assert config['img_size'][0] == config['img_size'][1], 'not support yet' assert config['img_size'][0] % config['strides'][-1] == 0, 'must be a multiple of last stride' self.class_names = [line.strip() for line in open(class_name_path).readlines()] self.img_size = yolo_config['img_size'] self.num_classes = len(self.class_names) self.weight_path = weight_path self.anchors = np.array(yolo_config['anchors']).reshape((3, 3, 2)) self.xyscale = yolo_config['xyscale'] self.strides = yolo_config['strides'] self.output_sizes = [self.img_size[0] // s for s in self.strides] self.class_color = {name: list(np.random.random(size=3)*255) for name in self.class_names} # Training self.max_boxes = yolo_config['max_boxes'] self.iou_loss_thresh = yolo_config['iou_loss_thresh'] self.config = yolo_config assert self.num_classes > 0, 'no classes detected!' tf.keras.backend.clear_session() if yolo_config['num_gpu'] > 1: mirrored_strategy = tf.distribute.MirroredStrategy() with mirrored_strategy.scope(): self.build_model(load_pretrained=True if self.weight_path else False) else: self.build_model(load_pretrained=True if self.weight_path else False) def build_model(self, load_pretrained=True): # core yolo model input_layer = layers.Input(self.img_size) yolov4_output = yolov4_neck(input_layer, self.num_classes) self.yolo_model = models.Model(input_layer, yolov4_output) # Build training model y_true = [ layers.Input(name='input_2', shape=(52, 52, 3, (self.num_classes + 5))), # label small boxes layers.Input(name='input_3', shape=(26, 26, 3, (self.num_classes + 5))), # label medium boxes layers.Input(name='input_4', shape=(13, 13, 3, (self.num_classes + 5))), # label large boxes layers.Input(name='input_5', shape=(self.max_boxes, 4)), # true bboxes ] loss_list = tf.keras.layers.Lambda(yolo_loss, name='yolo_loss', arguments={'num_classes': self.num_classes, 'iou_loss_thresh': self.iou_loss_thresh, 'anchors': self.anchors})([*self.yolo_model.output, *y_true]) self.training_model = models.Model([self.yolo_model.input, *y_true], loss_list) # Build inference model yolov4_output = yolov4_head(yolov4_output, self.num_classes, self.anchors, self.xyscale) # output: [boxes, scores, classes, valid_detections] self.inference_model = models.Model(input_layer, nms(yolov4_output, self.img_size, self.num_classes, iou_threshold=self.config['iou_threshold'], score_threshold=self.config['score_threshold'])) if load_pretrained and self.weight_path and self.weight_path.endswith('.weights'): if self.weight_path.endswith('.weights'): load_weights(self.yolo_model, self.weight_path) print(f'load from {self.weight_path}') elif self.weight_path.endswith('.h5'): self.training_model.load_weights(self.weight_path) print(f'load from {self.weight_path}') self.training_model.compile(optimizer=optimizers.Adam(lr=1e-3), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) def load_model(self, path): self.yolo_model = models.load_model(path, compile=False) yolov4_output = yolov4_head(self.yolo_model.output, self.num_classes, self.anchors, self.xyscale) self.inference_model = models.Model(self.yolo_model.input, nms(yolov4_output, self.img_size, self.num_classes)) # [boxes, scores, classes, valid_detections] def save_model(self, path): self.yolo_model.save(path) def preprocess_img(self, img): img = cv2.resize(img, self.img_size[:2]) img = img / 255. return img def fit(self, train_data_gen, epochs, val_data_gen=None, initial_epoch=0, callbacks=None): self.training_model.fit(train_data_gen, steps_per_epoch=len(train_data_gen), validation_data=val_data_gen, validation_steps=len(val_data_gen), epochs=epochs, callbacks=callbacks, initial_epoch=initial_epoch) # raw_img: RGB def predict_img(self, raw_img, random_color=True, plot_img=True, figsize=(10, 10), show_text=True, return_output=True): print('img shape: ', raw_img.shape) img = self.preprocess_img(raw_img) imgs = np.expand_dims(img, axis=0) pred_output = self.inference_model.predict(imgs) detections = get_detection_data(img=raw_img, model_outputs=pred_output, class_names=self.class_names) output_img = draw_bbox(raw_img, detections, cmap=self.class_color, random_color=random_color, figsize=figsize, show_text=show_text, show_img=False) if return_output: return output_img, detections else: return detections def predict(self, img_path, random_color=True, plot_img=True, figsize=(10, 10), show_text=True): raw_img = img_path return self.predict_img(raw_img, random_color, plot_img, figsize, show_text) def export_gt(self, annotation_path, gt_folder_path): with open(annotation_path) as file: for line in file: line = line.split(' ') filename = line[0].split(os.sep)[-1].split('.')[0] objs = line[1:] # export txt file with open(os.path.join(gt_folder_path, filename + '.txt'), 'w') as output_file: for obj in objs: x_min, y_min, x_max, y_max, class_id = [float(o) for o in obj.strip().split(',')] output_file.write(f'{self.class_names[int(class_id)]} {x_min} {y_min} {x_max} {y_max}\n') def export_prediction(self, annotation_path, pred_folder_path, img_folder_path, bs=2): with open(annotation_path) as file: img_paths = [os.path.join(img_folder_path, line.split(' ')[0].split(os.sep)[-1]) for line in file] # print(img_paths[:20]) for batch_idx in tqdm(range(0, len(img_paths), bs)): # print(len(img_paths), batch_idx, batch_idx*bs, (batch_idx+1)*bs) paths = img_paths[batch_idx:batch_idx+bs] # print(paths) # read and process img imgs = np.zeros((len(paths), *self.img_size)) raw_img_shapes = [] for j, path in enumerate(paths): img = cv2.imread(path) raw_img_shapes.append(img.shape) img = self.preprocess_img(img) imgs[j] = img # process batch output b_boxes, b_scores, b_classes, b_valid_detections = self.inference_model.predict(imgs) for k in range(len(paths)): num_boxes = b_valid_detections[k] raw_img_shape = raw_img_shapes[k] boxes = b_boxes[k, :num_boxes] classes = b_classes[k, :num_boxes] scores = b_scores[k, :num_boxes] # print(raw_img_shape) boxes[:, [0, 2]] = (boxes[:, [0, 2]] * raw_img_shape[1]) # w boxes[:, [1, 3]] = (boxes[:, [1, 3]] * raw_img_shape[0]) # h cls_names = [self.class_names[int(c)] for c in classes] # print(raw_img_shape, boxes.astype(int), cls_names, scores) img_path = paths[k] filename = img_path.split(os.sep)[-1].split('.')[0] # print(filename) output_path = os.path.join(pred_folder_path, filename+'.txt') with open(output_path, 'w') as pred_file: for box_idx in range(num_boxes): b = boxes[box_idx] pred_file.write(f'{cls_names[box_idx]} {scores[box_idx]} {b[0]} {b[1]} {b[2]} {b[3]}\n') def eval_map(self, gt_folder_path, pred_folder_path, temp_json_folder_path, output_files_path): """Process Gt""" ground_truth_files_list = glob(gt_folder_path + '/*.txt') assert len(ground_truth_files_list) > 0, 'no ground truth file' ground_truth_files_list.sort() # dictionary with counter per class gt_counter_per_class = {} counter_images_per_class = {} gt_files = [] for txt_file in ground_truth_files_list: file_id = txt_file.split(".txt", 1)[0] file_id = os.path.basename(os.path.normpath(file_id)) # check if there is a correspondent detection-results file temp_path = os.path.join(pred_folder_path, (file_id + ".txt")) assert os.path.exists(temp_path), "Error. File not found: {}\n".format(temp_path) lines_list = read_txt_to_list(txt_file) # create ground-truth dictionary bounding_boxes = [] is_difficult = False already_seen_classes = [] for line in lines_list: class_name, left, top, right, bottom = line.split() # check if class is in the ignore list, if yes skip bbox = left + " " + top + " " + right + " " + bottom bounding_boxes.append({"class_name": class_name, "bbox": bbox, "used": False}) # count that object if class_name in gt_counter_per_class: gt_counter_per_class[class_name] += 1 else: # if class didn't exist yet gt_counter_per_class[class_name] = 1 if class_name not in already_seen_classes: if class_name in counter_images_per_class: counter_images_per_class[class_name] += 1 else: # if class didn't exist yet counter_images_per_class[class_name] = 1 already_seen_classes.append(class_name) # dump bounding_boxes into a ".json" file new_temp_file = os.path.join(temp_json_folder_path, file_id+"_ground_truth.json") #TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json" gt_files.append(new_temp_file) with open(new_temp_file, 'w') as outfile: json.dump(bounding_boxes, outfile) gt_classes = list(gt_counter_per_class.keys()) # let's sort the classes alphabetically gt_classes = sorted(gt_classes) n_classes = len(gt_classes) print(gt_classes, gt_counter_per_class) """Process prediction""" dr_files_list = sorted(glob(os.path.join(pred_folder_path, '*.txt'))) for class_index, class_name in enumerate(gt_classes): bounding_boxes = [] for txt_file in dr_files_list: # the first time it checks if all the corresponding ground-truth files exist file_id = txt_file.split(".txt", 1)[0] file_id = os.path.basename(os.path.normpath(file_id)) temp_path = os.path.join(gt_folder_path, (file_id + ".txt")) if class_index == 0: if not os.path.exists(temp_path): error_msg = f"Error. File not found: {temp_path}\n" print(error_msg) lines = read_txt_to_list(txt_file) for line in lines: try: tmp_class_name, confidence, left, top, right, bottom = line.split() except ValueError: error_msg = f"""Error: File {txt_file} in the wrong format.\n Expected: \n Received: {line} \n""" print(error_msg) if tmp_class_name == class_name: # print("match") bbox = left + " " + top + " " + right + " " + bottom bounding_boxes.append({"confidence": confidence, "file_id": file_id, "bbox": bbox}) # sort detection-results by decreasing confidence bounding_boxes.sort(key=lambda x: float(x['confidence']), reverse=True) with open(temp_json_folder_path + "/" + class_name + "_dr.json", 'w') as outfile: json.dump(bounding_boxes, outfile) """ Calculate the AP for each class """ sum_AP = 0.0 ap_dictionary = {} # open file to store the output with open(output_files_path + "/output.txt", 'w') as output_file: output_file.write("# AP and precision/recall per class\n") count_true_positives = {} for class_index, class_name in enumerate(gt_classes): count_true_positives[class_name] = 0 """ Load detection-results of that class """ dr_file = temp_json_folder_path + "/" + class_name + "_dr.json" dr_data = json.load(open(dr_file)) """ Assign detection-results to ground-truth objects """ nd = len(dr_data) tp = [0] * nd # creates an array of zeros of size nd fp = [0] * nd for idx, detection in enumerate(dr_data): file_id = detection["file_id"] gt_file = temp_json_folder_path + "/" + file_id + "_ground_truth.json" ground_truth_data = json.load(open(gt_file)) ovmax = -1 gt_match = -1 # load detected object bounding-box bb = [float(x) for x in detection["bbox"].split()] for obj in ground_truth_data: # look for a class_name match if obj["class_name"] == class_name: bbgt = [float(x) for x in obj["bbox"].split()] bi = [max(bb[0], bbgt[0]), max(bb[1], bbgt[1]), min(bb[2], bbgt[2]), min(bb[3], bbgt[3])] iw = bi[2] - bi[0] + 1 ih = bi[3] - bi[1] + 1 if iw > 0 and ih > 0: # compute overlap (IoU) = area of intersection / area of union ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + \ (bbgt[2] - bbgt[0]+ 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih ov = iw * ih / ua if ov > ovmax: ovmax = ov gt_match = obj min_overlap = 0.5 if ovmax >= min_overlap: # if "difficult" not in gt_match: if not bool(gt_match["used"]): # true positive tp[idx] = 1 gt_match["used"] = True count_true_positives[class_name] += 1 # update the ".json" file with open(gt_file, 'w') as f: f.write(json.dumps(ground_truth_data)) else: # false positive (multiple detection) fp[idx] = 1 else: fp[idx] = 1 # compute precision/recall cumsum = 0 for idx, val in enumerate(fp): fp[idx] += cumsum cumsum += val print('fp ', cumsum) cumsum = 0 for idx, val in enumerate(tp): tp[idx] += cumsum cumsum += val print('tp ', cumsum) rec = tp[:] for idx, val in enumerate(tp): rec[idx] = float(tp[idx]) / gt_counter_per_class[class_name] print('recall ', cumsum) prec = tp[:] for idx, val in enumerate(tp): prec[idx] = float(tp[idx]) / (fp[idx] + tp[idx]) print('prec ', cumsum) ap, mrec, mprec = voc_ap(rec[:], prec[:]) sum_AP += ap text = "{0:.2f}%".format( ap * 100) + " = " + class_name + " AP " # class_name + " AP = {0:.2f}%".format(ap*100) print(text) ap_dictionary[class_name] = ap n_images = counter_images_per_class[class_name] # lamr, mr, fppi = log_average_miss_rate(np.array(prec), np.array(rec), n_images) # lamr_dictionary[class_name] = lamr """ Draw plot """ if True: plt.plot(rec, prec, '-o') # add a new penultimate point to the list (mrec[-2], 0.0) # since the last line segment (and respective area) do not affect the AP value area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]] area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]] plt.fill_between(area_under_curve_x, 0, area_under_curve_y, alpha=0.2, edgecolor='r') # set window title fig = plt.gcf() # gcf - get current figure fig.canvas.set_window_title('AP ' + class_name) # set plot title plt.title('class: ' + text) # plt.suptitle('This is a somewhat long figure title', fontsize=16) # set axis titles plt.xlabel('Recall') plt.ylabel('Precision') # optional - set axes axes = plt.gca() # gca - get current axes axes.set_xlim([0.0, 1.0]) axes.set_ylim([0.0, 1.05]) # .05 to give some extra space # Alternative option -> wait for button to be pressed # while not plt.waitforbuttonpress(): pass # wait for key display # Alternative option -> normal display plt.show() # save the plot # fig.savefig(output_files_path + "/classes/" + class_name + ".png") # plt.cla() # clear axes for next plot # if show_animation: # cv2.destroyAllWindows() output_file.write("\n# mAP of all classes\n") mAP = sum_AP / n_classes text = "mAP = {0:.2f}%".format(mAP * 100) output_file.write(text + "\n") print(text) """ Count total of detection-results """ # iterate through all the files det_counter_per_class = {} for txt_file in dr_files_list: # get lines to list lines_list = read_txt_to_list(txt_file) for line in lines_list: class_name = line.split()[0] # check if class is in the ignore list, if yes skip # if class_name in args.ignore: # continue # count that object if class_name in det_counter_per_class: det_counter_per_class[class_name] += 1 else: # if class didn't exist yet det_counter_per_class[class_name] = 1 # print(det_counter_per_class) dr_classes = list(det_counter_per_class.keys()) """ Plot the total number of occurences of each class in the ground-truth """ if True: window_title = "ground-truth-info" plot_title = "ground-truth\n" plot_title += "(" + str(len(ground_truth_files_list)) + " files and " + str(n_classes) + " classes)" x_label = "Number of objects per class" output_path = output_files_path + "/ground-truth-info.png" to_show = False plot_color = 'forestgreen' draw_plot_func( gt_counter_per_class, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, '', ) """ Finish counting true positives """ for class_name in dr_classes: # if class exists in detection-result but not in ground-truth then there are no true positives in that class if class_name not in gt_classes: count_true_positives[class_name] = 0 # print(count_true_positives) """ Plot the total number of occurences of each class in the "detection-results" folder """ if True: window_title = "detection-results-info" # Plot title plot_title = "detection-results\n" plot_title += "(" + str(len(dr_files_list)) + " files and " count_non_zero_values_in_dictionary = sum(int(x) > 0 for x in list(det_counter_per_class.values())) plot_title += str(count_non_zero_values_in_dictionary) + " detected classes)" # end Plot title x_label = "Number of objects per class" output_path = output_files_path + "/detection-results-info.png" to_show = False plot_color = 'forestgreen' true_p_bar = count_true_positives draw_plot_func( det_counter_per_class, len(det_counter_per_class), window_title, plot_title, x_label, output_path, to_show, plot_color, true_p_bar ) """ Draw mAP plot (Show AP's of all classes in decreasing order) """ if True: window_title = "mAP" plot_title = "mAP = {0:.2f}%".format(mAP * 100) x_label = "Average Precision" output_path = output_files_path + "/mAP.png" to_show = True plot_color = 'royalblue' draw_plot_func( ap_dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, "" ) def predict_raw(self, img_path): raw_img = cv2.imread(img_path) print('img shape: ', raw_img.shape) img = self.preprocess_img(raw_img) imgs = np.expand_dims(img, axis=0) return self.yolo_model.predict(imgs) def predict_nonms(self, img_path, iou_threshold=0.413, score_threshold=0.1): raw_img = cv2.imread(img_path) print('img shape: ', raw_img.shape) img = self.preprocess_img(raw_img) imgs = np.expand_dims(img, axis=0) yolov4_output = self.yolo_model.predict(imgs) output = yolov4_head(yolov4_output, self.num_classes, self.anchors, self.xyscale) pred_output = nms(output, self.img_size, self.num_classes, iou_threshold, score_threshold) pred_output = [p.numpy() for p in pred_output] detections = get_detection_data(img=raw_img, model_outputs=pred_output, class_names=self.class_names) draw_bbox(raw_img, detections, cmap=self.class_color, random_color=True) return detections