def detect(self, image, image_id): if self.model_image_size != (None, None): assert self.model_image_size[ 0] % 32 == 0, 'Multiples of 32 required' assert self.model_image_size[ 1] % 32 == 0, 'Multiples of 32 required' boxed_image = letterbox_image( image, tuple(reversed(self.model_image_size))) else: new_image_size = (image.width - (image.width % 32), image.height - (image.height % 32)) boxed_image = letterbox_image(image, new_image_size) image_data = np.array(boxed_image, dtype='float32') print(image_data.shape) image_data /= 255. image_data = np.expand_dims(image_data, 0) # Add batch dimension. start = timer() out_boxes, out_scores, out_classes = self.sess.run( [self.boxes, self.scores, self.classes], feed_dict={ self.yolo_model.input: image_data, self.input_image_shape: [image.size[1], image.size[0]], K.learning_phase(): 0 }) end = timer() print("Run time : ", (end - start)) result = [] result.append(image_id) for i, c in reversed(list(enumerate(out_classes))): predicted_class = self.class_names[c] box = out_boxes[i] score = out_scores[i] label = '{} {:.2f}'.format(predicted_class, score) # draw = ImageDraw.Draw(image) # label_size = draw.textsize(label, font) top, left, bottom, right = box top = max(0, np.floor(top + 0.5).astype('int32')) left = max(0, np.floor(left + 0.5).astype('int32')) bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32')) right = min(image.size[0], np.floor(right + 0.5).astype('int32')) result.append([[left, top, right, bottom], score, predicted_class]) return result
def detect_image(self, image, print_box_in_image=True): start = timer() boxed_image = letterbox_image(image, tuple(reversed(self.input_image_size))) image_data = np.array(boxed_image, dtype='float32') image_data /= 255. image_data = np.expand_dims(image_data, 0) # Add batch dimension. image_size = [image.size[1], image.size[0]] inputs_data, inputs_image_size = self.make_input_placeholders() boxes_, scores_, classes_ = self.yolo_model(inputs_data, inputs_image_size) self.sess.run(tf.global_variables_initializer()) out_boxes, out_scores, out_classes = \ self.sess.run([boxes_, scores_, classes_], feed_dict={inputs_data: image_data, inputs_image_size: image_size}) if print_box_in_image: self.draw_objects_in_image(image, out_boxes, out_scores, out_classes) end = timer() print("cost time: ", round((end - start), 2), 's') return image else: return out_boxes, out_scores, out_classes
def detect_image(self, image): start = timer() if self.model_image_size != (None, None): assert self.model_image_size[ 0] % 32 == 0, 'Multiples of 32 required' assert self.model_image_size[ 1] % 32 == 0, 'Multiples of 32 required' boxed_image = letterbox_image( image, tuple(reversed(self.model_image_size))) else: new_image_size = (image.width - (image.width % 32), image.height - (image.height % 32)) boxed_image = letterbox_image(image, new_image_size) image_data = np.array(boxed_image, dtype='float32') print(image_data.shape) image_data /= 255. image_data = np.expand_dims(image_data, 0) # Add batch dimension. out_boxes, out_scores, out_classes = self.sess.run( [self.boxes, self.scores, self.classes], feed_dict={ self.yolo_model.input: image_data, self.input_image_shape: [image.size[1], image.size[0]], K.learning_phase(): 0 }) print('Found {} boxes for {}'.format(len(out_boxes), 'img')) font = ImageFont.truetype(font='classes_txt/FiraMono-Medium.otf', size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32')) thickness = (image.size[0] + image.size[1]) // 300 for i, c in reversed(list(enumerate(out_classes))): predicted_class = self.class_names[c] box = out_boxes[i] score = out_scores[i] label = '{} {:.2f}'.format(predicted_class, score) draw = ImageDraw.Draw(image) label_size = draw.textsize(label, font) top, left, bottom, right = box top = max(0, np.floor(top + 0.5).astype('int32')) left = max(0, np.floor(left + 0.5).astype('int32')) bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32')) right = min(image.size[0], np.floor(right + 0.5).astype('int32')) print(label, (left, top), (right, bottom)) if top - label_size[1] >= 0: text_origin = np.array([left, top - label_size[1]]) else: text_origin = np.array([left, top + 1]) # My kingdom for a good redistributable image drawing library. for i in range(thickness): draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c]) draw.rectangle( [tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c]) draw.text(text_origin, label, fill=(0, 0, 0), font=font) del draw end = timer() print(end - start) return image