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Main.py

+#basic imports
+import numpy as np
+import cv2
+import Model
+import WeightReader
+import Load_image
+import Process_predictions
+import pandas as pd
+
+
+Model = Model.make_yolov3_model()
+weight_reader = WeightReader("yolov3.weights")
+weight_reader.load_weights(Model)
+input_w, input_h = 416, 416    # here put the size of your input image                   
+photo_filename = 'birds9.jpg'  # here put the filename of your input image
+image, image_w, image_h = Load_image.load_image_pixels(photo_filename, (input_w, input_h))
+
+outputs = Model.predict(image)
+
+anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]
+class_threshold = 0.6 
+boxes = list()
+for i in range(len(outputs)):
+	boxes += Process_predictions.decode_netout(outputs[i][0], anchors[i], class_threshold, input_h, input_w)
+
+Process_predictions.correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)
+Process_predictions.do_nms(boxes, 0.5)
+
+labels = ["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"]
+v_boxes, v_labels, v_scores = Process_predictions.get_boxes(boxes, labels, class_threshold)
+
+#view our detections
+Process_predictions.draw_boxes(photo_filename, v_boxes, v_labels, v_scores) 
+
+csv_array = np.zeros((len(v_boxes),5))
+# this code creates an array in which we save the coordinates of the rectangles: the detected birds
+id_image = 0 # i still don't know how to fill up image ids
+from PIL import Image
+#for j in range( number of images )
+for i in range(len(v_boxes)):
+  csv_array[i][0]=i
+  csv_array[i][1]=v_boxes[i].xmin
+  csv_array[i][2]=v_boxes[i].ymax
+  csv_array[i][3]=v_boxes[i].xmax
+  csv_array[i][4]=v_boxes[i].ymin
+# we convert our array to a dataframe and then we save it in a csv file
+import pandas as pd
+pd.DataFrame(csv_array).to_csv('sample.csv')
+
+#now we will reload our original image and crop the detected birds and save them in different files
+img = cv2.imread('birds9.jpg') # insert original image here
+print(img.shape) # Print image shape
+for i in range(len(v_boxes)):
+  cropped_image = img[v_boxes[i].ymin:v_boxes[i].ymax, v_boxes[i].xmin:v_boxes[i].xmax]
+  cv2.imwrite("oiseau " + str(i+1) + ".jpg", cropped_image)
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