to detect single photo from cmd

photoshow.py

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+# importing the dependencies
+import cv2
+import matplotlib.pyplot as plt
+from darkflow.net.build import TFNet
+import argparse
+
+# the cfg file and weights location change this thing according to your model location
+model = {"model": "cfg/yolov2.cfg",
+           "load": "yolov2.weights",
+           "threshold": 0.4}
+
+# creating the object
+tfnet = TFNet(model)
+
+# to get the image path
+parser = argparse.ArgumentParser()
+parser.add_argument('--img',type=str,help='path of the image')
+arg = parser.parse_args()
+
+imgcv = cv2.imread(arg.img) # read the image
+result = tfnet.return_predict(imgcv) # predict the classes and cordinates of the oject
+
+# This is for draw the bounding box around the predicted classes 
+tl = []
+br = []
+labels = []
+for i in range(len(result)):
+    topleft = (result[i]['topleft']['x'],result[i]['topleft']['y']) # to get the labels from the predicted class ,it's in the form of dictionary
+    bottomright = (result[i]['bottomright']['x'],result[i]['bottomright']['y'])
+    label = (result[i]['label'])
+    st = result[i]['topleft']['x'] 
+    nd = result[i]['bottomright']['x']
+    mid_x = (nd-st)//2 + st # mid point of the top box line
+    mid_y = result[i]['topleft']['y']
+    tl.append(topleft)
+    br.append(bottomright)
+    labels.append(label)
+    img2 = cv2.rectangle(imgcv,tl[i],br[i],(0,255,255),5) # draw rectangles around the classes here we pass image,topleft cordinates ,bottomright cordinates ,which colour box we want and how thik the line
+    img2 = cv2.putText(imgcv,labels[i],tl[i],cv2.FONT_HERSHEY_COMPLEX,1, (0 ,0 ,0), 2) # putting the label on the topleft corner
+    img2 = cv2.putText(imgcv,confidence[i],cf_cor[i],cv2.FONT_ITALIC,1, (0 ,0 ,255), 2) # putting the confidence score
+
+
+
+img2 = cv2.cvtColor(img2,cv2.COLOR_BGR2RGB)  # convert the image in RGB format
+cv2.imshow('prediction',img2)
+cv2.waitKey(0) # waitkey for hold the image in display until user press any key