workaround for interrupted weights

deepface/commons/functions.py

@@ -7,6 +7,19 @@ from keras.preprocessing import image
 import cv2
 from pathlib import Path
 import gdown
+import hashlib
+
+def findFileHash(file):
+	BLOCK_SIZE = 65536 # The size of each read from the file
+
+	file_hash = hashlib.sha256() # Create the hash object, can use something other than `.sha256()` if you wish
+	with open(file, 'rb') as f: # Open the file to read it's bytes
+		fb = f.read(BLOCK_SIZE) # Read from the file. Take in the amount declared above
+		while len(fb) > 0: # While there is still data being read from the file
+			file_hash.update(fb) # Update the hash
+			fb = f.read(BLOCK_SIZE) # Read the next block from the file
+	
+	return file_hash.hexdigest()
 
 def initializeFolder():
 	
@@ -20,6 +33,34 @@ def initializeFolder():
 		os.mkdir(home+"/.deepface/weights")
 		print("Directory ",home,"/.deepface/weights created")
 	
+	#----------------------------------
+	"""
+	#avoid interrupted file download
+	
+	weight_hashes = [
+		['age_model_weights.h5', '0aeff75734bfe794113756d2bfd0ac823d51e9422c8961125b570871d3c2b114']
+		, ['facenet_weights.h5', '90659cc97bfda5999120f95d8e122f4d262cca11715a21e59ba024bcce816d5c']
+		, ['facial_expression_model_weights.h5', 'e8e8851d3fa05c001b1c27fd8841dfe08d7f82bb786a53ad8776725b7a1e824c']
+		, ['gender_model_weights.h5', '45513ce5678549112d25ab85b1926fb65986507d49c674a3d04b2ba70dba2eb5']
+		, ['openface_weights.h5', '5b41897ec6dd762cee20575eee54ed4d719a78cb982b2080a87dc14887d88a7a']
+		, ['race_model_single_batch.h5', 'eb22b28b1f6dfce65b64040af4e86003a5edccb169a1a338470dde270b6f5e54']
+		, ['vgg_face_weights.h5', '759266b9614d0fd5d65b97bf716818b746cc77ab5944c7bffc937c6ba9455d8c']
+	]
+	
+	for i in weight_hashes:
+		
+		weight_file = home+"/.deepface/weights/"+i[0]
+		expected_hash = i[1]
+		
+		#check file exits
+		if os.path.isfile(weight_file) == True:
+			current_hash = findFileHash(weight_file)
+			if current_hash != expected_hash:
+				print("hash violated for ", i[0],". It's going to be removed.")
+				os.remove(weight_file)
+	"""
+	#----------------------------------
+
 def findThreshold(model_name, distance_metric):
 	
 	threshold = 0.40