bat.apis.google 

 1import io
 2from google.cloud import vision
 3
 4import concurrent.futures
 5
 6class CloudVision:
 7    def __init__(self, concurrency=1):
 8        self.concurrency = concurrency
 9
10    def predict(self, image_path):
11        try:
12            with io.open(image_path, 'rb') as image_file:
13                content = image_file.read()
14
15            image = vision.Image(content=content)
16
17            client = vision.ImageAnnotatorClient()
18
19            response = client.label_detection(image=image)
20            labels = response.label_annotations
21
22        except Exception as e:
23            print(e)
24            return
25
26        return labels
27
28    def predictX(self, image_paths):
29        y_preds = []
30        y_index = []
31        y_executors = {}
32
33        with concurrent.futures.ThreadPoolExecutor(max_workers=self.concurrency) as executor:
34            for i, image_path in enumerate(image_paths):
35                # Load the input image and construct the payload for the request
36                y_executors[executor.submit(self.predict, image_path)] = i
37
38            for y_executor in concurrent.futures.as_completed(y_executors):
39                y_index.append(y_executors[y_executor])
40                y_preds.append(y_executor.result())
41
42            y_preds = [y for _, y in sorted(zip(y_index, y_preds))]
43
44        return y_preds
45
46    def print(self, y_preds):
47        max_mid_len = 0
48        max_desc_len = 0
49
50        for y in y_preds:
51            if len(y.description) > max_desc_len:
52                max_desc_len = len(y.description)
53            if len(y.mid) > max_mid_len:
54                max_mid_len = len(y.mid)
55
56        for y in y_preds:
57            print('{:<{w_id}s} {:<{w_desc}s} {:.5f}'.format(y.mid, y.description, y.score, w_id=max_mid_len, w_desc=max_desc_len))
class CloudVision: 
 7class CloudVision:
 8    def __init__(self, concurrency=1):
 9        self.concurrency = concurrency
10
11    def predict(self, image_path):
12        try:
13            with io.open(image_path, 'rb') as image_file:
14                content = image_file.read()
15
16            image = vision.Image(content=content)
17
18            client = vision.ImageAnnotatorClient()
19
20            response = client.label_detection(image=image)
21            labels = response.label_annotations
22
23        except Exception as e:
24            print(e)
25            return
26
27        return labels
28
29    def predictX(self, image_paths):
30        y_preds = []
31        y_index = []
32        y_executors = {}
33
34        with concurrent.futures.ThreadPoolExecutor(max_workers=self.concurrency) as executor:
35            for i, image_path in enumerate(image_paths):
36                # Load the input image and construct the payload for the request
37                y_executors[executor.submit(self.predict, image_path)] = i
38
39            for y_executor in concurrent.futures.as_completed(y_executors):
40                y_index.append(y_executors[y_executor])
41                y_preds.append(y_executor.result())
42
43            y_preds = [y for _, y in sorted(zip(y_index, y_preds))]
44
45        return y_preds
46
47    def print(self, y_preds):
48        max_mid_len = 0
49        max_desc_len = 0
50
51        for y in y_preds:
52            if len(y.description) > max_desc_len:
53                max_desc_len = len(y.description)
54            if len(y.mid) > max_mid_len:
55                max_mid_len = len(y.mid)
56
57        for y in y_preds:
58            print('{:<{w_id}s} {:<{w_desc}s} {:.5f}'.format(y.mid, y.description, y.score, w_id=max_mid_len, w_desc=max_desc_len))
CloudVision(concurrency=1) 
8    def __init__(self, concurrency=1):
9        self.concurrency = concurrency
concurrency
def predict(self, image_path): 
11    def predict(self, image_path):
12        try:
13            with io.open(image_path, 'rb') as image_file:
14                content = image_file.read()
15
16            image = vision.Image(content=content)
17
18            client = vision.ImageAnnotatorClient()
19
20            response = client.label_detection(image=image)
21            labels = response.label_annotations
22
23        except Exception as e:
24            print(e)
25            return
26
27        return labels
def predictX(self, image_paths): 
29    def predictX(self, image_paths):
30        y_preds = []
31        y_index = []
32        y_executors = {}
33
34        with concurrent.futures.ThreadPoolExecutor(max_workers=self.concurrency) as executor:
35            for i, image_path in enumerate(image_paths):
36                # Load the input image and construct the payload for the request
37                y_executors[executor.submit(self.predict, image_path)] = i
38
39            for y_executor in concurrent.futures.as_completed(y_executors):
40                y_index.append(y_executors[y_executor])
41                y_preds.append(y_executor.result())
42
43            y_preds = [y for _, y in sorted(zip(y_index, y_preds))]
44
45        return y_preds
def print(self, y_preds): 
47    def print(self, y_preds):
48        max_mid_len = 0
49        max_desc_len = 0
50
51        for y in y_preds:
52            if len(y.description) > max_desc_len:
53                max_desc_len = len(y.description)
54            if len(y.mid) > max_mid_len:
55                max_mid_len = len(y.mid)
56
57        for y in y_preds:
58            print('{:<{w_id}s} {:<{w_desc}s} {:.5f}'.format(y.mid, y.description, y.score, w_id=max_mid_len, w_desc=max_desc_len))